malicious_session_identification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6905210b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import base64\n",
    "import numpy as np\n",
    "import io\n",
    "from joblib import Parallel, delayed\n",
    "from PIL import Image\n",
    "from scapy.all import *\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "load_layer(\"tls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6606be19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d30684",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5813f72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('malware_detection_data/train.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "b932ebad",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('malware_detection_data/test.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "0bef9de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57823, 27)"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e8f93199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'insert_id', 'timestamp', 'src_ip', 'src_port', 'dst_ip',\n",
       "       'dst_port', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10',\n",
       "       'd1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06911b22",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a55731f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>insert_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>malware_family</th>\n",
       "      <th>malware_group</th>\n",
       "      <th>malware_info</th>\n",
       "      <th>src_ip</th>\n",
       "      <th>src_port</th>\n",
       "      <th>dst_ip</th>\n",
       "      <th>dst_port</th>\n",
       "      <th>...</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "      <th>d6</th>\n",
       "      <th>d7</th>\n",
       "      <th>d8</th>\n",
       "      <th>d9</th>\n",
       "      <th>d10</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>qqjjl78hq0mr2n7h</td>\n",
       "      <td>2022-05-20 10:48:22.431120+00:00</td>\n",
       "      <td>Laroux</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.168.180.160</td>\n",
       "      <td>49684.0</td>\n",
       "      <td>52.109.20.75</td>\n",
       "      <td>443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>u8ikd767a8vq7e6z</td>\n",
       "      <td>2022-08-10 18:50:19.040566+00:00</td>\n",
       "      <td>Laroux</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.168.180.122</td>\n",
       "      <td>49694.0</td>\n",
       "      <td>20.42.73.26</td>\n",
       "      <td>443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>m5fx7zv0g2jbke9x</td>\n",
       "      <td>2022-08-20 11:44:43.778929+00:00</td>\n",
       "      <td>CoinMiner</td>\n",
       "      <td>LinuxMalware</td>\n",
       "      <td>{\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...</td>\n",
       "      <td>192.168.180.160</td>\n",
       "      <td>49723.0</td>\n",
       "      <td>20.189.173.12</td>\n",
       "      <td>443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>y22wwz7hdy93mel9</td>\n",
       "      <td>2022-08-19 23:52:03.445314+00:00</td>\n",
       "      <td>CoinMiner</td>\n",
       "      <td>LinuxMalware</td>\n",
       "      <td>{\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...</td>\n",
       "      <td>192.168.180.139</td>\n",
       "      <td>51961.0</td>\n",
       "      <td>23.32.129.39</td>\n",
       "      <td>443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>stjf559mu275c9j3</td>\n",
       "      <td>2022-05-14 07:59:11.570870+00:00</td>\n",
       "      <td>InstallCore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.168.180.169</td>\n",
       "      <td>49174.0</td>\n",
       "      <td>172.67.188.70</td>\n",
       "      <td>443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         insert_id                         timestamp  \\\n",
       "0           0  qqjjl78hq0mr2n7h  2022-05-20 10:48:22.431120+00:00   \n",
       "1           1  u8ikd767a8vq7e6z  2022-08-10 18:50:19.040566+00:00   \n",
       "2           2  m5fx7zv0g2jbke9x  2022-08-20 11:44:43.778929+00:00   \n",
       "3           3  y22wwz7hdy93mel9  2022-08-19 23:52:03.445314+00:00   \n",
       "4           4  stjf559mu275c9j3  2022-05-14 07:59:11.570870+00:00   \n",
       "\n",
       "  malware_family malware_group  \\\n",
       "0         Laroux           NaN   \n",
       "1         Laroux           NaN   \n",
       "2      CoinMiner  LinuxMalware   \n",
       "3      CoinMiner  LinuxMalware   \n",
       "4    InstallCore           NaN   \n",
       "\n",
       "                                        malware_info           src_ip  \\\n",
       "0                                                NaN  192.168.180.160   \n",
       "1                                                NaN  192.168.180.122   \n",
       "2  {\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...  192.168.180.160   \n",
       "3  {\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...  192.168.180.139   \n",
       "4                                                NaN  192.168.180.169   \n",
       "\n",
       "   src_port         dst_ip  dst_port  ...   d2   d3   d4   d5   d6  d7  d8  \\\n",
       "0   49684.0   52.109.20.75     443.0  ...  1.0  1.0  1.0  1.0  0.0 NaN NaN   \n",
       "1   49694.0    20.42.73.26     443.0  ...  1.0  1.0  1.0  1.0  0.0 NaN NaN   \n",
       "2   49723.0  20.189.173.12     443.0  ...  1.0  1.0  1.0  1.0  0.0 NaN NaN   \n",
       "3   51961.0   23.32.129.39     443.0  ...  1.0  1.0  1.0  1.0  0.0 NaN NaN   \n",
       "4   49174.0  172.67.188.70     443.0  ...  1.0  1.0  1.0  1.0  0.0 NaN NaN   \n",
       "\n",
       "   d9  d10      label  \n",
       "0 NaN  NaN     benign  \n",
       "1 NaN  NaN     benign  \n",
       "2 NaN  NaN     benign  \n",
       "3 NaN  NaN     benign  \n",
       "4 NaN  NaN  malicious  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62844544",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'insert_id', 'timestamp', 'malware_family',\n",
       "       'malware_group', 'malware_info', 'src_ip', 'src_port', 'dst_ip',\n",
       "       'dst_port', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10',\n",
       "       'd1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed0a722",
   "metadata": {},
   "source": [
    "Getting rid of the Unnamed column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "b20042be",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop('Unnamed: 0', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "7e9cfb2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['binary_label'] = train_data['label'].map({'benign': 0, 'malicious': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d803f522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_data['label']);\n",
    "plt.xlabel('Label');\n",
    "plt.ylabel('Count of Datapoints');\n",
    "plt.title('Distribution of data for each label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "b33b5efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0992976980101443"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_data[train_data.label == 'malicious'].shape[0]/train_data.shape[0])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18daea3",
   "metadata": {},
   "source": [
    "In the above plot it can be clearly seen that there is a problem of class imbalance where only 1% of the data accounts for malicious sessions. And this is usually the case in the kind of problem we are trying to solve here. To address this problem we will hav to apply one of the follwoing techniques so that the model generalizes well for both the classes:\n",
    " <br> - Undersampling</font>\n",
    " <br> - Oversampling</font>\n",
    " <br> - SMOTE analysis</font><br>\n",
    "<br>But we will keep this for future work of this project.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb42b68",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50396b93",
   "metadata": {},
   "source": [
    "Decoding the payload into raw bytes and concatenatenating them into a TLS stream for further preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c6374a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['payload_concatenated'] = train_data[['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']\n",
    "          ].apply(lambda x: b''.join(\n",
    "    [base64.b64decode(p) if type(p) != float else b'' for p in x.values]), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1412f98c",
   "metadata": {},
   "source": [
    "#### Generating TLS related features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa61af03",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Fetching more features from TLS payload information\n",
    "train_data_payload = dict(train_data[['insert_id', 'payload_concatenated']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "207bfba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Method to extract TLS handshake fields and values\n",
    "def gen_attributes(insert_id, payload):\n",
    "        load_layer(\"tls\")\n",
    "        id_attributes = {}\n",
    "        x_tls = TLS(payload)\n",
    "        attributes = {}\n",
    "        print(insert_id)\n",
    "        while True:\n",
    "                if x_tls.fields == {}:\n",
    "                        break\n",
    "                if type(x_tls) == scapy.packet.NoPayload or type(x_tls) == scapy.packet.Raw or type(x_tls) == scapy.layers.tls.record._TLSEncryptedContent:\n",
    "                        break\n",
    "                if hasattr(x_tls, 'version'):\n",
    "                        attributes['tls_version'] = x_tls.version\n",
    "                if not hasattr(x_tls, 'msg'):\n",
    "                        continue\n",
    "                for m in x_tls.msg:\n",
    "                        if type(m) == scapy.packet.Raw:\n",
    "                                break\n",
    "                        # client hello\n",
    "                        if type(m) == scapy.layers.tls.handshake.TLSClientHello:\n",
    "                                attributes['client_cipher_len'] = m.cipherslen\n",
    "                                attributes['client_ciphers'] = m.ciphers\n",
    "                                attributes['client_extension_len'] = m.extlen\n",
    "                                attributes['client_extensions'] = []\n",
    "                                if m.ext:\n",
    "                                        for ext in m.ext:\n",
    "                                                attributes['client_extensions'].append(ext.type)\n",
    "                        # server hello\n",
    "                        if type(m) == scapy.layers.tls.handshake.TLSServerHello:\n",
    "                                attributes['server_cipher'] = m.cipher\n",
    "                                attributes['server_extension_len'] = m.extlen\n",
    "                                attributes['server_extensions'] = []\n",
    "                                if m.ext:\n",
    "                                        for ext in m.ext:\n",
    "                                                attributes['server_extensions'].append(ext.type)\n",
    "                        # certificate\n",
    "                        if type(m) == scapy.layers.tls.handshake.TLSCertificate:\n",
    "                                attributes['cert_len'] = m.certslen\n",
    "                                subject_common_name_cnt = 0\n",
    "                                for certs in m.certs:\n",
    "                                        if len(certs) >= 2:\n",
    "                                                cert = certs[1]\n",
    "                                                if 'commonName' in cert.subject:\n",
    "                                                        subject_common_name_cnt += 1\n",
    "                                attributes['subject_common_name_cnt'] = subject_common_name_cnt\n",
    "                x_tls = x_tls.payload\n",
    "        id_attributes[insert_id] = attributes\n",
    "        return id_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57a3c7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "### For each insert id extract TLS handshake features\n",
    "result = Parallel(n_jobs=1, prefer='threads')(delayed(gen_attributes)(insert_id, payload) for insert_id, payload in train_data_payload.items())\n",
    "train_data_payload_attributes = {k: v for x in result for k, v in x.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7c4da5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Generate a feature dataframe from the TLS handshake features.\n",
    "train_data_features = pd.DataFrame.from_dict(train_data_payload_attributes, orient='index')\n",
    "train_data_final = pd.merge(train_data, train_data_features, on='insert_id', how='outer')\n",
    "\n",
    "### Save to file since extraction of TLS handshake features is time consuming\n",
    "train_data_final.to_csv(\"train_data_final.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2576e711",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train_data_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2e8adaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Getting rid of index related unnecessary columns\n",
    "train_data.drop(['Unnamed: 0.1', 'Unnamed: 0'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdf07dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102520, 40)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "798557f4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>insert_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>malware_family</th>\n",
       "      <th>malware_group</th>\n",
       "      <th>malware_info</th>\n",
       "      <th>src_ip</th>\n",
       "      <th>src_port</th>\n",
       "      <th>dst_ip</th>\n",
       "      <th>dst_port</th>\n",
       "      <th>p1</th>\n",
       "      <th>...</th>\n",
       "      <th>test</th>\n",
       "      <th>client_cipher_len</th>\n",
       "      <th>client_ciphers</th>\n",
       "      <th>client_extension_len</th>\n",
       "      <th>client_extensions</th>\n",
       "      <th>server_cipher</th>\n",
       "      <th>server_extension_len</th>\n",
       "      <th>server_extensions</th>\n",
       "      <th>cert_len</th>\n",
       "      <th>subject_common_name_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qqjjl78hq0mr2n7h</td>\n",
       "      <td>2022-05-20 10:48:22.431120+00:00</td>\n",
       "      <td>Laroux</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.168.180.160</td>\n",
       "      <td>49684.0</td>\n",
       "      <td>52.109.20.75</td>\n",
       "      <td>443.0</td>\n",
       "      <td>FgMDAMgBAADEAwNiXh1fwoKclK96t6RtIW9ILkyQCCYkk1...</td>\n",
       "      <td>...</td>\n",
       "      <td>b'\\x16\\x03\\x03\\x00\\xc8\\x01\\x00\\x00\\xc4\\x03\\x03...</td>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[5, 23, 65281]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>u8ikd767a8vq7e6z</td>\n",
       "      <td>2022-08-10 18:50:19.040566+00:00</td>\n",
       "      <td>Laroux</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.168.180.122</td>\n",
       "      <td>49694.0</td>\n",
       "      <td>20.42.73.26</td>\n",
       "      <td>443.0</td>\n",
       "      <td>FgMDAM8BAADLAwNi8gYWgd8yr6rb9HbDCHEmEIWIbPRQTR...</td>\n",
       "      <td>...</td>\n",
       "      <td>b'\\x16\\x03\\x03\\x00\\xcf\\x01\\x00\\x00\\xcb\\x03\\x03...</td>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[23, 65281, 0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m5fx7zv0g2jbke9x</td>\n",
       "      <td>2022-08-20 11:44:43.778929+00:00</td>\n",
       "      <td>CoinMiner</td>\n",
       "      <td>LinuxMalware</td>\n",
       "      <td>{\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...</td>\n",
       "      <td>192.168.180.160</td>\n",
       "      <td>49723.0</td>\n",
       "      <td>20.189.173.12</td>\n",
       "      <td>443.0</td>\n",
       "      <td>FgMDAM8BAADLAwNjAK4l5Qji2zApC8JS4SoMV3LDfSt4/b...</td>\n",
       "      <td>...</td>\n",
       "      <td>b'\\x16\\x03\\x03\\x00\\xcf\\x01\\x00\\x00\\xcb\\x03\\x03...</td>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[23, 65281, 0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>y22wwz7hdy93mel9</td>\n",
       "      <td>2022-08-19 23:52:03.445314+00:00</td>\n",
       "      <td>CoinMiner</td>\n",
       "      <td>LinuxMalware</td>\n",
       "      <td>{\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...</td>\n",
       "      <td>192.168.180.139</td>\n",
       "      <td>51961.0</td>\n",
       "      <td>23.32.129.39</td>\n",
       "      <td>443.0</td>\n",
       "      <td>FgMDAL4BAAC6AwNi/JRCTyRuVTJK/GpWrHSjx9AeRZ9z1L...</td>\n",
       "      <td>...</td>\n",
       "      <td>b'\\x16\\x03\\x03\\x00\\xbe\\x01\\x00\\x00\\xba\\x03\\x03...</td>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>89.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49199.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>[65281, 0, 11, 35, 5]</td>\n",
       "      <td>3551.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>stjf559mu275c9j3</td>\n",
       "      <td>2022-05-14 07:59:11.570870+00:00</td>\n",
       "      <td>InstallCore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.168.180.169</td>\n",
       "      <td>49174.0</td>\n",
       "      <td>172.67.188.70</td>\n",
       "      <td>443.0</td>\n",
       "      <td>FgMBAHEBAABtAwFiWM9tEHnN9LM8Kip+B45LYFTekLy9Pj...</td>\n",
       "      <td>...</td>\n",
       "      <td>b'\\x16\\x03\\x01\\x00q\\x01\\x00\\x00m\\x03\\x01bX\\xcf...</td>\n",
       "      <td>24</td>\n",
       "      <td>[47, 53, 5, 10, 49171, 49172, 49161, 49162, 50...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>[0, 10, 11, 65281]</td>\n",
       "      <td>49161.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>[0, 65281, 11]</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          insert_id                         timestamp malware_family  \\\n",
       "0  qqjjl78hq0mr2n7h  2022-05-20 10:48:22.431120+00:00         Laroux   \n",
       "1  u8ikd767a8vq7e6z  2022-08-10 18:50:19.040566+00:00         Laroux   \n",
       "2  m5fx7zv0g2jbke9x  2022-08-20 11:44:43.778929+00:00      CoinMiner   \n",
       "3  y22wwz7hdy93mel9  2022-08-19 23:52:03.445314+00:00      CoinMiner   \n",
       "4  stjf559mu275c9j3  2022-05-14 07:59:11.570870+00:00    InstallCore   \n",
       "\n",
       "  malware_group                                       malware_info  \\\n",
       "0           NaN                                                NaN   \n",
       "1           NaN                                                NaN   \n",
       "2  LinuxMalware  {\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...   \n",
       "3  LinuxMalware  {\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...   \n",
       "4           NaN                                                NaN   \n",
       "\n",
       "            src_ip  src_port         dst_ip  dst_port  \\\n",
       "0  192.168.180.160   49684.0   52.109.20.75     443.0   \n",
       "1  192.168.180.122   49694.0    20.42.73.26     443.0   \n",
       "2  192.168.180.160   49723.0  20.189.173.12     443.0   \n",
       "3  192.168.180.139   51961.0   23.32.129.39     443.0   \n",
       "4  192.168.180.169   49174.0  172.67.188.70     443.0   \n",
       "\n",
       "                                                  p1  ...  \\\n",
       "0  FgMDAMgBAADEAwNiXh1fwoKclK96t6RtIW9ILkyQCCYkk1...  ...   \n",
       "1  FgMDAM8BAADLAwNi8gYWgd8yr6rb9HbDCHEmEIWIbPRQTR...  ...   \n",
       "2  FgMDAM8BAADLAwNjAK4l5Qji2zApC8JS4SoMV3LDfSt4/b...  ...   \n",
       "3  FgMDAL4BAAC6AwNi/JRCTyRuVTJK/GpWrHSjx9AeRZ9z1L...  ...   \n",
       "4  FgMBAHEBAABtAwFiWM9tEHnN9LM8Kip+B45LYFTekLy9Pj...  ...   \n",
       "\n",
       "                                                test client_cipher_len  \\\n",
       "0  b'\\x16\\x03\\x03\\x00\\xc8\\x01\\x00\\x00\\xc4\\x03\\x03...                56   \n",
       "1  b'\\x16\\x03\\x03\\x00\\xcf\\x01\\x00\\x00\\xcb\\x03\\x03...                56   \n",
       "2  b'\\x16\\x03\\x03\\x00\\xcf\\x01\\x00\\x00\\xcb\\x03\\x03...                56   \n",
       "3  b'\\x16\\x03\\x03\\x00\\xbe\\x01\\x00\\x00\\xba\\x03\\x03...                56   \n",
       "4  b'\\x16\\x03\\x01\\x00q\\x01\\x00\\x00m\\x03\\x01bX\\xcf...                24   \n",
       "\n",
       "                                      client_ciphers client_extension_len  \\\n",
       "0  [49196, 49195, 49200, 49199, 159, 158, 49188, ...                 99.0   \n",
       "1  [49196, 49195, 49200, 49199, 159, 158, 49188, ...                106.0   \n",
       "2  [49196, 49195, 49200, 49199, 159, 158, 49188, ...                106.0   \n",
       "3  [49196, 49195, 49200, 49199, 159, 158, 49188, ...                 89.0   \n",
       "4  [47, 53, 5, 10, 49171, 49172, 49161, 49162, 50...                 44.0   \n",
       "\n",
       "                   client_extensions  server_cipher  server_extension_len  \\\n",
       "0  [0, 5, 10, 11, 13, 35, 23, 65281]        49200.0                  13.0   \n",
       "1  [0, 5, 10, 11, 13, 35, 23, 65281]        49200.0                  13.0   \n",
       "2  [0, 5, 10, 11, 13, 35, 23, 65281]        49200.0                  13.0   \n",
       "3  [0, 5, 10, 11, 13, 35, 23, 65281]        49199.0                  25.0   \n",
       "4                 [0, 10, 11, 65281]        49161.0                  15.0   \n",
       "\n",
       "       server_extensions  cert_len  subject_common_name_cnt  \n",
       "0         [5, 23, 65281]       NaN                      NaN  \n",
       "1         [23, 65281, 0]       NaN                      NaN  \n",
       "2         [23, 65281, 0]       NaN                      NaN  \n",
       "3  [65281, 0, 11, 35, 5]    3551.0                      2.0  \n",
       "4         [0, 65281, 11]    4196.0                      4.0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4ad6c58",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['insert_id', 'timestamp', 'malware_family', 'malware_group',\n",
       "       'malware_info', 'src_ip', 'src_port', 'dst_ip', 'dst_port', 'p1', 'p2',\n",
       "       'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10', 'd1', 'd2', 'd3', 'd4',\n",
       "       'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'label', 'test',\n",
       "       'client_cipher_len', 'client_ciphers', 'client_extension_len',\n",
       "       'client_extensions', 'server_cipher', 'server_extension_len',\n",
       "       'server_extensions', 'cert_len', 'subject_common_name_cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "66e038cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102520 entries, 0 to 102519\n",
      "Data columns (total 41 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   insert_id                102520 non-null  object \n",
      " 1   timestamp                102520 non-null  object \n",
      " 2   malware_family           102520 non-null  object \n",
      " 3   malware_group            67540 non-null   object \n",
      " 4   malware_info             67343 non-null   object \n",
      " 5   src_ip                   102520 non-null  object \n",
      " 6   src_port                 102520 non-null  float64\n",
      " 7   dst_ip                   102520 non-null  object \n",
      " 8   dst_port                 102520 non-null  float64\n",
      " 9   p1                       102520 non-null  object \n",
      " 10  p2                       102209 non-null  object \n",
      " 11  p3                       102028 non-null  object \n",
      " 12  p4                       102024 non-null  object \n",
      " 13  p5                       100994 non-null  object \n",
      " 14  p6                       682 non-null     object \n",
      " 15  p7                       0 non-null       float64\n",
      " 16  p8                       0 non-null       float64\n",
      " 17  p9                       0 non-null       float64\n",
      " 18  p10                      0 non-null       float64\n",
      " 19  d1                       102520 non-null  float64\n",
      " 20  d2                       102520 non-null  float64\n",
      " 21  d3                       102520 non-null  float64\n",
      " 22  d4                       102520 non-null  float64\n",
      " 23  d5                       102520 non-null  float64\n",
      " 24  d6                       102520 non-null  float64\n",
      " 25  d7                       0 non-null       float64\n",
      " 26  d8                       50 non-null      float64\n",
      " 27  d9                       0 non-null       float64\n",
      " 28  d10                      50 non-null      float64\n",
      " 29  label                    102520 non-null  object \n",
      " 30  test                     102520 non-null  object \n",
      " 31  client_cipher_len        102520 non-null  int64  \n",
      " 32  client_ciphers           102520 non-null  object \n",
      " 33  client_extension_len     102431 non-null  float64\n",
      " 34  client_extensions        102520 non-null  object \n",
      " 35  server_cipher            102011 non-null  float64\n",
      " 36  server_extension_len     101988 non-null  float64\n",
      " 37  server_extensions        102011 non-null  object \n",
      " 38  cert_len                 26931 non-null   float64\n",
      " 39  subject_common_name_cnt  26931 non-null   float64\n",
      " 40  binary_label             102520 non-null  int64  \n",
      "dtypes: float64(21), int64(2), object(18)\n",
      "memory usage: 32.1+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b890c5",
   "metadata": {},
   "source": [
    "There are certain attributes that have NA values which means that the information was absent at the time of the collection of the data. These values can be filled with zero simply denoting that the values were not present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "1d75059f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9223235d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>FgMDAMwBAADIAwNi7PHXXnc0ED0pn/5oMnzxI6MEe3xx4u...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1601.995844</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49763.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49200.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3551.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65534.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60001.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49200.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4953.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               insert_id                         timestamp malware_family  \\\n",
       "count             102520                            102520         102520   \n",
       "unique            102520                            102520              9   \n",
       "top     qqjjl78hq0mr2n7h  2022-05-20 10:48:22.431120+00:00      coinminer   \n",
       "freq                   1                                 1          67271   \n",
       "mean                 NaN                               NaN            NaN   \n",
       "std                  NaN                               NaN            NaN   \n",
       "min                  NaN                               NaN            NaN   \n",
       "25%                  NaN                               NaN            NaN   \n",
       "50%                  NaN                               NaN            NaN   \n",
       "75%                  NaN                               NaN            NaN   \n",
       "max                  NaN                               NaN            NaN   \n",
       "\n",
       "       malware_group                                       malware_info  \\\n",
       "count         102520                                             102520   \n",
       "unique             9                                                  5   \n",
       "top     linuxmalware  {\"alias0\": \"MoneroMiner\", \"alias1\": \"NeksMiner...   \n",
       "freq           67044                                              67240   \n",
       "mean             NaN                                                NaN   \n",
       "std              NaN                                                NaN   \n",
       "min              NaN                                                NaN   \n",
       "25%              NaN                                                NaN   \n",
       "50%              NaN                                                NaN   \n",
       "75%              NaN                                                NaN   \n",
       "max              NaN                                                NaN   \n",
       "\n",
       "                 src_ip       src_port         dst_ip       dst_port  \\\n",
       "count            102520  102520.000000         102520  102520.000000   \n",
       "unique              116            NaN            465            NaN   \n",
       "top     192.168.180.139            NaN  20.80.190.220            NaN   \n",
       "freq               2220            NaN          11541            NaN   \n",
       "mean                NaN   51525.247376            NaN     443.665694   \n",
       "std                 NaN    4139.940337            NaN     187.919654   \n",
       "min                 NaN    1028.000000            NaN     443.000000   \n",
       "25%                 NaN   49718.000000            NaN     443.000000   \n",
       "50%                 NaN   49730.000000            NaN     443.000000   \n",
       "75%                 NaN   49763.000000            NaN     443.000000   \n",
       "max                 NaN   65534.000000            NaN   60001.000000   \n",
       "\n",
       "                                                       p1  ...  \\\n",
       "count                                              102520  ...   \n",
       "unique                                             101956  ...   \n",
       "top     FgMDAMwBAADIAwNi7PHXXnc0ED0pn/5oMnzxI6MEe3xx4u...  ...   \n",
       "freq                                                    3  ...   \n",
       "mean                                                  NaN  ...   \n",
       "std                                                   NaN  ...   \n",
       "min                                                   NaN  ...   \n",
       "25%                                                   NaN  ...   \n",
       "50%                                                   NaN  ...   \n",
       "75%                                                   NaN  ...   \n",
       "max                                                   NaN  ...   \n",
       "\n",
       "        client_cipher_len                                     client_ciphers  \\\n",
       "count       102520.000000                                             102520   \n",
       "unique                NaN                                                 24   \n",
       "top                   NaN  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "freq                  NaN                                              98522   \n",
       "mean            54.789661                                                NaN   \n",
       "std              6.162862                                                NaN   \n",
       "min              6.000000                                                NaN   \n",
       "25%             56.000000                                                NaN   \n",
       "50%             56.000000                                                NaN   \n",
       "75%             56.000000                                                NaN   \n",
       "max            132.000000                                                NaN   \n",
       "\n",
       "       client_extension_len                  client_extensions  server_cipher  \\\n",
       "count         102520.000000                             102520  102520.000000   \n",
       "unique                  NaN                                 25            NaN   \n",
       "top                     NaN  [0, 5, 10, 11, 13, 35, 23, 65281]            NaN   \n",
       "freq                    NaN                              98377            NaN   \n",
       "mean              95.335456                                NaN   48907.333857   \n",
       "std               13.458045                                NaN    3773.972389   \n",
       "min                0.000000                                NaN       0.000000   \n",
       "25%               89.000000                                NaN   49199.000000   \n",
       "50%               98.000000                                NaN   49200.000000   \n",
       "75%              103.000000                                NaN   49200.000000   \n",
       "max              389.000000                                NaN   49200.000000   \n",
       "\n",
       "        server_extension_len  server_extensions       cert_len  \\\n",
       "count          102520.000000             102520  102520.000000   \n",
       "unique                   NaN                 44            NaN   \n",
       "top                      NaN     [5, 23, 65281]            NaN   \n",
       "freq                     NaN              60022            NaN   \n",
       "mean               16.017957                NaN     954.830179   \n",
       "std                 5.413896                NaN    1601.995844   \n",
       "min                 0.000000                NaN       0.000000   \n",
       "25%                13.000000                NaN       0.000000   \n",
       "50%                13.000000                NaN       0.000000   \n",
       "75%                25.000000                NaN    3551.000000   \n",
       "max                44.000000                NaN    4953.000000   \n",
       "\n",
       "        subject_common_name_cnt   binary_label  \n",
       "count             102520.000000  102520.000000  \n",
       "unique                      NaN            NaN  \n",
       "top                         NaN            NaN  \n",
       "freq                        NaN            NaN  \n",
       "mean                   0.544079       0.010993  \n",
       "std                    0.930315       0.104270  \n",
       "min                    0.000000       0.000000  \n",
       "25%                    0.000000       0.000000  \n",
       "50%                    0.000000       0.000000  \n",
       "75%                    2.000000       0.000000  \n",
       "max                    4.000000       1.000000  \n",
       "\n",
       "[11 rows x 41 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ee169e",
   "metadata": {},
   "source": [
    "### Potential Feature/Attribute Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab42140a",
   "metadata": {},
   "source": [
    "A malicious client can change or modify the pattern of connection to infilterate the server. This information can be caputred in the TLS cipher suite. Hence, all the information associated with the cipher can be beneficial in differentiationg between malicious and bening connections. Following is the list of cipher related features: \n",
    " - client_cipher_len \n",
    " - client_ciphers : This looks like a list of ciphers offered by the client i.e. a categorical variable and will have to be encoded in numeric form to be used in the model\n",
    " - server_cipher : This is the cipher accepted by the server\n",
    " - client_extension_len\n",
    " - client_extensions\n",
    " - server_extension_len\n",
    " - server_extensions\n",
    " - cert_len \n",
    " - subject_common_name_cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863c9f09",
   "metadata": {},
   "source": [
    "> ####  List of Numerical Features\n",
    "        - 'client_cipher_len'\n",
    "        - 'client_extension_len'\n",
    "        - 'server_extension_len'\n",
    "        - 'cert_len'\n",
    "        - 'subject_common_name_cnt'\n",
    "        \n",
    "> #### List of Categorical Features (These features will be One-hot encoded as part of feature Engineering)\n",
    "       - 'server_cipher',\n",
    "       - 'client_ciphers', \n",
    "       - 'client_extensions',  \n",
    "       - 'server_extensions'\n",
    "       \n",
    " - I am going to ignore the following features for the scope of this analysis as I am not sure if the complete list of these extensions are included in this dataset to cover all the categories that could be seen in the test set. But given an opportunity for further exploration I will one-hot encode these features as well and use in my analysis.\n",
    "       - 'client_ciphers', \n",
    "       - 'client_extensions',  \n",
    "       - 'server_extensions'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c0472e97",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>subject_common_name_cnt</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[5, 23, 65281]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[23, 65281, 0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[23, 65281, 0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>89.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49199.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>[65281, 0, 11, 35, 5]</td>\n",
       "      <td>3551.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>[47, 53, 5, 10, 49171, 49172, 49161, 49162, 50...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>[0, 10, 11, 65281]</td>\n",
       "      <td>49161.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>[0, 65281, 11]</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102515</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>[65281, 0, 11, 35, 5]</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102516</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49199.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[5, 23, 65281]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102517</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[5, 23, 65281]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102518</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>[65281, 0, 11, 35, 5]</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102519</th>\n",
       "      <td>56</td>\n",
       "      <td>[49196, 49195, 49200, 49199, 159, 158, 49188, ...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>[0, 5, 10, 11, 13, 35, 23, 65281]</td>\n",
       "      <td>49199.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[5, 23, 65281]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102520 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        client_cipher_len                                     client_ciphers  \\\n",
       "0                      56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "1                      56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "2                      56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "3                      56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "4                      24  [47, 53, 5, 10, 49171, 49172, 49161, 49162, 50...   \n",
       "...                   ...                                                ...   \n",
       "102515                 56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "102516                 56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "102517                 56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "102518                 56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "102519                 56  [49196, 49195, 49200, 49199, 159, 158, 49188, ...   \n",
       "\n",
       "        client_extension_len                  client_extensions  \\\n",
       "0                       99.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "1                      106.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "2                      106.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "3                       89.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "4                       44.0                 [0, 10, 11, 65281]   \n",
       "...                      ...                                ...   \n",
       "102515                  98.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "102516                  83.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "102517                  99.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "102518                  98.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "102519                  97.0  [0, 5, 10, 11, 13, 35, 23, 65281]   \n",
       "\n",
       "        server_cipher  server_extension_len      server_extensions  cert_len  \\\n",
       "0             49200.0                  13.0         [5, 23, 65281]       NaN   \n",
       "1             49200.0                  13.0         [23, 65281, 0]       NaN   \n",
       "2             49200.0                  13.0         [23, 65281, 0]       NaN   \n",
       "3             49199.0                  25.0  [65281, 0, 11, 35, 5]    3551.0   \n",
       "4             49161.0                  15.0         [0, 65281, 11]    4196.0   \n",
       "...               ...                   ...                    ...       ...   \n",
       "102515        49200.0                  25.0  [65281, 0, 11, 35, 5]    3704.0   \n",
       "102516        49199.0                  13.0         [5, 23, 65281]       NaN   \n",
       "102517        49200.0                  13.0         [5, 23, 65281]       NaN   \n",
       "102518        49200.0                  25.0  [65281, 0, 11, 35, 5]    3704.0   \n",
       "102519        49199.0                  13.0         [5, 23, 65281]       NaN   \n",
       "\n",
       "        subject_common_name_cnt      label  \n",
       "0                           NaN     benign  \n",
       "1                           NaN     benign  \n",
       "2                           NaN     benign  \n",
       "3                           2.0     benign  \n",
       "4                           4.0  malicious  \n",
       "...                         ...        ...  \n",
       "102515                      2.0     benign  \n",
       "102516                      NaN     benign  \n",
       "102517                      NaN     benign  \n",
       "102518                      2.0     benign  \n",
       "102519                      NaN     benign  \n",
       "\n",
       "[102520 rows x 10 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[['client_cipher_len', 'client_ciphers', 'client_extension_len',\n",
    "       'client_extensions', 'server_cipher', 'server_extension_len',\n",
    "       'server_extensions', 'cert_len', 'subject_common_name_cnt','label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "2caa4257",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data[train_data.label == 'malicious']['label'], train_data[train_data.label == 'malicious']['client_cipher_len'], 'ro');\n",
    "plt.plot(train_data[train_data.label == 'benign']['label'], train_data[train_data.label == 'benign']['client_cipher_len'], 'go');\n",
    "plt.xlabel('Class Label');\n",
    "plt.ylabel('client_cipher_len');\n",
    "plt.title('client_cipher_len wrt Class Labels');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e7c0d71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grpd_df = train_data.groupby(['server_cipher', 'label']).size().reset_index(name = 'count_of_labels_per_attribute_grp')\n",
    "malicious = grpd_df[grpd_df.label == 'malicious']\n",
    "benign = grpd_df[grpd_df.label == 'benign']\n",
    "\n",
    "mal_benign_df = pd.merge(malicious, benign, on = 'server_cipher', how = 'outer', suffixes = ('_malicious', '_benign')).fillna(0).drop(['label_malicious', 'label_benign'], axis = 1)\n",
    "\n",
    "malicious_count = mal_benign_df['count_of_labels_per_attribute_grp_malicious'].apply(lambda x: np.log(x+1)).values\n",
    "benign_count = mal_benign_df['count_of_labels_per_attribute_grp_benign'].apply(lambda x: np.log(x+1)).values\n",
    "\n",
    "X_axis = np.arange(len(mal_benign_df['server_cipher']))\n",
    "\n",
    "plt.bar(X_axis - 0.2, malicious_count, 0.4, label = 'Malicious', color = 'red')\n",
    "plt.bar(X_axis + 0.2, benign_count, 0.4, label = 'Benign')\n",
    "\n",
    "plt.xticks(X_axis, mal_benign_df['server_cipher'], rotation = 90)\n",
    "plt.xlabel(\"Server Cipher\")\n",
    "plt.ylabel(\"Count of unique connections\")\n",
    "plt.title(\"Number of connections in each group\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "4bcbeb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data[train_data.label == 'malicious']['label'], np.log(train_data[train_data.label == 'malicious']['client_extension_len']), 'ro');\n",
    "### Log was taken to capture the small differences in client_extension_len\n",
    "plt.plot(train_data[train_data.label == 'benign']['label'], np.log(train_data[train_data.label == 'benign']['client_extension_len']), 'go');\n",
    "plt.xlabel('Class Label');\n",
    "plt.ylabel('Log of client_extension_len');\n",
    "plt.title('Log of client_extension_len wrt Class Labels');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "12c7b058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2NhryO6OPpKQk/P777wgJCcFHH32kbU9JScm1CMnp77v4+HjUqFEDwP9+T7777rtcVwPm9Y/CBg0aYP369RBC4PTp0wgNDcXnn38OKysrnRwpO/bgkA4bGxs0b94cW7Zs0fnXSnp6OtasWYPKlStr96Dx8/PDvn37dP6Vmp6ejk2bNuV7ntq1a6NSpUpYt24dhBDa9idPniAsLEy7sqogKlasiMGDB6Nv376IiYnB06dPUbt2bdSsWROnTp2Ct7d3jo+sBUFu+vbtC0tLS4SGhiI0NBSVKlVC+/btdT6jUufKS5cuXfDgwQNoNJocz/HyRXbPnj2YNWsWPvnkE+zZswcODg54++23kZqaqo3JrVfB2J/H1NQUzZs3xw8//AAAiI6OzjXW399fe9F82apVq2BtbV0oy8oL6+cLAGZmZmjVqhX27NmDffv2oV27dgAyLvRlypTBJ598oi149CG3B2vYsGFwcXHBpEmTEBsbm2NMZm9MTjKLrMzzZvrxxx/zPG+tWrXwySefoEGDBjn+Psj5ndGHJEkQQmTLc/ny5dBoNDm+Z+3atTrPDx8+jOvXr2tXe7722mtwdHTE2bNnc/09MTc31yu3hg0bYv78+XB0dCzwZy0N2INTSu3bty/HZdydO3fGrFmz0K5dO7Rp0wYTJkyAubk5Fi1ahH///Re//PKL9i+rqVOn4rfffoO/vz+mTp0KKysrLFmyRNuN/fLcmqxMTEwwe/Zs9O/fH126dMHw4cORkpKCOXPmIDExEV999ZVBn6t58+bo0qULvLy8ULZsWZw7dw6rV6/WKZh+/PFHdOrUCR06dMDgwYNRqVIlJCQk4Ny5c4iOjtarQAMAR0dHvPnmmwgNDUViYiImTJiQ7TMrda689OnTB2vXrkXnzp0xduxY+Pj4wMzMDLdu3cL+/fsRGBiIN998E3Fxcdq9hEJCQmBiYoINGzagVatWmDRpEhYsWAAgYwjAysoKa9euRd26dWFraws3Nze4ubkp/nmWLFmCffv24Y033kCVKlXw/Plz/PzzzwCQY29EppCQEPz+++9o06YNPvvsMzg5OWHt2rXYsWMHZs+eDQcHB4O/TzkK4+ebyd/fHx9++CGA/303VlZWaNGiBXbv3g0vLy84OzvrdSw7OztUrVoVv/76K/z9/eHk5ITy5cvDw8Mjx3gHBwf8+uuv6NKlCxo3bqyz0d/FixexZs0anDp1KteerBYtWqBs2bIYMWIEQkJCYGZmhrVr1+LUqVM6cadPn8aoUaPQu3dv1KxZE+bm5ti3bx9Onz6t7a0w9HcmU3Jyco579lSoUAF+fn5o1aoV5syZo/0+IiIi8NNPP8HR0THH40VFReHdd99F7969cfPmTUydOhWVKlXSDlPa2triu+++w6BBg5CQkIBevXrB2dkZ9+7dw6lTp3Dv3j0sXrw4x2P//vvvWLRoEbp3747q1atDCIEtW7YgMTFRW+RSHopyhjMVvsxZ+rk9MmfvHzhwQLRt21bY2NgIKysr8eqrr4rffvst2/EOHDggmjdvLiwsLISLi4uYOHGi+PrrrwUAnRVGudm2bZto3ry5sLS0FDY2NsLf318cOnRIJ0bOKqqPPvpIeHt7i7JlywoLCwtRvXp1MX78eHH//n2duFOnTom33npLODs7CzMzM+Hi4iLatm0rlixZku27ymvF2e7du7Xf3curV4xxrry8ePFCfPPNN6Jhw4bC0tJS2Nraijp16ojhw4eLixcvirS0NOHn5ycqVqyYbcXOnDlzBACxdetWbdsvv/wi6tSpI8zMzLKttinI58m6EikyMlK8+eabomrVqsLCwkKUK1dO+Pn5ie3bt+u8L2sOQghx5swZ0bVrV+Hg4CDMzc1Fw4YNs638yu135+rVq7muFMtNTquolP4+8nLq1CkBQNSsWVOnfebMmQKACA4OzvYeAGLkyJE5Hm/v3r2icePGwsLCQgAQgwYNyjeH+Ph4MXnyZFG/fn1hbW0tLCwsRI0aNcTw4cPFmTNntHE5raI6fPiw8PX1FdbW1qJChQri3XffFdHR0To/hzt37ojBgweLOnXqCBsbG2Frayu8vLzE/PnztUv69f2dyYmfn1+uf/f5+fkJIYS4deuW6Nmzpyhbtqyws7MTHTt2FP/++6+oWrWqzneU+TPdvXu3GDBggHB0dBRWVlaic+fO4uLFi9nOHRERId544w3h5OQkzMzMRKVKlcQbb7yh87uZdRXV+fPnRd++fYWnp6ewsrISDg4OwsfHR4SGhub7WUkISYiXxgeIFNC+fXtcu3YNFy5cKOpUiIiolOIQFRVIcHAwGjduDHd3dyQkJGDt2rXYs2ePdpIgERFRUWCBQwWi0Wjw2WefIT4+HpIkoV69eli9ejXeeeedok5NFdLS0vJ83cTEJM+5TpQ/IUSuE0gzmZqa8p5gRCUMh6iIiqlr166hWrVqecaEhIRku0cTyRMaGqqzvD8n+/fv1/uu20RUPLDAISqmUlNTdW7amZPM1U1kuAcPHuDq1at5xtSuXVuxJd9EVDhY4BAREZHqcPCeiIiIVKdUTjJOT0/H7du3YWdnx4mDREREJYQQAo8ePYKbm1u+CyxKZYFz+/btbHchJiIiopLh5s2bqFy5cp4xpbLAyZwsePPmTdjb2xdxNkRERKSP5ORkuLu76zXpv1QWOJnDUvb29ixwiIiIShh9ppdwkjERERGpDgscIiIiUh0WOERERKQ6LHCIiIhIdVjgEBERkeqwwCEiIiLVYYFDREREqsMCh4iIiFSHBQ4RERGpTqncydgo4uOBRo2AxETA0RE4eRJwcSnanIiIiApRaloqFkUtwuWEy/B08kSQdxDMy5gXSS6SEEIUyZmLUHJyMhwcHJCUlKTMrRpsbICnT7O3W1sDT54U/PhERETF3KQ9kzAvch40QqNtM5VMEewbjNntZityDjnXbw5RFVRuxQ2Q0W5jU7j5EBERFbJJeyZhzuE5OsUNAGiEBnMOz8GkPZMKPScWOAURH597cZPp6dOMOCIiIhVKTUvFvMh5ecbMi5yH1LTUQsooAwucgmjUSNk4IiKiEmZR1KJsPTdZaYQGi6IWFVJGGVjgFERiorJxREREJczlhMuKximFBU5BODoqG0dERFTCeDp5KhqnFBY4BXHypLJxREREJUyQdxBMJdM8Y0wlUwR5BxVSRhlY4BSEi0vGUvC8WFtzPxwiIlIt8zLmCPYNzjMm2De40PfDYYFTUE+e5F7kcB8cIiIqBWa3m42JLSZm68kxlUwxscVExfbBkYMb/Smx0R/AnYyJiKjUM/ZOxnKu3yxwlCpwiIiIyKi4kzERERGVaixwiIiISHVY4BAREZHqsMAhIiIiRTx+/hhvrn8TXou98Ob6N/H4+eMiy6VMkZ2ZiIiIVMNnmQ+O3z6ufX7m7hnYfW2HZm7NcOy9Y4WeD3twiIiIqECyFjcvO377OHyW+RRyRixwiIiIqAAeP3+ca3GT6fjt44U+XMUCh4iIiAw2YNsAReOUwgKHiIiIDHb54WVF45TCAoeIiIgM5lnWU9E4pbDAISIiIoOt7r5a0TilsMBRyrNnwKhRQIcOGX8+e1bUGRERERmdraUtmrk1yzOmmVsz2FraFlJGGVjgKKF7d8DaGvjhB2D37ow/ra0z2omIiFTu2HvHci1yimofHN5NvKB3E+/eHfj119xfDwwEtm0r2DmIiIhKgMfPH2PAtgG4/PAyPMt6YnX31Yr23Mi5frPAKUiB8+xZRk9Nfp4+BaysDD8PERERybp+c4iqICZOVDaOiIiIFMECpyAuXlQ2joiIiBTBAqcgatZUNo6IiIgUwTk4nINDRERUInAOTmGxsspYJZWXwEAWN0REVCqkpqViwZEFGP3HaCw4sgCpaalFlgt7cAq6TBwAHB2BpKTs7Q4OQGJiwY9PRERUzE3aMwnzIudBIzTaNlPJFMG+wZjdbrYi52APTmHy8cm5uAEy2n18CjcfIiKiQjZpzyTMOTxHp7gBAI3QYM7hOZi0Z1Kh58QenIL04Dx+DNjZ5R/36BFgW7hbVBMRERWG1LRUWH9pna24eZmpZIqnHz+FeRnzAp2LPTiFZcAAZeOIiIhKmEVRi/IsboCMnpxFUYsKKaMMLHAK4vJlZeOIiIhKmMsJ+l3j9I1TCgucgvD0VDaOiIiohPF00u8ap2+cUjgHh3NwiIiIDMY5OGpkaws0y/n28FrNmrG4ISIi1TIvY45g3+A8Y4J9gwtc3MjFAqegjh3Lvchp1izjdSIiIhWb3W42JraYCBNJt6wwlUwxscVExfbBkYMFjhKOH5fXTkREpEZZJr0U5SyYMkV2ZrWQpPxfL33TnIiIqBTJ3Ogvq3Ska9sLuxeHPTgFsXmzsnFEREQlTGpaKuZFzsszZl7kvEK/LxULnILo3VvZOCIiohKGG/3lYPHixfDy8oK9vT3s7e3h6+uLnTt35hofHh4OSZKyPc6fP1+IWRMREVGm4rrRX5HOwalcuTK++uor1KhRAwCwcuVKBAYG4sSJE6hfv36u74uJidFZ/16hQgWj50pERETZFdeN/oq0B6dr167o3LkzatWqhVq1amHmzJmwtbXFkSNH8nyfs7MzXFxctA9TU9NCyjiLTZuUjSMiIiphgryDYCrlfR02lUwR5B1USBllKDZzcDQaDdavX48nT57A19c3z9jGjRvD1dUV/v7+2L9/f77HTklJQXJyss5DEb16KRtHRERUwnCjv1ycOXMGtra2sLCwwIgRI7B161bUq1cvx1hXV1csXboUYWFh2LJlC2rXrg1/f3/8/fffeZ5j1qxZcHBw0D7c3d2V+wD5LQHnEnEiIlK5zI3+svbkFOVGf0V+L6rU1FTcuHEDiYmJCAsLw/LlyxEREZFrkZNV165dIUkStm/fnmtMSkoKUlJStM+Tk5Ph7u5e8HtRZdqyBRg8OOOeU5ns7IDQUKBHj4Ifn4iIqJjbcm4LRv8xGrcf39a2udm64bvO36FHXWWuhXLuRVXkBU5WAQEB8PT0xI8//qhX/MyZM7FmzRqcO3dO73ModrNNIKO46dUre09N5gaAmzezyCEiIlXbcm4Lem3sBZFlK2MJGdfCzW9tVqTIKdE32xRC6PS25OfEiRNwdXU1YkZ50GiAsWNzHobKbBs3LiOOiIhIhTTpGozdNTZbcQNA2zZu1zho0gv3Wliky8Q//vhjdOrUCe7u7nj06BHWr1+P8PBw7Nq1CwAwZcoUxMbGYtWqVQCABQsWwMPDA/Xr10dqairWrFmDsLAwhIWFFc0HOHAAuHUr99eFAG7ezIhr3brQ0iIiIiosB24cwK3k3K+FAgI3k2/iwI0DaO3RutDyKtIC586dOxgwYADi4uLg4OAALy8v7Nq1C+3atQMAxMXF4caNG9r41NRUTJgwAbGxsbCyskL9+vWxY8cOdO7cuWg+QFycsnFEREQlTNwj/a5x+sYppUgLnJ9++inP10NDQ3WeT5o0CZMmTTJiRjLpOzRWVENoRERERuZqp981Tt84pRS7OTglSsuWQOXKud9RXJIAd/eMOCIiIhVqWaUlKttX1k4ozkqCBHd7d7SsUrjXQhY4BWFqCixcmPHfWYuczOcLFmTEERERqZCpiSkWdsy4FmYtcjKfL+i4AKYmhXstZIFTUD16ZCwFr1RJt71yZS4RJyKiUqFH3R7Y/NZmVLLXvRZWtq+s2BJxuYrdPjiFQdF9cDKlpgKLFgGXLwOenkBQEGBeuNtSExERFaUzN86g4YqGEBCQIOHUkFNoUKWBYscv0Rv9FQbFC5wtWzL2w3l5yXjlyhnDV+zBISKiUkCanst8VAAiRJlSo0Rv9FfiZO5knHU/nNjYjPYtW4omLyIiokKSV3Gjz+vGwAKnILiTMRERlXJnb51VNE4pLHAKQs5OxkRERCrU4Cf95tjoG6cUFjgFwZ2MiYiolEtHuqJxSmGBUxDcyZiIiEo5Ez1LCX3jlMICpyC4kzEREZVyZ4adUTROKSxwCoI7GRMRUSlX2622onFKYYFTUNzJmIiISrEDN/RbSKNvnFKK9G7iqtGjBxAYmLFaKi4uY85Ny5bsuSEiItWLe6TfQhp945TCAkcppqZA69ZFnQUREVGhcrXTbyGNvnFK4RAVERERGaxllZaobF85zxh3e3e0rFK4C25Y4BAREZHBTE1M4Wqbd++Mi60LTE0Kd9oGCxwiIiIy2LPUZzh++3ieMcdvH8ez1GeFlFEGFjhERERksIl7JyoapxROMlaKRsNVVEREVOpcfHBR0TilsAdHCVu2AB4eQJs2QL9+GX96eGS0ExERqVjNcjUVjVOKJIQQhXrGYiA5ORkODg5ISkqCvb19wQ62ZQvQq1fGncNflrmTMTf7IyIiFXuW+gzWs6zzjXs65SmszK0KdC4512+DhqguXLiA8PBw3L17F+npuncH/eyzzww5ZMmk0QBjx2YvboCMNkkCxo3L2ASQw1VERKRCVuZWCKwdiF9jfs01JrB2YIGLG7lk9+AsW7YMH3zwAcqXLw8XFxdIL92DSZIkREdHK56k0hTrwQkPzxiOys/+/dwEkIiIVK37+u45FjmBtQOxrc82Rc5h1B6cGTNmYObMmZg8ebLBCapGnJ7bTusbR0REVEJt67MNz1KfYeLeibj44CJqlquJOQFzCr3nJpPsAufhw4fo3bu3MXIpeVz13HZa3zgiIqIS7JM/P8EP0T8AAHZf2Q0LjQXmdp1bJLnIHqIaNmwYmjVrhhEjRhgrJ6NTbIhKo8lYLXXrVu4x7u7A1aucg0NERKomTZdyfU2EKLOeyahDVDVq1MCnn36KI0eOoEGDBjAzM9N5fcyYMXIPWXKZmuZd3ADAzZssboiISNXyKm4yX1eqyNGX7B6catWq5X4wScKVK1cKnJSxKdaDs2wZ8P77+cctXQq8957h5yEiIiqmPvztQ8yLnpdvXHCT4AIPV8m5fnMfnIIUOFLeFauO0vc1ExFRKZBf783LCtqLI+f6bfBOxqmpqYiJiUFaWpqhhyAiIiIyCtkFztOnTzFs2DBYW1ujfv36uHHjBoCMuTdfffWV4gkSERERySW7wJkyZQpOnTqF8PBwWFpaatsDAgKwYcMGRZMr9pYuVTaOiIiohAluEqxonFJkz8GpWrUqNmzYgFdffRV2dnY4deoUqlevjkuXLqFJkyZITk42Vq6KUfReVPrMw+H8GyIiUjF95uEosYrKqHNw7t27B2dn52ztT5480bltQ6mRX/HC4oaIiFRM30nGciYjK0F2gdOsWTPs2LFD+zyzqFm2bBl8fX2Vy6wkESL7MNTSpSxuiIiIiojsjf5mzZqFjh074uzZs0hLS8PChQvx33//ITIyEhEREcbIsWR47z3udUNERFRMyO7BadGiBQ4dOoSnT5/C09MTu3fvRsWKFREZGYmmTZsaI0ciIiIiWWT34ABAgwYNsHLlSqVzISIiIlKEXgWOnJVRBV6VRERERCWGCBGFtopKDr0KHEdHx3xXSAkhIEkSNBqNIokRERFRyZBfkVPYxQ2gZ4Gzf/9+Y+dBREREJVhuRU5RFDeAngWOn5+f7AMHBQXh888/R/ny5WW/l4iIiEqeoipmcmLwzTbzs2bNmhKxqzERERGpj9EKHJl3gCAiIiJSjNEKHCIiIqKiwgKHiIiIVIcFDhEREakOCxwiIiJSHaMVOO+88w53NSYiIqIiYdC9qBITE3Hs2DHcvXsX6enpOq8NHDgQALB48eKCZ0dERERkANkFzm+//Yb+/fvjyZMnsLOz07mFgyRJ2gKHiIiISpfUtFQsilqEywmX4enkiSDvIJiXMS+SXCQhc8OaWrVqoXPnzvjyyy9hbW1trLyMKjk5GQ4ODkhKSuIwGhERkQIm7ZmEeZHzoBH/uyelqWSKYN9gzG43W5FzyLl+y+7BiY2NxZgxY0pscUNERETKmrRnEuYcnpOtXSM02nalihx9yZ5k3KFDB0RFRRkjFyIiIiphUtNSMS9yXp4x8yLnITUttZAyyiC7B+eNN97AxIkTcfbsWTRo0ABmZmY6r3fr1k2x5IiIiKh4WxS1SGdYKicaocGiqEUY9+q4wkkKBhQ47733HgDg888/z/aaJEnQaPL+kERERKQelxMuKxqnFNkFTtZl4URERFR6eTp5KhqnFO5krJTUVGDBAmD06Iw/Uwt3rJGIiKgoBHkHwVQyzTPGVDJFkHdQIWWUwaACJyIiAl27dkWNGjVQs2ZNdOvWDQcOHFA6t5Jj0iTA2hoYPx74/vuMP62tM9qJiIhUzLyMOYJ9g/OMCfYNLvT9cGQXOGvWrEFAQACsra0xZswYjBo1ClZWVvD398e6deuMkWPxNmkSMGcOkHXukUaT0c4ih4iIVC6nJeJyXjcG2Rv91a1bF++//z7Gjx+v0z5v3jwsW7YM586dUzRBY1Bso7/U1IyemrwmVpuaAk+fAuZFs5MjERGRMfVc1RNbrm7JN65HtR4IGxhWoHPJuX7L7sG5cuUKunbtmq29W7duuHr1qtzDlWyLFuVd3AAZry9aVDj5EBERFTJ9ihs5cUqRXeC4u7vjr7/+ytb+119/wd3dXZGkSozLei550zeOiIiIFCF7mfiHH36IMWPG4OTJk2jRogUkScLBgwcRGhqKhQsXGiPH4stTzyVv+sYRERGRImTPwQGArVu3Yu7cudr5NnXr1sXEiRMRGBioeILGwDk4REREylDNHBwAePPNN3Hw4EE8ePAADx48wMGDBw0qbhYvXgwvLy/Y29vD3t4evr6+2LlzZ57viYiIQNOmTWFpaYnq1atjyZIlhnwEZZibA8F5L41DcDCLGyIiUi19i5aCFjdyFelGf5UrV8ZXX32FqKgoREVFoW3btggMDMR///2XY/zVq1fRuXNntGzZEidOnMDHH3+MMWPGICyscL80HbNnAxMnZvTUvMzUNKN9duHePZWIiKiwiZC8B4Pye90Y9BqicnJywoULF1C+fHmULVsWkiTlGpuQkFCghJycnDBnzhwMGzYs22uTJ0/G9u3bdZaijxgxAqdOnUJkZKTe51BsiOplqakZq6UuX86YcxMUxJ4bIiIqVbIOVykxLPUyOddvvSYZz58/H3Z2dtr/zqvAMZRGo8GmTZvw5MkT+Pr65hgTGRmJ9u3b67R16NABP/30E168eJHtzuaZUlJSkJKSon2enJysXOKZzM2BceOUPy4REVEJUdjDUHnRq8AZNGiQ9r8HDx6saAJnzpyBr68vnj9/DltbW2zduhX16tXLMTY+Ph4VK1bUaatYsSLS0tJw//59uLq65vi+WbNmYfr06YrmTURERMWX7Dk40dHROHPmjPb5r7/+iu7du+Pjjz9GqgE3mKxduzZOnjyJI0eO4IMPPsCgQYNw9uzZXOOz9h5ljrDl1as0ZcoUJCUlaR83b96UnScRERGVHLILnOHDh+PChQsAMnY1fvvtt2FtbY1NmzZhkgH3XTI3N0eNGjXg7e2NWbNmoWHDhrnup+Pi4oL4+Hidtrt376JMmTIoV65cruewsLDQrtTKfBAREZF6yS5wLly4gEaNGgEANm3aBD8/P6xbtw6hoaGKrGYSQujMl3mZr68v9uzZo9O2e/dueHt75zr/hoiIiEof2QWOEALp6ekAgL1796Jz584AMm7hcP/+fVnH+vjjj3HgwAFcu3YNZ86cwdSpUxEeHo7+/fsDyBhaGjhwoDZ+xIgRuH79OoKDg3Hu3Dn8/PPP+OmnnzBhwgS5H0N5V68CVlaAiUnGn6XtvlxERFTqrTi6AtJ0SftYcXRFkeUi+1YN3t7emDFjBgICAhAREYHFixcDyNijJusE4PzcuXMHAwYMQFxcHBwcHODl5YVdu3ahXbt2AIC4uDjcuHFDG1+tWjX88ccfGD9+PH744Qe4ubnh22+/Rc+ePeV+DGWZmgL/X/QBAJ4/B6pXzyh28rsZJxERkQpI07PPhR26ayiG7hpafPfBednp06fRv39/3LhxA8HBwQgJCQEAjB49Gg8ePMC6deuMkqiSFN0HJ2txkxWLHCIiUrmcipuslChyFN8H52VeXl46q6gyzZkzB6ZZd/NVu6tX8y5ugIzXr14FqlUrnJyIiIgKkb7DUCuOrsCQ5kOMnM3/GHSzTQBITU3F3bt3tfNxMlWpUkWRxIxJsR4cK6uM4aj8WFoCz54Zfh4iIqJiSp/em0wF7cUxag/OhQsXMGzYMBw+fFinXQgBSZKgKU3DMbms9jI4joiIiBQhu8AZMmQIypQpg99//x2urq5GuW1DiWFhoV8PjoWF8XMhIiIiLdkFzsmTJ/HPP/+gTp06xsinZDl7NmO1lD5xREREKvRzx58xdNdQveIKk+x9cOrVqyd7vxvVqlYtY5VUXkxMOMGYiIhUS9+Jw4U5wRgwoMD5+uuvMWnSJISHh+PBgwdITk7WeZQ6Gk3uRQ6XiBMRUSmQ3+ThErEPjsn/X8xzuullSZlkrOg+OJmuXgXq1cuYUGxhkTEsxZ4bIiIqRVYcXaEzXPVzx58V7bmRc/2WXeBERETk+bqfn5+cwxUJoxQ4REREZFRGXSZeEgoYIiIiKt1kz8EBgAMHDuCdd95BixYtEBsbCwBYvXo1Dh48qGhyRERERIaQXeCEhYWhQ4cOsLKyQnR0NFL+fxO7R48e4csvv1Q8QSIiIiK5ZBc4M2bMwJIlS7Bs2TKYmZlp21u0aIHo6GhFkyMiIiIyhOwCJyYmBq1atcrWbm9vj8TERCVyIiIiIioQ2QWOq6srLl26lK394MGDqK7Prr5qdewYIEn/exw7VtQZERERFar+v/SHNF3SPvr/0r/IcpFd4AwfPhxjx47F0aNHIUkSbt++jbVr12LChAkICgoyRo7FnyQBzZvrtjVvntFORERUCkjTJay7sE6nbd2FdbLuNq4k2cvEJ02ahKSkJLRp0wbPnz9Hq1atYGFhgQkTJmDUqFHGyLF4y6+IkSRA3lZDREREJUp+RYw0XSr03YwNWiY+c+ZM3L9/H8eOHcORI0dw7949fPHFF0rnVvzpOwzF4SoiIlIpfYehCnu4SnaBM3ToUDx69AjW1tbw9vaGj48PbG1t8eTJEwwdmv/dRFUl67BUQeOIiIhKmKzDUgWNU4rsAmflypV49uxZtvZnz55h1apViiRFREREVBB6z8FJTk6GEAJCCDx69AiWlpba1zQaDf744w84OzsbJUkiIiIiOfQucBwdHSFJEiRJQq1atbK9LkkSpk+frmhyxd7Ro/oNPx09avxciIiIikC/Wv30Gn7qV6tfIWTzP3rfTTwiIgJCCLRt2xZhYWFwcnLSvmZubo6qVavCzc3NaIkqSdG7ieuzFJyrqIiISMX0WQquxCoqo9xNPPMu4levXoW7uztMTAxagKU+QuRd5LC4ISIilRMhIs8ip7CXiAMGTDJevnw5cur0SUpKQt++fRVJqsQRIvsw1NGjLG6IiKjUECEi2zBUv1r9iqS4AWQMUWWqWrUqXF1dsXbtWnh6egIAwsPDMXDgQFSqVAmRkZFGSVRJig5RERERUaGQc/2W3YNz+vRpeHh4oFGjRli2bBkmTpyI9u3bY/DgwTh48KDBSRMREREpRfatGhwcHLB+/XpMnToVw4cPR5kyZbBz5074+/sbIz8iIiIi2QyaKfzdd99h/vz56Nu3L6pXr44xY8bg1KlTSudGREREZBDZBU6nTp0wffp0rFq1CmvXrsWJEyfQqlUrvPrqq5g9e7YxciQiIiKSRXaBk5aWhtOnT6NXr14AACsrKyxevBibN2/G/PnzFU+QiIiISC7Zq6jycv/+fZQvX16pwxkNV1ERERGVPEZdRQUABw4cwDvvvANfX1/ExsYCAFavXo3z588bcjgiIiIiRckucMLCwtChQwdYWVnhxIkTSElJAQA8evQIX375peIJlhiSlP1BRERUisyPmA9puqR9zI8ouqkrsoeoGjdujPHjx2PgwIGws7PDqVOnUL16dZw8eRIdO3ZEfHy8sXJVjOJDVLxVAxERlXKFcasGow5RxcTEoFWrVtna7e3tkZiYKPdwJV9+PTXsySEiIpXL72ab+tyMU2myCxxXV1dcunQpW/vBgwdRvXp1RZIqMfQtXljkEBGRSuk7DFXYw1WyC5zhw4dj7NixOHr0KCRJwu3bt7F27VpMmDABQUFBxsiRiIiIiqng8GBF45Qi+1YNkyZNQlJSEtq0aYPnz5+jVatWsLCwwIQJEzBq1Chj5EhEREQki8H74Dx9+hRnz55Feno66tWrB1tbW53Xb926BTc3N5iYGLQS3agUm2QsZ+iJk42JiEiF5MyvKehkY6PvgwMA1tbW8Pb2ho+PT7biBgDq1auHa9euGXp4IiIiKgHmtZ6naJxSjNa9ouAGycWXvp+xNHwXRERUKo33G69onFKK3/hRSZNf8cLihoiIVC6/oSel9sGRgwWOEnIrYljcEBFRKSFCRLZhqHmt5xVJcQMYsIqKcsFihoiISrnxfuMLfSgqN0brwZG4uR0REREVEU4yJiIiItWRVeCkpaWhTJky+Pfff/ONPXv2LKpWrWpwYkRERESGkjUHp0yZMqhatSo0Gk2+se7u7gYnRURERFQQsoeoPvnkE0yZMgUJCQnGyIeIiIiowGSvovr2229x6dIluLm5oWrVqrCxsdF5PTo6WrHkiIiIiAwhu8Dp3r27EdIgIiIiUo7BN9ssyRS72SYREREVGqPfbDMxMRHLly/XmYsTHR2N2NhYQw5HREREpCjZQ1SnT59GQEAAHBwccO3aNbz33ntwcnLC1q1bcf36daxatcoYeRIRERHpTXYPTnBwMAYPHoyLFy/C0tJS296pUyf8/fffiiZXoty7B1SrBtjaZvx5715RZ0RERFSopOlStkdRkV3gHD9+HMOHD8/WXqlSJcTHxyuSVInj6Ag4OwPXrgFPnmT86eyc0U5ERFQK5FbMFFWRI7vAsbS0RHJycrb2mJgYVKhQQZGkShRHRyApKefXkpJY5BARkerlV8QURZEju8AJDAzE559/jhcvXgDIuKnmjRs38NFHH6Fnz56KJ1is3buXe3GTKSmJw1VERKRa+hYvhV3kyC5wvvnmG9y7dw/Ozs549uwZ/Pz8UKNGDdjZ2WHmzJnGyLH48vFRNo6IiIgUIXsVlb29PQ4ePIh9+/YhOjoa6enpaNKkCQICAoyRX/Gmb88Me3CIiIgKlewC59q1a/Dw8EDbtm3Rtm1bY+RUclSokDGpWJ84IiIiKjSyh6iqV6+O119/HT/++CNvuHnsmLJxREREpAjZBU5UVBR8fX0xY8YMuLm5ITAwEJs2bUJKSoox8iveKlQAHBzyjnFwYA8OERGplgjR745P+sYpRXaB06RJE8yZMwc3btzAzp074ezsjOHDh8PZ2RlDhw41Ro7FW2Ji7kWOg0PG60RERCqWX/FS2MUNoNDNNqOjozFs2DCcPn0aGo1GibyMyig327x3L2O11L17GT02x46x54aIiEqVnJaCK1ncGP1mmwBw8+ZNzJ49G40aNUKzZs1gY2OD77//XtYxZs2ahWbNmsHOzg7Ozs7o3r07YmJi8nxPeHg4JEnK9jh//ryhH0UZFSoAV68Cjx9n/MnihoiIShkRIrI9iorsVVRLly7F2rVrcejQIdSuXRv9+/fHtm3b4OHhIfvkERERGDlyJJo1a4a0tDRMnToV7du3x9mzZ2FjY5Pne2NiYnSqt1K5izIRERHlSPYQlbu7O/r06YP+/fujUaNGiiaTuYFgREQEWrVqlWNMeHg42rRpg4cPH8LRwNsgGGWIioiIiIzKaENUaWlpGDJkCMaMGaN4cQMASf9/2wMnJ6d8Yxs3bgxXV1f4+/tj//79ecampKQgOTlZ50FERETqJavAKVOmDObPn2+UicRCCAQHB+P111/HK6+8kmucq6srli5dirCwMGzZsgW1a9eGv78//v7771zfM2vWLDg4OGgf7u7uiudPRERExYfsIaru3buje/fuGDx4sKKJjBw5Ejt27MDBgwdRuXJlWe/t2rUrJEnC9u3bc3w9JSVFZ5+e5ORkuLu7c4iKiIioBJEzRCV7knGnTp0wZcoU/Pvvv2jatGm2ycDdunWTe0iMHj0a27dvx99//y27uAGAV199FWvWrMn1dQsLC1hYWMg+LhEREZVMsntwTExyH9WSJEnW8JUQAqNHj8bWrVsRHh6OmjVryklFq1evXkhISMC+ffv0iuckYyIiopLHqD046enpBieW1ciRI7Fu3Tr8+uuvsLOzQ3x8PADAwcEBVlZWAIApU6YgNjYWq1atAgAsWLAAHh4eqF+/PlJTU7FmzRqEhYUhLCxMsbyIiIioZJNd4Lzs+fPnsLS0NPj9ixcvBgC0bt1ap33FihXaOT5xcXG4ceOG9rXU1FRMmDABsbGxsLKyQv369bFjxw507tzZ4DyIiIhIXWQPUWk0Gnz55ZdYsmQJ7ty5gwsXLqB69er49NNP4eHhgWHDhhkrV8VwiIqIiKjkMeqtGmbOnInQ0FDMnj0b5ubm2vYGDRpg+fLl8rMlIiIiUpjsAmfVqlVYunQp+vfvD1NTU227l5dX0d8PioiIiAgGFDixsbGoUaNGtvb09HS8ePFCkaSIiIiICkJ2gVO/fn0cOHAgW/umTZvQuHFjRZIiIiIiKgjZq6hCQkIwYMAAxMbGIj09HVu2bEFMTAxWrVqF33//3Rg5lgwaDXDgABAXB7i6Ai1bAi8N4REREandzL0z8cmhT7TPZ7w2A1MDphZJLrJXUQHAn3/+iS+//BL//PMP0tPT0aRJE3z22Wdo3769MXJUnOKrqLZsAcaOBW7d+l9b5crAwoVAjx4FPz4REVExJ02Xcn1NhMguNXIk5/ptUIFT0ila4GzZAvTqBWT9GqX//0Fv3swih4iIVC2v4iaTEkWOUZeJ37x5E7de6qk4duwYxo0bh6VLl8rPtKTTaDJ6bnKqETPbxo3LiCMiIlKhmXtnKhqnFNkFTr9+/bB//34AQHx8PAICAnDs2DF8/PHH+PzzzxVPsFg7cEB3WCorIYCbNzPiiIiIVOjlOTdKxClFdoHz77//wsfHBwCwceNGNGjQAIcPH8a6desQGhqqdH7FW1ycsnFERESkCNkFzosXL2BhYQEA2Lt3L7p16wYAqFOnDuJK24Xc1VXZOCIiIlKEQfvgLFmyBAcOHMCePXvQsWNHAMDt27dRrlw5xRMs1lq2zFgtJeUyuUqSAHf3jDgiIiIVmvHaDEXjlCK7wPn666/x448/onXr1ujbty8aNmwIANi+fbt26KrUMDXNWAoOZC9yMp8vWMD9cIiISLX03eemsPfDMWiZuEajQXJyMsqWLattu3btGqytreHs7KxogsZQKPvguLtnFDdcIk5ERKUA98EpBhQvcADuZExERKWesXcyZoGTD6MUOERERGRURt3oj4iIiKi4Y4FDREREqqNXgePk5IT79+8DAIYOHYpHjx4ZNSkiIiKigtCrwElNTUVycjIAYOXKlXj+/LlRkyIiIiIqiDL6BPn6+qJ79+5o2rQphBAYM2YMrKyscoz9+eefFU2QiIiISC69Cpw1a9Zg/vz5uHz5MiRJQlJSEntxiIiIqNiSvUy8WrVqiIqKKtG3ZeAycSIiopJHzvVbrx6cl129etXgxIiIiIgKg0HLxCMiItC1a1fUqFEDNWvWRLdu3XDgwAGlcyMiIiIyiOwCZ82aNQgICIC1tTXGjBmDUaNGwcrKCv7+/li3bp0xciQiIiKSRfYcnLp16+L999/H+PHjddrnzZuHZcuW4dy5c4omaAycg0NERFTyGPVWDVeuXEHXrl2ztXfr1o3zc4iIiKhYkF3guLu746+//srW/tdff8Hd3V2RpIiIiIgKQvYqqg8//BBjxozByZMn0aJFC0iShIMHDyI0NBQLFy40Ro5EREREssgucD744AO4uLhg7ty52LhxI4CMeTkbNmxAYGCg4gkSERERySV7krEacJIxERFRyWPUScZERERExR0LHCIiIlIdFjhERESkOrInGVMu6tYFzp//3/M6dYASsOkhERGRUqTpUrY2EVI0U33Zg6MESdItboCM51L2HzQREZEa5VTc5NVubLJ7cIKDg3NslyQJlpaWqFGjBgIDA+Hk5FTg5EqE/IoYSQJK30I1IiIqRfIrYqTpUqH35MheJt6mTRtER0dDo9Ggdu3aEELg4sWLMDU1RZ06dRATE6Pd/K9evXrGyrtAFFsmnnVYKjccriIiIpWS00NT0CLHqMvEAwMDERAQgNu3b+Off/5BdHQ0YmNj0a5dO/Tt2xexsbFo1apVtptxqpI+xY2cOCIiIlKE7B6cSpUqYc+ePdl6Z/777z+0b98esbGxiI6ORvv27XH//n1Fk1WKYj04cubYcJiKiIhUSDU9OElJSbh792629nv37iE5ORkA4OjoiNTUVLmHJiIiIlKEQUNUQ4cOxdatW3Hr1i3ExsZi69atGDZsGLp37w4AOHbsGGrVqqV0rsVPnTrKxhEREZEiZA9RPX78GOPHj8eqVauQlpYGAChTpgwGDRqE+fPnw8bGBidPngQANGrUSOl8FaHovaj0Gabi8BQREamYPsNUSqyiknP9Nvhmm48fP8aVK1cghICnpydsbW0NSrYoKH6zzbyKHBY3RERUCuRV5Ci1RLxQbrZpa2sLJycnlC9fvkQVN0YhRPZhqDp1WNwQEVGpkVsRU1Q7Gcve6C89PR0zZszA3Llz8fjxYwCAnZ0dPvzwQ0ydOhUmJqV0c2Tuc0NERKVcURUzOZFd4EydOhU//fQTvvrqK7z22msQQuDQoUOYNm0anj9/jpkzZxojTyIiIiK9yZ6D4+bmhiVLlqBbt2467b/++iuCgoIQGxuraILGoPgcHCIiIjI6o87BSUhIQJ0clj3XqVMHCQkJcg9HREREpDjZBU7Dhg3x/fffZ2v//vvv0bBhQ0WSIiIiIioI2XNwZs+ejTfeeAN79+6Fr68vJEnC4cOHcfPmTfzxxx/GyJGIiIhIFtk9OH5+frhw4QLefPNNJCYmIiEhAT169EBMTAxatmxpjByJiIiIZDF4o7+sbt68iZCQEPz8889KHM6oOMmYiIio5CmUjf6ySkhIwMqVK5U6HBEREZHBSumufERERKRmLHCIiIhIdVjgEBERkerovUy8R48eeb6emJhY0FyIiIiIFKF3gePg4JDv6wMHDixwQkREREQFpXeBs2LFCmPmQURERKQYzsEhIiIi1WGBQ0RERKrDAoeIiIhUhwUOERERqQ4LHCIiIlKdIi1wZs2ahWbNmsHOzg7Ozs7o3r07YmJi8n1fREQEmjZtCktLS1SvXh1LliwphGyJiIiopCjSAiciIgIjR47EkSNHsGfPHqSlpaF9+/Z48uRJru+5evUqOnfujJYtW+LEiRP4+OOPMWbMGISFhRVi5kRERFScSUIIUdRJZLp37x6cnZ0RERGBVq1a5RgzefJkbN++HefOndO2jRgxAqdOnUJkZKRe55Fzu3UiIiIqHuRcv4vVHJykpCQAgJOTU64xkZGRaN++vU5bhw4dEBUVhRcvXhg1PyIiIioZ9N7J2NiEEAgODsbrr7+OV155Jde4+Ph4VKxYUaetYsWKSEtLw/379+Hq6prtPSkpKUhJSdE+T05OVi5xIiIiKnaKTQ/OqFGjcPr0afzyyy/5xkqSpPM8c5Qta3umWbNmwcHBQftwd3cveMJERERUbBWLAmf06NHYvn079u/fj8qVK+cZ6+Ligvj4eJ22u3fvokyZMihXrlyO75kyZQqSkpK0j5s3byqWOxERERU/RTpEJYTA6NGjsXXrVoSHh6NatWr5vsfX1xe//fabTtvu3bvh7e0NMzOzHN9jYWEBCwsLRXImIiKi4q9Ie3BGjhyJNWvWYN26dbCzs0N8fDzi4+Px7NkzbcyUKVMwcOBA7fMRI0bg+vXrCA4Oxrlz5/Dzzz/jp59+woQJE4riIxAREVExVKQFzuLFi5GUlITWrVvD1dVV+9iwYYM2Ji4uDjdu3NA+r1atGv744w+Eh4ejUaNG+OKLL/Dtt9+iZ8+eRfERiIiIqBgqVvvgFBbug0NERFTylNh9cIiIiIiUwAKHiIiIVIcFDhEREakOCxwiIiJSHRY4REREpDoscIiIiEh1WOAQERGR6rDAISIiItVhgUNERESqwwKHiIiIVIcFDhEREakOCxwiIiJSHRY4REREpDoscIiIiEh1WOAQERGR6rDAISIiItVhgUNERESqwwKHiIiIVIcFDhEREakOCxwiIiJSHRY4REREpDoscIiIiEh1WOAQERGR6rDAISIiItVhgUNERESqwwKHiIiIVIcFDhEREakOCxwiIiJSHRY4REREpDoscIiIiEh1WOAQERGR6rDAISIiItVhgUNERESqwwKHiIiIVIcFDhEREakOCxwiIiJSHRY4REREpDoscIiIiEh1WOAQERGR6rDAISIiItVhgUNERESqwwKHiIiIVIcFDhEREakOCxwiIiJSHRY4REREpDoscIiIiEh1WOAQERGR6rDAISIiItVhgUNERESqU6aoEyAiIiJ1cJzuiCQkaZ87wAGJIYlFkgt7cIiIiKjApOmSTnEDAElIgjRdKpJ8WOAQERFRgeRXxBRFkcMCh4iIiAzmON1R0TilsMAhIiIig2UdliponFJY4BAREZHqsMAhIiIi1WGBQ0RERAZzgIOicUphgUNEREQG03efm8LeD4cFDhERERWICBEFet0YWOAQERFRgYkQkW0YygEORVLcALxVAxERESmkqG7LkBP24BAREZHqsMAhIiIi1WGBQ0RERKrDAoeIiIhUhwUOERERqQ4LHCIiIlIdFjhERESkOixwiIiISHVY4BAREZHqlMqdjIXI2DY6OTm5iDMhIiIifWVetzOv43kplQXOo0ePAADu7u5FnAkRERHJ9ejRIzg4OOQZIwl9yiCVSU9Px+3bt2FnZwdJkhQ7bnJyMtzd3XHz5k3Y29srdlwiIqKSwpjXQiEEHj16BDc3N5iY5D3LplT24JiYmKBy5cpGO769vT0LHCIiKtWMdS3Mr+cmEycZExERkeqwwCEiIiLVYYGjIAsLC4SEhMDCwqKoUyEiIioSxeVaWConGRMREZG6sQeHiIiIVIcFDhEREakOCxwiIiJSHRY4ehg8eDC6d++ufd66dWuMGzdOr/eGh4dDkiQkJiYaJTciIiJDyLmWGSrr9bMwlcqN/gpqy5YtMDMz0yu2RYsWiIuL03tjIiIiIrVYuHChXveNMgYWOAZwcnLSO9bc3BwuLi5GzIaIiKh4Ksp/3KtuiKp169YYPXo0xo0bh7Jly6JixYpYunQpnjx5giFDhsDOzg6enp7YuXMnAECj0WDYsGGoVq0arKysULt2bSxcuDDfc7zcrZeSkoJJkybB3d0dFhYWqFmzJn766ScAOQ9RhYWFoX79+rCwsICHhwfmzp2rc3xJkrBt2zadNkdHR4SGhgIAUlNTMWrUKLi6usLS0hIeHh6YNWuWYV8YERGVWmlpaRg1ahQcHR1Rrlw5fPLJJ9oel9TUVEyaNAmVKlWCjY0NmjdvjvDwcO17Q0ND4ejoiD///BN169aFra0tOnbsiLi4OG1M1iGqR48eoX///rCxsYGrqyvmz5+f7Zrq4eGBL7/8EkOHDoWdnR2qVKmCpUuXyv5sqitwAGDlypUoX748jh07htGjR+ODDz5A79690aJFC0RHR6NDhw4YMGAAnj59ivT0dFSuXBkbN27E2bNn8dlnn+Hjjz/Gxo0b9T7fwIEDsX79enz77bc4d+4clixZAltb2xxj//nnH7z11lvo06cPzpw5g2nTpuHTTz/VFi/6+Pbbb7F9+3Zs3LgRMTExWLNmDTw8PPR+PxEREZBxvSxTpgyOHj2Kb7/9FvPnz8fy5csBAEOGDMGhQ4ewfv16nD59Gr1790bHjh1x8eJF7fufPn2Kb775BqtXr8bff/+NGzduYMKECbmeLzg4GIcOHcL27duxZ88eHDhwANHR0dni5s6dC29vb5w4cQJBQUH44IMPcP78eXkfTqiMn5+feP3117XP09LShI2NjRgwYIC2LS4uTgAQkZGROR4jKChI9OzZU/t80KBBIjAwUOccY8eOFUIIERMTIwCIPXv25His/fv3CwDi4cOHQggh+vXrJ9q1a6cTM3HiRFGvXj3tcwBi69atOjEODg5ixYoVQgghRo8eLdq2bSvS09NzPCcREVF+/Pz8RN26dXWuJZMnTxZ169YVly5dEpIkidjYWJ33+Pv7iylTpgghhFixYoUAIC5duqR9/YcffhAVK1bUPn/5+pmcnCzMzMzEpk2btK8nJiYKa2tr7TVVCCGqVq0q3nnnHe3z9PR04ezsLBYvXizr86myB8fLy0v736ampihXrhwaNGigbatYsSIA4O7duwCAJUuWwNvbGxUqVICtrS2WLVuGGzdu6HWukydPwtTUFH5+fnrFnzt3Dq+99ppO22uvvYaLFy9Co9HodYzBgwfj5MmTqF27NsaMGYPdu3fr9T4iIqKXvfrqq5AkSfvc19cXFy9eRFRUFIQQqFWrFmxtbbWPiIgIXL58WRtvbW0NT09P7XNXV1fttTWrK1eu4MWLF/Dx8dG2OTg4oHbt2tliX76OS5IEFxeXXI+bG1VOMs66wkmSJJ22zB9meno6Nm7ciPHjx2Pu3Lnw9fWFnZ0d5syZg6NHj+p1LisrK1m5CSF0fpky27Lmm7XtxYsX2v9u0qQJrl69ip07d2Lv3r146623EBAQgM2bN8vKhYiIKDempqb4559/YGpqqtP+8hSMnK63Wa9fmTLb87sG5nbc9PR0/ZOHSgscOQ4cOIAWLVogKChI2/ZydZqfBg0aID09HREREQgICMg3vl69ejh48KBO2+HDh1GrVi3tL1GFChV0JmldvHgRT58+1XmPvb093n77bbz99tvo1asXOnbsiISEBFkrvIiIqHQ7cuRItuc1a9ZE48aNodFocPfuXbRs2VKRc3l6esLMzAzHjh2Du7s7ACA5ORkXL17UexREjlJf4NSoUQOrVq3Cn3/+iWrVqmH16tU4fvw4qlWrptf7PTw8MGjQIAwdOhTffvstGjZsiOvXr+Pu3bt46623ssV/+OGHaNasGb744gu8/fbbiIyMxPfff49FixZpY9q2bYvvv/8er776KtLT0zF58mSdanb+/PlwdXVFo0aNYGJigk2bNsHFxQWOjo4F/j6IiKj0uHnzJoKDgzF8+HBER0fju+++w9y5c1GrVi30798fAwcOxNy5c9G4cWPcv38f+/btQ4MGDdC5c2fZ57Kzs8OgQYMwceJEODk5wdnZGSEhITAxMcnWq6MEVc7BkWPEiBHo0aMH3n77bTRv3hwPHjzQ6c3Rx+LFi9GrVy8EBQWhTp06eO+99/DkyZMcY5s0aYKNGzdi/fr1eOWVV/DZZ5/h888/x+DBg7Uxc+fOhbu7O1q1aoV+/fphwoQJsLa21r5ua2uLr7/+Gt7e3mjWrBmuXbuGP/74AyYmpf7HSUREMgwcOBDPnj2Dj48PRo4cidGjR+P9998HAKxYsQIDBw7Ehx9+iNq1a6Nbt244evSotvfFEPPmzYOvry+6dOmCgIAAvPbaa6hbty4sLS2V+khakshtsIyIiIjIiJ48eYJKlSph7ty5GDZsmKLHLvVDVERERFQ4Tpw4gfPnz8PHxwdJSUn4/PPPAQCBgYGKn4sFDhERERWab775BjExMTA3N0fTpk1x4MABlC9fXvHzcIiKiIiIVIezUomIiEh1WOAQERGR6rDAISIiItVhgUNERESqwwKHiIxKkiRs27atqNMwyLRp09CoUaMCHePatWuQJAknT55UJCci0g8LHCIyWHx8PEaPHo3q1avDwsIC7u7u6Nq1K/7666+iTg0A0Lp1a4wbN66o0yCiIsB9cIjIINeuXcNrr70GR0dHzJ49G15eXnjx4gX+/PNPjBw5EufPny/qFImoFGMPDhEZJCgoCJIk4dixY+jVqxdq1aqF+vXrIzg4ONsdil82efJk1KpVC9bW1qhevTo+/fRTvHjxQvv6qVOn0KZNG9jZ2cHe3h5NmzZFVFQUAOD69evo2rUrypYtCxsbG9SvXx9//PGHwZ8hv1wy/fjjj3B3d4e1tTV69+6NxMREnddXrFihvZ9OnTp1dG6eS0RFgz04RCRbQkICdu3ahZkzZ8LGxibb63nd2d7Ozg6hoaFwc3PDmTNn8N5778HOzg6TJk0CAPTv3x+NGzfG4sWLYWpqipMnT8LMzAwAMHLkSKSmpuLvv/+GjY0Nzp49C1tbW4M/R365AMClS5ewceNG/Pbbb0hOTsawYcMwcuRIrF27FgCwbNkyhISE4Pvvv0fjxo1x4sQJvPfee7CxscGgQYMMzo2ICkgQEcl09OhRAUBs2bIl31gAYuvWrbm+Pnv2bNG0aVPtczs7OxEaGppjbIMGDcS0adP0ztPPz0+MHTtW7/isuYSEhAhTU1Nx8+ZNbdvOnTuFiYmJiIuLE0II4e7uLtatW6dznC+++EL4+voKIYS4evWqACBOnDihdx5EVHDswSEi2cT/3+FFkiTZ7928eTMWLFiAS5cu4fHjx0hLS4O9vb329eDgYLz77rtYvXo1AgIC0Lt3b3h6egIAxowZgw8++AC7d+9GQEAAevbsCS8vL4M/R365AECVKlVQuXJl7XNfX1+kp6cjJiYGpqamuHnzJoYNG4b33ntPG5OWlgYHBweD8yKiguMcHCKSrWbNmpAkCefOnZP1viNHjqBPnz7o1KkTfv/9d5w4cQJTp05FamqqNmbatGn477//8MYbb2Dfvn2oV68etm7dCgB49913ceXKFQwYMABnzpyBt7c3vvvuO4M+gz655CSzqJMkCenp6QAyhqlOnjypffz77795zkMiIuNjgUNEsjk5OaFDhw744Ycf8OTJk2yvZ52Em+nQoUOoWrUqpk6dCm9vb9SsWRPXr1/PFlerVi2MHz8eu3fvRo8ePbBixQrta+7u7hgxYgS2bNmCDz/8EMuWLTPoM+iby40bN3D79m3t88jISJiYmKBWrVqoWLEiKlWqhCtXrqBGjRo6j2rVqhmUFxEpg0NURGSQRYsWoUWLFvDx8cHnn38OLy8vpKWlYc+ePVi8eHGOvTs1atTAjRs3sH79ejRr1gw7duzQ9s4AwLNnzzBx4kT06tUL1apVw61bt3D8+HH07NkTADBu3Dh06tQJtWrVwsOHD7Fv3z7UrVs3zzzv3buXbZM9FxeXfHPJZGlpiUGDBuGbb75BcnIyxowZg7feegsuLi4AMnqcxowZA3t7e3Tq1AkpKSmIiorCw4cPERwcLPdrJSKlFPUkICIquW7fvi1GjhwpqlatKszNzUWlSpVEt27dxP79+7UxyDLJeOLEiaJcuXLC1tZWvP3222L+/PnCwcFBCCFESkqK6NOnj3B3dxfm5ubCzc1NjBo1Sjx79kwIIcSoUaOEp6ensLCwEBUqVBADBgwQ9+/fzzU/Pz8/ASDbIyQkJN9chMiYZNywYUOxaNEi4ebmJiwtLUWPHj1EQkKCznnWrl0rGjVqJMzNzUXZsmVFq1attBOwOcmYqGhIQvz/bEEiIiIileAcHCIiIlIdFjhERESkOixwiIiISHVY4BAREZHqsMAhIiIi1WGBQ0RERKrDAoeIiIhUhwUOERERqQ4LHCIiIlIdFjhERESkOixwiIiISHVY4BAREZHq/B/2nIMgAQhJxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data[train_data.label == 'malicious']['label'], np.log(train_data[train_data.label == 'malicious']['server_extension_len']), 'ro');\n",
    "### Log was taken to capture the small differences in client_extension_len\n",
    "plt.plot(train_data[train_data.label == 'benign']['label'], np.log(train_data[train_data.label == 'benign']['server_extension_len']), 'go');\n",
    "plt.xlabel('Class Label');\n",
    "plt.ylabel('Log of server_extension_len');\n",
    "plt.title('Log of server_extension_len wrt Class Labels');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "522a2846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grpd_df = train_data.groupby(['client_extensions', 'label']).size().reset_index(name = 'count_of_labels_per_attribute_grp')\n",
    "malicious = grpd_df[grpd_df.label == 'malicious']\n",
    "benign = grpd_df[grpd_df.label == 'benign']\n",
    "\n",
    "mal_benign_df = pd.merge(malicious, benign, on = 'client_extensions', how = 'outer', suffixes = ('_malicious', '_benign')).fillna(0).drop(['label_malicious', 'label_benign'], axis = 1)\n",
    "\n",
    "malicious_count = mal_benign_df['count_of_labels_per_attribute_grp_malicious'].apply(lambda x: np.log(x+1)).values\n",
    "benign_count = mal_benign_df['count_of_labels_per_attribute_grp_benign'].apply(lambda x: np.log(x+1)).values\n",
    "\n",
    "X_axis = np.arange(len(mal_benign_df['client_extensions']))\n",
    "\n",
    "plt.bar(X_axis - 0.2, malicious_count, 0.4, label = 'Malicious', color = 'red')\n",
    "plt.bar(X_axis + 0.2, benign_count, 0.4, label = 'Benign')\n",
    "\n",
    "plt.xticks(X_axis, mal_benign_df['client_extensions'], rotation = 90)\n",
    "plt.xlabel(\"Client Extensions\")\n",
    "plt.ylabel(\"Count of unique connections\")\n",
    "plt.title(\"Number of connections in each group\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "adf7114c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QnJxsMK1JkyaIiop65dM02XFyckKFChWwatUqvHjxQp3+5MkT/Pnnn5r7qFy5MlasWGHwHUhJScGCBQvg7++P0NBQAFCPgr68f1mzZk2GfZ88eVI9Qqu3aNEiuLi4sD5QLsYjM/TaTJgwAeXLl8edO3dQokQJdbqiKOjQoQPmzp2LQoUKoUyZMti/fz8WLVpktlju3LmDt956Cz179kRsbCyGDx8Oe3t7DBkyRG3z448/okmTJmjUqBG6dOmC/Pnz48GDBzh9+jQOHz6MZcuWvdK8+/fvj19//RUREREYNWoUgoKCsHbtWvzwww/o3bu3uhM2Ro0aNdCxY0eMGTMGt2/fRrNmzWBnZ4cjR47A0dERH3/8MYoUKYIPPvgA06dPh06nQ5MmTXD58mUMGzYMAQEBZi3I5+zsjOnTp6Nz58548OABWrdujXz58uHu3bs4duwY7t69qyZiU6ZMQfXq1VG5cmUMHjwYhQsXxu3bt7F69Wr8+OOPcHFxUStIz549Gy4uLrC3t0eBAgUy/M+5QYMGaNSoEb744gvExcUhPDwcx48fx/Dhw1G2bFl07NjR6OWpXLkymjVrhtKlS8PDwwOnT5/Gb7/9hqpVq8LR0fHfDZYRgoODMWrUKAwdOhSXLl1C48aN4eHhgdu3b2P//v3qESRXV1fUrFkTkyZNQt68eREcHIzt27fj559/hru7u0Gfo0aNwrp161CzZk18+eWXKFWqFB49eoT169djwIABKFq06L+Oe9SoUYiIiECjRo3w6aefIjk5GZMmTYKzs3O6I0WZGTduHBo0aIA6depg4MCBsLW1xQ8//IATJ05g8eLF6tGcpk2bIk+ePOjevTtGjRoFa2tr/PLLL7h27VqG/fr5+aFFixYYMWIEfH19sWDBAmzatAkTJkx4reuWjJSTVx/Tf1Pau5le9t577wkAg7uZRFJvRe7Ro4d4e3uLk5OTNG/eXC5fvpzp3UxpbyUVSb0jxMnJKd38Xr5zSn8Xxm+//SaffPKJeHl5iZ2dndSoUUMOHjyY7vPHjh2Ttm3bSr58+cTGxkZ8fHykbt26MmvWLE3Lm5krV67Ie++9J56enmJjYyNFihSRSZMmqXdI6Wm9m0kk9c6gqVOnSsmSJcXW1lbc3NykatWqsmbNGoM2EyZMkNDQULGxsZG8efNKhw4d5Nq1awZ9vTxuei/fKaQfz2XLlhm0y+wOte3bt0tERITkyZNHbGxsJH/+/BIREZHu86dOnZI2bdqIp6en2NraSmBgoHTp0sXgNupp06ZJgQIFxMrKymBeL8coknpH0hdffCFBQUFiY2Mjvr6+0rt3b3n48KFBu8zGu1atWlKrVi319eDBg6VChQri4eEhdnZ2UrBgQenfv7/cu3cv3WfTyuxupoy+u5ndhZORVatWSZ06dcTV1VXs7OwkKChIWrdubXDL+/Xr1+Wdd94RDw8PcXFxkcaNG8uJEyckKChIOnfubNDftWvXpFu3buLj4yM2Njbi5+cnbdu2ldu3bxssh9b1npGVK1dKqVKl1PU7fvx4+eSTT8TDw8OgHQD56KOPMuzj77//lrp164qTk5M4ODhIlSpVDL7vevv375dq1aqJk5OT5M+fX4YPHy4//fRThnczRUREyPLly6VEiRJia2srwcHBMmXKlGyXh3KWIpLmWDsREVEOSExMRFhYGPLnz4+NGzfmSAzBwcEoWbKk5tNdlHvwNBMREb123bt3R4MGDeDr64tbt25h1qxZOH36NL799tucDo0sEJMZIiJ67R4/foyBAwfi7t27sLGxQbly5fDXX3+hfv36OR0aWSCeZiIiIiKLxluziYiIyKIxmSEiIiKL9p+/ZiYlJQU3b96Ei4uL5gqtRERElLNEBI8fP4afn5/Bozky8p9PZm7evImAgICcDoOIiIhewbVr17J9Xth/PpnRl5y/du0aXF1dczgaIiIi0iIuLg4BAQGaHh3zn09m9KeWXF1dmcwQERFZGC2XiPACYCIiIrJoTGaIiIjIojGZISIiIov2n79mRqvk5GQkJibmdBhkJBsbG1hZWeV0GERElIPe+GRGRHDr1i08evQop0OhV+Tu7g4fHx/WESIiekO98cmMPpHJly8fHB0d+QfRgogInj59ijt37gAAfH19czgiIiLKCW90MpOcnKwmMp6enjkdDr0CBwcHAMCdO3eQL18+nnIiInoDvdEXAOuvkXF0dMzhSOjf0K8/XvNERPRmeqOTGT2eWrJsXH9ERG82JjNERERk0ZjMEBERkUVjMpMZRXl9Pzns8uXLUBQFR48eBQBs27YNiqJovl29du3a6Nevn9niIyIiygqTGQvVpUsXKIqCXr16pXuvT58+UBQFXbp0eaW+q1WrhpiYGLi5uWlqv2LFCowePfqV5kVERPRvMZmxYAEBAViyZAmePXumTnv+/DkWL16MwMDAV+7X1tbWqCJ0efLk0fSIdiIiInNgMmPBypUrh8DAQKxYsUKdtmLFCgQEBKBs2bLqtPXr16N69epwd3eHp6cnmjVrhosXL2bab0anmXbt2oVatWrB0dERHh4eaNSoER4+fAgg/Wmmhw8folOnTvDw8ICjoyOaNGmC8+fPq++PGDECYWFhBvOcNm0agoODDWKoVKkSnJyc4O7ujvDwcFy5csXIESIiojcBkxkL17VrV8ybN099PXfuXHTr1s2gTXx8PAYMGIADBw5gy5Yt0Ol0eOutt5CSkqJpHkePHkW9evVQokQJ7NmzBzt37kTz5s2RnJycYfsuXbrg4MGDWL16Nfbs2QMRQdOmTTXXgUlKSkKrVq1Qq1YtHD9+HHv27MEHH3zAW7CJshE8eK3BD9Gb4o2uAPxf0LFjRwwZMkS9iHfXrl1YsmQJtm3bprZ55513DD7z888/I1++fDh16hRKliyZ7TwmTpyIChUq4IcfflCnlShRIsO258+fx+rVq7Fr1y5Uq1YNALBw4UIEBARg1apVaNOmTbbzi4uLQ2xsLJo1a4ZChQoBAIoVK5bt54iI6M3EIzMWLm/evIiIiMD8+fMxb948REREIG/evAZtLl68iPfeew8FCxaEq6srChQoAAC4evWqpnnoj8xocfr0aVhbW6Ny5crqNE9PTxQpUgSnT5/W1EeePHnQpUsXNGrUCM2bN8e3336LmJgYTZ8lIqI3D5OZ/4Bu3brhl19+wfz589OdYgKA5s2b4/79+5gzZw727duHffv2AQBevHihqX/984+0EJFMp+tPE+l0unTtXj4FNW/ePOzZswfVqlXD0qVLERoair1792qOg4iI3hxMZv4DGjdujBcvXuDFixdo1KiRwXv379/H6dOn8dVXX6FevXooVqyYeuGuVqVLl8aWLVs0tS1evDiSkpLUhEkfw7lz59RTRV5eXrh165ZBQqOvcZNW2bJlMWTIEOzevRslS5bEokWLjIqbiIjeDExm/gOsrKxw+vRpnD59Ot1Toz08PODp6YnZs2fjwoUL2Lp1KwYMGGBU/0OGDMGBAwfQp08fHD9+HGfOnMHMmTNx7969dG1DQkLQsmVL9OzZEzt37sSxY8fQoUMH5M+fHy1btgSQevfT3bt3MXHiRFy8eBEzZszAunXr1D6io6MxZMgQ7NmzB1euXMHGjRsNkiEiIqK0mMxkRuT1/ZiAq6srXF1d003X6XRYsmQJDh06hJIlS6J///6YNGmSUX2HhoZi48aNOHbsGCpVqoSqVavijz/+gLV1xtePz5s3D+XLl0ezZs1QtWpViAj++usv2NjYAEi9mPeHH37AjBkzUKZMGezfvx8DBw5UP+/o6IgzZ87gnXfeQWhoKD744AP07dsXH374oVFxExHRm0GRzC5y+I+Ii4uDm5sbYmNj0/2xf/78OaKjo1GgQAHY29vnUIT0b3E9EqV6+Xbsy+MjcigSon8vq7/fL+ORGSIiIrJoTGaIiIjIojGZISIiIovGZIaIiIgsGpMZIiIismhMZoiIiMiiMZkhIiIii8ZkhoiIiCwakxkiIiKyaBnXo6d0lTTNKTdX6QwODka/fv3Qr1+/nA6FiIgoQzwyY6G6dOkCRVHUH09PTzRu3BjHjx836XwOHDiADz74wKR9EhERmRKTGQvWuHFjxMTEICYmBlu2bIG1tTWaNWtm0nl4eXnB0dHRpH0SERGZEpMZC2ZnZwcfHx/4+PggLCwMX3zxBa5du4a7d+8CAG7cuIF3330XHh4e8PT0RMuWLXH58mX18126dEGrVq3wzTffwNfXF56envjoo4+QmJiotgkODsa0adPU12fOnEH16tVhb2+P4sWLY/PmzVAUBatWrQIAXL58GYqiYMWKFahTpw4cHR1RpkwZ7Nmz53UMCRERvYGYzPxHPHnyBAsXLkThwoXh6emJp0+fok6dOnB2dsaOHTuwc+dOODs7o3Hjxnjx4oX6uaioKFy8eBFRUVGYP38+fvnlF/zyyy8ZziMlJQWtWrWCo6Mj9u3bh9mzZ2Po0KEZth06dCgGDhyIo0ePIjQ0FO3bt0dSUpI5Fp2IiN5wvADYgv35559wdnYGAMTHx8PX1xd//vkndDodlixZAp1Oh59++gmKogAA5s2bB3d3d2zbtg0NGzYEAHh4eOD777+HlZUVihYtioiICGzZsgU9e/ZMN7+NGzfi4sWL2LZtG3x8fAAAkZGRaNCgQbq2AwcORERE6oXNI0eORIkSJXDhwgUULVrULGNBRERvLh6ZsWB16tTB0aNHcfToUezbtw8NGzZEkyZNcOXKFRw6dAgXLlyAi4sLnJ2d4ezsjDx58uD58+e4ePGi2keJEiVgZWWlvvb19cWdO3cynN/Zs2cREBCgJjIAUKlSpQzbli5d2qBPAJn2S0RE9G/wyIwFc3JyQuHChdXX5cuXh5ubG+bMmYOUlBSUL18eCxcuTPc5Ly8v9XcbGxuD9xRFQUpKSobzExH1KE920var/0xm/RIREf0bTGb+QxRFgU6nw7Nnz1CuXDksXboU+fLlg6urq0n6L1q0KK5evYrbt2/D29sbQOqt20RERDmJp5ksWEJCAm7duoVbt27h9OnT+Pjjj/HkyRM0b94c77//PvLmzYuWLVvi77//RnR0NLZv345PP/0U169ff6X5NWjQAIUKFULnzp1x/Phx7Nq1S70AWOsRGyIiIlPjkZlM5OaqvHrr169Xr0dxcXFB0aJFsWzZMtSuXRsAsGPHDnzxxRd4++238fjxY+TPnx/16tV75SM1VlZWWLVqFXr06IGKFSuiYMGCmDRpEpo3bw57e3tTLRYRkVm8XNndEvbzpI0iIpLTQZhTXFwc3NzcEBsbm+6P+PPnzxEdHY0CBQrwj/Er2rVrF6pXr44LFy6gUKFCORID1yNRKv6xzhrHx7Jk9ff7ZTl6mmnHjh1o3rw5/Pz8DAqv6YkIRowYAT8/Pzg4OKB27do4efJkzgRLAICVK1di06ZNuHz5MjZv3owPPvgA4eHhOZbIEBER5WgyEx8fjzJlyuD777/P8P2JEydiypQp+P7773HgwAH4+PigQYMGePz48WuOlPQeP36MPn36oGjRoujSpQsqVqyIP/74I6fDIiKiN1iOXjPTpEkTNGnSJMP3RATTpk3D0KFD8fbbbwMA5s+fD29vbyxatAgffvhhhp9LSEhAQkKC+jouLs70gb/BOnXqhE6dOuV0GERERKpcezdTdHQ0bt26pVaqBVKfRVSrVi3s3r0708+NGzcObm5u6k9AQMDrCJeIiIhySK5NZm7dugUAaj0TPW9vb/W9jAwZMgSxsbHqz7Vr17KdF4u5WTauPyKiN1uuvzX75fol2VWhtbOzg52dnaa+bW1todPpcPPmTXh5ecHW1pb1UiyIiODFixe4e/cudDodbG1tczokIiLKAbk2mdE//+fWrVtqLRUg9fk+Lx+teVU6nQ4FChRATEwMbt68aZI+6fVzdHREYGAgdLpce6CRiIjMKNcmMwUKFICPjw82bdqEsmXLAgBevHiB7du3Y8KECSabj62tLQIDA5GUlITk5GST9Uuvh5WVFaytrXlEjYjoDZajycyTJ09w4cIF9XV0dDSOHj2KPHnyIDAwEP369cPYsWMREhKCkJAQjB07Fo6OjnjvvfdMGoeiKLCxsUn30EUiIiLK/XI0mTl48CDq1Kmjvh4wYAAAoHPnzvjll18waNAgPHv2DH369MHDhw9RuXJlbNy4ES4uLjkVMhEREeUyOZrM1K5dG1k9TUFRFIwYMQIjRox4fUERERGRReEVk0RERGTRmMwQERGRRWMyQ0RERBaNyQwRERFZNCYzREREZNGYzBAREZFFYzJDREREFi3XPs6ALEfw4LXppl0eH5EDkRAR0ZuIR2aIiIjIojGZISIiIovGZIaIiIgsGpMZIiIismhMZoiIiMiiMZkhIiIii8ZkhoiIiCwakxkiIiKyaExmiIiIyKIxmSEiIiKLxmSGiIiILBqTGSIiIrJoTGaIiIjIojGZISIiIovGZIaIiIgsGpMZIiIismhMZoiIiMiiMZkhIiIii8ZkhoiIiCwakxkiIiKyaExmiIiIyKIxmSEiIiKL9q+TmeTkZBw9ehQPHz40RTxERERERjE6menXrx9+/vlnAKmJTK1atVCuXDkEBARg27Ztpo6PiIiIKEtGJzPLly9HmTJlAABr1qxBdHQ0zpw5g379+mHo0KEmD5CIiIgoK0YnM/fu3YOPjw8A4K+//kKbNm0QGhqK7t27459//jF5gERERERZMTqZ8fb2xqlTp5CcnIz169ejfv36AICnT5/CysrK5AESERERZcXa2A907doVbdu2ha+vLxRFQYMGDQAA+/btQ9GiRU0eIBEREVFWjE5mRowYgZIlS+LatWto06YN7OzsAABWVlYYPHiwyQMkIiIiyorRyQwAtG7dOt20zp07/+tgiIiIiIz1SsnMli1bsGXLFty5cwcpKSkG782dO9ckgRERERFpYXQyM3LkSIwaNQoVKlRQr5shIiIiyilGJzOzZs3CL7/8go4dO5ojHiIiIiKjGH1r9osXL1CtWjVzxEJERERkNKOTmR49emDRokXmiIWIiIjIaEafZnr+/Dlmz56NzZs3o3Tp0rCxsTF4f8qUKSYLjoiIiCg7Riczx48fR1hYGADgxIkTBu/xYmAiIiJ63YxOZqKioswRBxEREdErMfqambSuX7+OGzdumCoWIiIiIqMZncykpKRg1KhRcHNzQ1BQEAIDA+Hu7o7Ro0enK6BHREREZG5Gn2YaOnQofv75Z4wfPx7h4eEQEezatQsjRozA8+fPERkZaY44iYiIiDJkdDIzf/58/PTTT2jRooU6rUyZMsifPz/69OnDZIaIiIheK6NPMz148ABFixZNN71o0aJ48OCBSYIiIiIi0sroZKZMmTL4/vvv003//vvvUaZMGZMERURERKSV0aeZJk6ciIiICGzevBlVq1aFoijYvXs3rl27hr/++sukwSUlJWHEiBFYuHAhbt26BV9fX3Tp0gVfffUVdLp/dSMWERER/UcYnczUqlUL586dw4wZM3DmzBmICN5++2306dMHfn5+Jg1uwoQJmDVrFubPn48SJUrg4MGD6Nq1K9zc3PDpp5+adF5ERERkmYxOZgDAz8/vtVzou2fPHrRs2RIREREAgODgYCxevBgHDx40+7yJiIjIMmhKZo4fP46SJUtCp9Ph+PHjWbYtXbq0SQIDgOrVq2PWrFk4d+4cQkNDcezYMezcuRPTpk3L9DMJCQlISEhQX8fFxZksHiIiIsp9NCUzYWFhuHXrFvLly4ewsDAoigIRSddOURQkJyebLLgvvvgCsbGxKFq0KKysrJCcnIzIyEi0b98+08+MGzcOI0eONFkMRERElLtpSmaio6Ph5eWl/v66LF26FAsWLMCiRYtQokQJHD16FP369YOfnx86d+6c4WeGDBmCAQMGqK/j4uIQEBDwukImIiKi10xTMhMUFKT+fuXKFVSrVg3W1oYfTUpKwu7duw3a/luff/45Bg8ejHbt2gEASpUqhStXrmDcuHGZJjN2dnaws7MzWQxERESUuxl9f3OdOnUyLI4XGxuLOnXqmCQovadPn6a7BdvKyorPgCIiIiKV0XcziQgURUk3/f79+3BycjJJUHrNmzdHZGQkAgMDUaJECRw5cgRTpkxBt27dTDofIiIislyak5m3334bQOpFvl26dDE4lZOcnIzjx4+jWrVqJg1u+vTpGDZsGPr06YM7d+7Az88PH374Ib7++muTzoeIiIgsl+Zkxs3NDUDqkRkXFxc4ODio79na2qJKlSro2bOnSYNzcXHBtGnTsrwVm4iIiN5smpOZefPmAUgtXPf555/D0dHRbEERERERaWX0BcCdOnXCjRs30k0/f/48Ll++bIqYiIiIiDQzOpnp0qULdu/enW76vn370KVLF1PERERERKSZ0cnMkSNHEB4enm56lSpVcPToUVPERERERKSZ0cmMoih4/PhxuumxsbEmfZQBERERkRZGJzM1atTAuHHjDBKX5ORkjBs3DtWrVzdpcERERETZMbpo3sSJE1GzZk0UKVIENWrUAAD8/fffiIuLw9atW00eIBEREVFWjD4yU7x4cRw/fhxt27bFnTt38PjxY3Tq1AlnzpxByZIlzREjERERUaaMPjIDAH5+fhg7dqypYyEiIiIymtFHZoDU00odOnRAtWrV1Jozv/32G3bu3GnS4IiIiIiyY3Qy8/vvv6NRo0ZwcHDA4cOHkZCQAAB4/Pgxj9YQERHRa2d0MjNmzBjMmjULc+bMgY2NjTq9WrVqOHz4sEmDIyIiIsqO0cnM2bNnUbNmzXTTXV1d8ejRI1PERERERKSZ0cmMr68vLly4kG76zp07UbBgQZMERURERKSV0cnMhx9+iE8//RT79u2Doii4efMmFi5ciIEDB6JPnz7miJGIiIgoU0bfmj1o0CDExsaiTp06eP78OWrWrAk7OzsMHDgQffv2NUeMRERERJl6pTozkZGRGDp0KE6dOoWUlBQUL14czs7Opo6NiIiIKFuvlMwAgKOjIypUqGDKWIiIiIiMZnQyEx8fj/Hjx2PLli24c+cOUlJSDN6/dOmSyYIjIiIiyo7RyUyPHj2wfft2dOzYEb6+vlAUxRxxEREREWlidDKzbt06rF27FuHh4eaIh4iIiMgoRt+a7eHhgTx58pgjFiIiIiKjGZ3MjB49Gl9//TWePn1qjniIiIiIjGL0aabJkyfj4sWL8Pb2RnBwsMHzmQDw+UxERET0WhmdzLRq1coMYRARERG9GqOTmeHDh5sjDiIiIqJX8spF8168eJFhnZnAwMB/HRQRERGRVkYnM+fOnUP37t2xe/dug+kiAkVRkJycbLLgiIiIiLJjdDLTtWtXWFtb488//2TRPCIiIspxRiczR48exaFDh1C0aFFzxENERERkFKPrzBQvXhz37t0zRyxERERERjM6mZkwYQIGDRqEbdu24f79+4iLizP4ISIiInqdjD7NVL9+fQBAvXr1DKbzAuBc7OXrmkRyJg4iIiIzMDqZiYqKMkccRERERK/E6GSmVq1a5oiDiIiI6JW8UtG8R48e4eeff8bp06ehKAqKFy+Obt26wc3NzdTxEREREWXJ6AuADx48iEKFCmHq1Kl48OAB7t27hylTpqBQoUJ8yCQRERG9dkYfmenfvz9atGiBOXPmwNo69eNJSUno0aMH+vXrhx07dpg8SCIiIqLMGJ3MHDx40CCRAQBra2sMGjQIFSpUMGlwRERERNkx+jSTq6srrl69mm76tWvX4OLiYpKgiIiIiLQyOpl599130b17dyxduhTXrl3D9evXsWTJEvTo0QPt27c3R4xEREREmTL6NNM333wDRVHQqVMnJCUlAQBsbGzQu3dvjB8/3uQBEhEREWXF6GTG1tYW3377LcaNG4eLFy9CRFC4cGE4OjqaIz4iIiKiLBmdzMTGxiI5ORl58uRBqVKl1OkPHjyAtbU1XF1dTRogERERUVaMvmamXbt2WLJkSbrp//vf/9CuXTuTBEVERESkldHJzL59+1CnTp1002vXro19+/aZJCgiIiIirYxOZhISEtQLf9NKTEzEs2fPTBIUERERkVZGJzMVK1bE7Nmz002fNWsWypcvb5KgiIiIiLQy+gLgyMhI1K9fH8eOHUO9evUAAFu2bMGBAwewceNGkwdIRERElBWjj8yEh4djz549CAgIwP/+9z+sWbMGhQsXxvHjx1GjRg1zxEhERESUKaOPzABAWFgYFi5caOpYiIiIiIz2SskMZS548FqD15fHR+RQJERERG8Go08zEREREeUmuT6ZuXHjBjp06ABPT084OjoiLCwMhw4dyumwiIiIKJfI1aeZHj58iPDwcNSpUwfr1q1Dvnz5cPHiRbi7u+d0aERERJRLvHIyc+HCBVy8eBE1a9aEg4MDRASKopgyNkyYMAEBAQGYN2+eOi04ONik8yAiIiLLZvRppvv376N+/foIDQ1F06ZNERMTAwDo0aMHPvvsM5MGt3r1alSoUAFt2rRBvnz5ULZsWcyZMyfLzyQkJCAuLs7gh4iIiP67jE5m+vfvD2tra1y9ehWOjo7q9HfffRfr1683aXCXLl3CzJkzERISgg0bNqBXr1745JNP8Ouvv2b6mXHjxsHNzU39CQgIMGlMRERElLsYfZpp48aN2LBhA/z9/Q2mh4SE4MqVKyYLDABSUlJQoUIFjB07FgBQtmxZnDx5EjNnzkSnTp0y/MyQIUMwYMAA9XVcXBwTGiIiov8wo4/MxMfHGxyR0bt37x7s7OxMEpSer68vihcvbjCtWLFiuHr1aqafsbOzg6urq8EPERER/XcZnczUrFnT4DSPoihISUnBpEmTUKdOHZMGFx4ejrNnzxpMO3fuHIKCgkw6HyIiIrJcRp9mmjRpEmrXro2DBw/ixYsXGDRoEE6ePIkHDx5g165dJg2uf//+qFatGsaOHYu2bdti//79mD17doZP7SYiIqI3k9FHZooXL47jx4+jUqVKaNCgAeLj4/H222/jyJEjKFSokEmDq1ixIlauXInFixejZMmSGD16NKZNm4b333/fpPMhIiIiy/VKdWZ8fHwwcuRIU8eSoWbNmqFZs2avZV5ERERkeYxOZnbs2JHl+zVr1nzlYIiIiIiMZXQyU7t27XTT0lb+TU5O/lcBERERERnD6GTm4cOHBq8TExNx5MgRDBs2DJGRkSYLjIjIEgUPXmvw+vL4iByKhOjNYXQy4+bmlm5agwYNYGdnh/79+/OJ1kRERPRaGX03U2a8vLzS1YQhIiIiMjejj8wcP37c4LWIICYmBuPHj0eZMmVMFhgRERGRFkYnM2FhYVAUBSJiML1KlSqYO3euyQIjIiIi0sLoZCY6OtrgtU6ng5eXF+zt7U0WFBEREZFWRiczfC4SERER5SZGJzPfffed5raffPKJsd0TERERGcXoZGbq1Km4e/cunj59Cnd3dwDAo0eP4OjoCC8vL7WdoihMZoiIiMjsjL41OzIyEmFhYTh9+jQePHiABw8e4PTp0yhXrhzGjBmD6OhoREdH49KlS+aIl4iIiMiA0cnMsGHDMH36dBQpUkSdVqRIEUydOhVfffWVSYMjIiIiyo7RyUxMTAwSExPTTU9OTsbt27dNEhQRERGRVkYnM/Xq1UPPnj1x8OBBtdbMwYMH8eGHH6J+/fomD5CIiIgoK0YnM3PnzkX+/PlRqVIl2Nvbw87ODpUrV4avry9++uknc8RIRERElCmj72by8vLCX3/9hXPnzuHMmTMQERQrVgyhoaHmiI+IiIgoS0YnM3qhoaFMYIiIiCjHaUpmBgwYgNGjR8PJyQkDBgzIsu2UKVNMEhgRERGRFpqSmSNHjqh3MB05ciTTdoqimCYqIiIiIo00JTNRUVEZ/k5ERESU04y+m4mIiIgoNzH6AuD4+HiMHz8eW7ZswZ07d5CSkmLwPh9jQERERK+T0clMjx49sH37dnTs2BG+vr68ToaIiIhylNHJzLp167B27VqEh4ebIx4iIiIioxh9zYyHhwfy5MljjliIiIiIjGZ0MjN69Gh8/fXXePr0qTniISIiIjKK0aeZJk+ejIsXL8Lb2xvBwcGwsbExeP/w4cMmC46IiIhej+DBaw1eXx4fkUORGM/oZKZVq1ZmCIOIiN4ElvwHk3Ivo5OZ4cOHmyMOIiIiolfConlERERk0Yw+MqPT6bKsLZOcnPyvAiIiIiIyhtHJzMqVKw1eJyYm4siRI5g/fz5GjhxpssCIiIiItDA6mWnZsmW6aa1bt0aJEiWwdOlSdO/e3SSBEREREWlhsmtmKleujM2bN5uqO6I3g6IY/hARkdFMksw8e/YM06dPh7+/vym6IyIiItLM6NNMHh4eBhcAiwgeP34MR0dHLFiwwKTBEREREWXH6GRm2rRpBq91Oh28vLxQuXJleHh4mCouIiIiIk2MTmY6d+5sjjiIiIiIXgmL5hEREZFFYzJDREREFo3JDBEREVk0TcnM6tWrkZiYaO5YiIiIiIymKZl566238OjRIwCAlZUV7ty5Y86YiIiIiDTTlMx4eXlh7969AFLrymT1oEkiIiKi10nTrdm9evVCy5YtoSgKFEWBj49Ppm351GwiIiJ6nTQlMyNGjEC7du1w4cIFtGjRAvPmzYO7u7uZQyMiIiLKnuaieUWLFkXRokUxfPhwtGnTBo6OjuaMi4iIiEgToysADx8+HABw9+5dnD17FoqiIDQ0FF5eXiYPjoiIiCg7RteZefr0Kbp16wY/Pz/UrFkTNWrUgJ+fH7p3746nT5+aI0YiIiKiTBmdzPTv3x/bt2/H6tWr8ejRIzx69Ah//PEHtm/fjs8++8wcMRIRERFlyujTTL///juWL1+O2rVrq9OaNm0KBwcHtG3bFjNnzjRlfERERERZeqXTTN7e3umm58uXj6eZiIiI6LUzOpmpWrUqhg8fjufPn6vTnj17hpEjR6Jq1aomDe5l48aNg6Io6Nevn1nnQ0RERJbD6NNM3377LRo3bgx/f3+UKVMGiqLg6NGjsLe3x4YNG8wRIwDgwIEDmD17NkqXLm22eRAREZHlMfrITMmSJXH+/HmMGzcOYWFhKF26NMaPH4/z58+jRIkS5ogRT548wfvvv485c+bAw8PDLPMgIiIiy2T0kRkAcHBwQM+ePU0dS6Y++ugjREREoH79+hgzZkyWbRMSEpCQkKC+jouLM3d4RERElINeKZl5nZYsWYLDhw/jwIEDmtqPGzcOI0eONHNURERElFsYfZrpdbp27Ro+/fRTLFiwAPb29po+M2TIEMTGxqo/165dM3OURERElJNy9ZGZQ4cO4c6dOyhfvrw6LTk5GTt27MD333+PhIQEWFlZGXzGzs4OdnZ2rztUIiIiyiG5OpmpV68e/vnnH4NpXbt2RdGiRfHFF1+kS2RIm+DBaw1eXx4fkUORkLm9vK4Brm8i+u8xOpkpWLAgDhw4AE9PT4Ppjx49Qrly5XDp0iWTBefi4oKSJUsaTHNycoKnp2e66UREZHr854csgdHXzFy+fBnJycnppickJODGjRsmCYqIiIhIK81HZlavXq3+vmHDBri5uamvk5OTsWXLFgQHB5s0uIxs27bN7PMgIiIiy6E5mWnVqhUAQFEUdO7c2eA9GxsbBAcHY/LkySYNjoiIiCg7mpOZlJQUAECBAgVw4MAB5M2b12xBEREREWll9AXA0dHR5oiDiIjI4vAC6dzhlW7N3rJlC7Zs2YI7d+6oR2z05s6da5LAiIiIiLQwOpkZOXIkRo0ahQoVKsDX1xeKopgjLiIiIiJNjE5mZs2ahV9++QUdO3Y0RzxEFoOHl4mIcgej68y8ePEC1apVM0csREREREYzOpnp0aMHFi1aZI5YiOh1UhTDHyIiC2X0aabnz59j9uzZ2Lx5M0qXLg0bGxuD96dMmWKy4IiIiIiyY3Qyc/z4cYSFhQEATpw4YfAeLwYmIiKi183oZCYqKsoccZAJ8IJUIiJ6Exl9zQwRERFRbmL0kZk6depkeTpp69at/yogIiIiImMYnczor5fRS0xMxNGjR3HixIl0D6AkylEvJ90iORMHERGZldHJzNSpUzOcPmLECDx58uRfB0RERERkDJNdM9OhQwc+l4mIiIheO5MlM3v27IG9vb2puiMiIiLSxOjTTG+//bbBaxFBTEwMDh48iGHDhpksMCIiIiItjE5m3NzcDF7rdDoUKVIEo0aNQsOGDU0WGBEREZEWRicz8+bNM0ccREREZAFyY4FWo5MZvUOHDuH06dNQFAXFixdH2bJlTRkXERERkSZGJzN37txBu3btsG3bNri7u0NEEBsbizp16mDJkiXw8vIyR5xEREREGTL6bqaPP/4YcXFxOHnyJB48eICHDx/ixIkTiIuLwyeffGKOGImIiIgyZfSRmfXr12Pz5s0oVqyYOq148eKYMWMGLwAmIiKi187oIzMpKSmwsbFJN93GxgYpKSkmCYqIiIhIK6OPzNStWxeffvopFi9eDD8/PwDAjRs30L9/f9SrV8/kARIRUe6XG+9woTeH0Udmvv/+ezx+/BjBwcEoVKgQChcujAIFCuDx48eYPn26OWIkIiIiypTRR2YCAgJw+PBhbNq0CWfOnIGIoHjx4qhfv7454iMiIiLK0ivXmWnQoAEaNGhgyliIiIiIjKb5NNPWrVtRvHhxxMXFpXsvNjYWJUqUwN9//23S4IiIiIiyozmZmTZtGnr27AlXV9d077m5ueHDDz/ElClTTBocERERUXY0JzPHjh1D48aNM32/YcOGOHTokEmCIiIiItJKczJz+/btDOvL6FlbW+Pu3bsmCcqiKIrhDxEREb1WmpOZ/Pnz459//sn0/ePHj8PX19ckQRERERFppTmZadq0Kb7++ms8f/483XvPnj3D8OHD0axZM5MGR0RERJQdzbdmf/XVV1ixYgVCQ0PRt29fFClSBIqi4PTp05gxYwaSk5MxdOhQc8ZKRERElI7mZMbb2xu7d+9G7969MWTIEIgIAEBRFDRq1Ag//PADvL29zRYoERERUUaMKpoXFBSEv/76Cw8fPsSFCxcgIggJCYGHh4e54iPKEp8HQ0REr1QB2MPDAxUrVjR1LERERERGe+XHGRAREZFpmeNo85twBNvop2YTERER5SZMZoiIiMii8TSTpcqo2vAXf77+OP4LXh7L/3+nXm72Jhw2JiLSikdmiIiIyKIxmSHSis/hIiLKlZjMEBERkUVjMkNEREQWjRcAExEAXlScm3HdEGWNR2aIiIjIojGZISIiIovGZIaIiIgsGpMZIiIismhMZoiIiMii5epkZty4cahYsSJcXFyQL18+tGrVCmfPns3psIiIiCgXydXJzPbt2/HRRx9h79692LRpE5KSktCwYUPEx8fndGhERESUS+TqOjPr1683eD1v3jzky5cPhw4dQs2aNXMoKiIiIspNcnUy87LY2FgAQJ48eTJtk5CQgISEBPV1XFyc2eMiIiKinJOrTzOlJSIYMGAAqlevjpIlS2babty4cXBzc1N/AgICXmOURERE9LpZTDLTt29fHD9+HIsXL86y3ZAhQxAbG6v+XLt27TVFSERERDnBIk4zffzxx1i9ejV27NgBf3//LNva2dnBzs7uNUVGREREOS1XJzMigo8//hgrV67Etm3bUKBAgZwOiYiIiHKZXJ3MfPTRR1i0aBH++OMPuLi44NatWwAANzc3ODg45HB0RERElBvk6mtmZs6cidjYWNSuXRu+vr7qz9KlS3M6NCIiIsolcvWRGRHJ6RCIiIgol8vVycwbSVEMXzOhIyIiylKuPs1ERERElB0mM0RERGTRmMwQERGRRWMyQ0RERBaNyQwRERFZNN7NRGRqL9+RBmi+Ky148FqD15fHR5giIiKi/zQemSEiIiKLxmSGiIiILBqTGSIiIrJoTGaIiIjIojGZISIiIovGZIaIiIgsGm/NJqIcx1vSXy+O9+vHMTcvHpkhIiIii8ZkhoiIiCwakxkiIiKyaExmiIiIyKIxmSEiIiKLxmSGiIiILBqTGSIiIrJoTGaIiIjIorFoHv0fRTF8LZL75v1yuy/+NHkoubK41WtYbq1ycnz+S/POld+zXOTl8QE4RpQ5HpkhIiIii8ZkhoiIiCwakxkiIiKyaExmiIiIyKIxmSEiIiKLxmSGiIiILBqTGSIiIrJoTGaIiIjIojGZISIiIovGZIaIiIgsGpMZIiIismhMZoiIiMii8UGTr0tOPsTR1Ix56OFLbYNfassHx5kYH0hp1Lz5MMP/hjfxIaD87hrikRkiIiKyaExmiIiIyKIxmSEiIiKLxmSGiIiILBqTGSIiIrJoTGaIiIjIojGZISIiIovGZIaIiIgsGpMZIiIismhMZoiIiMiiMZkhIiIii8ZkhoiIiCwakxkiIiKyaExmiIiIyKIxmSEiIiKLxmSGiIiILBqTGSIiIrJoFpHM/PDDDyhQoADs7e1Rvnx5/P333zkdEhEREeUSuT6ZWbp0Kfr164ehQ4fiyJEjqFGjBpo0aYKrV6/mdGhERESUC+T6ZGbKlCno3r07evTogWLFimHatGkICAjAzJkzczo0IiIiygWsczqArLx48QKHDh3C4MGDDaY3bNgQu3fvzvAzCQkJSEhIUF/HxsYCAOLi4swXaBopCU8NXmc6X63xGBG31nmbOsaX+8vJeWe5nk3cpyXM2xzrxhzjk1Pz5vjk3nlz3RjXLifnba6/r/p+RST7xpKL3bhxQwDIrl27DKZHRkZKaGhohp8ZPny4AOAPf/jDH/7whz//gZ9r165lmy/k6iMzeoqiGLwWkXTT9IYMGYIBAwaor1NSUvDgwQN4enpm+hlTiYuLQ0BAAK5duwZXV9fX1i4n520JMb6p87aEGHNy3pYQ45s6b0uIMSfnbQkxmoKI4PHjx/Dz88u2ba5OZvLmzQsrKyvcunXLYPqdO3fg7e2d4Wfs7OxgZ2dnMM3d3d1cIWbI1dVV00o2dbucnLclxPimztsSYszJeVtCjG/qvC0hxpyctyXE+G+5ublpaperLwC2tbVF+fLlsWnTJoPpmzZtQrVq1XIoKiIiIspNcvWRGQAYMGAAOnbsiAoVKqBq1aqYPXs2rl69il69euV0aERERJQL5Ppk5t1338X9+/cxatQoxMTEoGTJkvjrr78QFBSU06GlY2dnh+HDh6c7zWXudjk5b0uI8U2dtyXEmJPztoQY39R5W0KMOTlvS4jxdVNEtNzzRERERJQ75eprZoiIiIiyw2SGiIiILBqTGSIiIrJoTGaIiIjIojGZISIiIouW62/N/q9bvXq10Z9p0KABHBwcLLrP7777TlMf27dvV3+vXLky7O3ts/1M165d4eLikun7Wuf9zz//qL/Xr18/y/FZs2YNAKBo0aKwsbHR1H92cZra8ePHNbXbtm2b+ntAQACsrKyy/Yypvj/6cQS0j2V246h1udMqXrw4rK1z3+4xJ7dtc6wbU9O6badlqhi1juPly5fV36tUqaJpn5bd99Ec3wtLw1uz/4Xr169j5syZ2L17N27dugVFUeDt7Y1q1aphxowZ0OmyP/D18OFD9ffsnh2lX1Vubm5Z9q21z7SrvkSJErC1tc207ZEjR4yKMbs+jxw5AhsbG3XMMvuDefnyZVhZWSE5ORn+/v5ZbtA3b97EixcvNC2L1nnraV1uLTECyDbOS5cuqb9XrVo1y53O3r17AQBly5bNsvbDypUr1d+1fi+0ttP6nTTm+5PVWGodR+DVvrsNGzaEk5NThu304w1kPeZbt25Vfw8MDMzye6FPuJydnU2+befmdXPmzBkAQKFChbJMjtImpFmN0cOHD6EoChRFgZ+fX6YxXr16FUDqs/uyG59Hjx4BSC3fb+p1o3XMtX7HtfSpb3P+/HkULFgw27aWIvf962Ehdu7ciSZNmiAgIAANGzZEw4YNISK4c+cOVq1ahdjYWHz66acoW7Zslv107doV06ZNw+DBg3HixIksv1w6nQ42NjYYOnQo8uXL96/71Ol0+Oyzz/Ddd9+hfv368PDwyLTPI0eOwNbWFqdPn842Ri19HjlyBJ9++immT5+Obdu2ZRnjzZs3UahQIWzfvl3T+GhZFq3zvnXrFgoVKoRjx45lO28HBwdNMWoZnxEjRqBEiRI4ffo0nJycsvzPMSYmRp1/dv9hNmvWDBs2bMCWLVsQGBiYYZsCBQpg//79qF27No4fP57l+EybNg2DBg3S9J20s7PDyZMnsx0f/ZhnNZbGfndtbGyyXGYgdbkjIiKwfv16uLi4ZDqWMTExCA4OxtWrV7Mc89jYWJQoUQJnzpxB3bp1s43RysrKpNu2pawbKysr1KtXL9t2jRs3xubNm7NcnrTjk12MxoyPtbW1SdfN/v37UatWLaxfvz7b76S1tbWmcRw4cCCmT5+OU6dOZbkejx8/jipVquDMmTN48uRJpu3Syq1HKg1k+1xtylCFChWkX79+mb4PQMqUKZNtP126dJG4uDhxdnaWixcvZtlWURTp3Lmz3L171yR9Kooit2/fll69emnq08nJSVOMWvocMWKExMfHZxujvt3YsWPl4cOH2c77yy+/zLad1nnrx1HLvEeMGCHdu3fPdhz145Ndn/p2Wr8Xjo6O2barXbu2PHz4UPNyN2nSRG7evJltjKb8/ujXTXZ9ah1HkdTlNua7q3W5tW5fplyHObltm2PdaF1urWOpdds2R4xa141+O9SybhRFkbp162b5fTRm3vo+tf4AEABSokQJKVu2bLY/5cqVk+vXr2c5f3PhaaZX5ODggKNHj6JIkSIZvn/mzBmULVsWz549e82RaXflyhUEBgZqOiwJANeuXYOfn1+W11CYo0+tcnLeWmmNcfv27QgPD0dMTEy2MW7fvh0igqpVq2oqMb5z505UrFgx15UjN4Y51rV+zLP7D1Tfbu/evVmOoz7GXbt2aRrvnPg+moMx6+bKlStQFAX58+fXtNz/lTHSyhzfc/2RIS8vr2z70x8Z6tOnT5ZHhoDUU2Ljx4/P9siQuTCZeUUFCxbEsGHD0LVr1wzfnzdvHkaPHm1w7YOxkpKScv+hPRNJTk7GvXv3oCgKPD09M90YtbYjIvPitmiZ6tSpg5UrV8Ld3T3btjqdDnXq1MGCBQvg6+ubbXsXF5dsT8mbC2/NfkUDBw5Er1690LdvX/zxxx/Yu3cv9u3bhz/++AN9+/ZF7969MWjQoHSfO3r0KJYtW4adO3eqF3etX79evXMmJSUFY8aMQf78+WFnZwd/f3+MHz8eWeWcGfWZnYsXL6Ju3brZtouPj8eOHTvU1wcOHMD777+PAgUKwMHBAY6OjihQoADef/99HDx4MNN+Hj58iAMHDuD69esG01euXInw8HA4OjrCz88Pvr6+cHR0RHh4OFatWmV0O73k5GSD1/v27cOOHTuQmJioTvvpp5/QuXNnzJs3DwCwdOlSFCtWDAULFsTw4cOzHRvAcBzPnTtnMP47d+5Eq1atUKJECdSvXx9//PGHwWfPnz+P+fPnY8KECZg4cSLmz5+P8+fPZzvP27dvqxcwapWUlKTpM9euXUO3bt3w4sULg+kXL15Ev379EBERgR49euDQoUOZ9vEq30f9POrWrWv0OKaV2ffsyJEjiI6OVl8vWLAA4eHhCAgIQPXq1bFkyZIsY7t7967Bdyc7Wsf75e1LRLBp0yaMHDkSvXv3Rp8+fTBy5Ehs3rz5lbZtU/T3cp/GbotalhvQts2KCKKjo5GUlAQg9SLjpUuX4tdff8W9e/cAGLcdaukvM5l9z2NiYrBgwQL89ddf6baj+Ph4jBo1CgBw+vRpzJs3T70I+syZM+jduze6detmcPG41vHRus1GRUVpSmQAIDo6Gps3b9aUyADAqVOncu4h0K/3rNZ/y5IlS6Ry5cpibW2tnmO0traWypUry9KlS6V9+/YSFxcnIiKPHz+Whg0biqIoYmtrK4qiSIUKFeThw4dSvHhx2bVrl4iIjB07Vjw9PWXKlCmybt06mTZtmnh7e8v48eNFRDT3mZ2jR4+KTqczqt3KlSvFxsZGGjduLFOnTpVFixbJwoULZerUqdKkSROxtbWVVatWyZAhQyQ+Pl5ERF68eCE9e/YUnU4niqKITqeTt956S549eyazZs0SW1tb6dWrl6xcuVJ2794tu3btkpUrV0qvXr3Ezs5OZs+erbmdiMjNmzclPDxcrKyspGbNmvLgwQOJiIhQ109oaKjcvHlTpk6dKk5OTvL222+Lr6+vjBkzRjw9PWXMmDEyatQocXNzkx9//NGo8dHpdHL79m0REYmKihKdTifNmzeXyMhIeeedd0Sn08n69evl0aNH0qJFC1EURdzd3SU0NFRCQkLE3d1ddDqdtGzZUmJjYyUuLk7ef/99CQwMlE6dOklCQoL06dNHHceaNWtKbGxstjG+yvpOuyxHjhwRR0dHCQsLk549e0rFihXF1tZW9u3bZ7LvY2bzzmoctX7PRETKli0rW7duFRGROXPmiIODg3zyyScyc+ZM6devnzg7O8vPP/8sP/74ozx//lxERFJSUiQyMlJdL46OjtK/f39JTk42+XiLiFy/fl3CwsLEyspKypQpIw0bNpQGDRpImTJlxMrKSvP1CPo+TdVf2j6N2Ra1LrfWbfbMmTMSFBQkOp1OChcuLJcuXZLy5cuLk5OTODo6St68eeXcuXOavz9a+xPRvt/dv3+/uLu7i6urqzg4OEhISIicOHFCXe5bt26JTqeTdevWia2treTJk0fs7e1l3bp14uXlJfXr15d69eqJtbW1bNmyxajx0brNGuPYsWOavu96J06ckMTERKPmYSo8zWQCiYmJahafN29e9RZDKysrxMTEIF++fPj888/x+++/Y/ny5ShXrhxOnDiBtm3bonHjxpg5cybOnTuHgIAAlCpVCsOGDUPbtm3V/teuXYt+/frh/PnzmvsMDg7OMuYbN27gm2++SZftv+zYsWMoV64ckpOTUbJkSXTo0AGDBw/OsO2ECRPw66+/4syZM2qMY8eOxbRp0zBr1ixUqVIFhw8fRq9evfDhhx9i/vz5GDJkCLp3755hf3PnzkVkZCQURdHU7uLFi+jUqRMuXryIwYMHY+HChbh27RqsrKywePFipKSk4P3330fp0qWxZcsWDBs2DO+99x6OHDmCSpUqYdasWeo85s2bhxkzZqBTp06ax1F/t0e+fPlQv359FClSBDNmzFDbDhkyBLt370ZQUBCOHj2KOXPmoHLlygb97du3Dx988AHCwsLg6uqKzZs3o0+fPlixYgXc3Nxw8eJFzJo1CykpKejTpw9atGiByMjILGME/m89pr09OyOXLl3CZ599BhFRl6V58+awt7fH//73P/Xcfbdu3RATE4ONGzdq+j5OmTIl2xog+rFMO++sxnHnzp2avmfDhg2Dk5MTTp8+jcDAQJQrVw69evXCBx98oPa5aNEiREZGGnx3f/zxR3z22WcYNWqU2udXX32FMWPGoG/fvprG25jtq2XLlnjy5EmGh/RjYmLQoUMHuLi4ZHtEVT+OzZo109TfqlWrNK+bAgUKaN4WtS631m32+vXrEBGMGTMGc+fOxcaNGxESEoJly5ZBRNC2bVu4uLhg4cKFmr4/Hh4emvr77bffNO93//nnHwQGBmLOnDmIj4/H4MGDsXTpUmzatAlly5bF7du34efnh8qVK6Nu3boYM2YMlixZgj59+qB3797qtjx06FAcOHAAGzdu1Dw+P/zwg6Ztdt26dVmuGyD1CO3w4cMxf/583Lp1S9P1NUDq7etHjx7NmVu+cySFekPor5gXESlRooQsXbrU4P21a9dKSEiI+Pr6yp49e0RExNvbWw4fPmzQ7ty5c+Lg4GBUn4qiiJ+fnwQHB2f44+fnJzqdTjw8PLL8cXV1Vf+DsrOzk7Nnz2a6vGfOnBE7OzuDGMPCwuTnn382aLd06VIpVqyY2Nvby5kzZzLt7/Tp02Jvb6+5nYgYjOX9+/dFURTZvHmz2nbr1q1SsGBBcXBwkCtXrqjT7ezsDP6DOn/+vLi7u2seRxHDdePr6yt79+41iPPkyZPi6ekpbm5u6d5La8+ePeLm5iYBAQHq0YQbN26IoiiyevVqtd3atWulSJEiIiLZ3mVQtGhRg6MWWd3BoH9fvyz+/v6yc+dOgxiPHj0q3t7emr+P+vHRMpZax1Hr90xExNPTUw4ePCgiIvny5ZOjR48atL1w4YI4ODgY9FmxYkWZMmWKQbs5c+ZI6dKlNY+3MduXk5NTurjSOnz4sDg5OWkeR639GbNutG6Lxiy31m3Wy8tLjhw5IiIiT548EUVR5O+//1bb7d69WwIDAzV/f7T2px8fLd9zDw+PdPvICRMmiIeHh+zfv189MuPq6irnz58XEZHk5GSxtraWQ4cOqZ/5559/xNvb26jx0brNaqE/cqYoinz44YfSv39/TT92dnbZ3k1lLm/G1aU5SJ8V3759GyVLljR4r0SJEuo1CpGRkVi1ahVatmyJH374AbNnz1Y/+/333yMsLMyoPoOCgjBhwgSDIzxpHT16FOXLl0dCQgJ69+6NUqVKZdjuypUrGDlyJIDUwlarVq3K8FogAPjjjz9QsGBBnDlzRo3x2rVrqFSpkkG7SpUq4cqVKyhRogRmz56NyZMnZ9jfnDlzUKJECQDQ3O7hw4fInz8/ACBPnjxwdHQ0OIdbqFAhxMTEwNHREfHx8ep0Ly8vODs7G/SblJSkeRz1Hj9+DHt7ezg4OKS7e8XW1hbPnj1TC/ZlRv/enTt3ULhwYQCAn58fHBwcDO6e069rIPVcdbt27VCgQIEM+4yJicG5c+fg6+uLGTNmoFWrVlkuj77wGJB6hNHV1dWgnaurK2JjYw3izer7CMCosdQyjmnnndX3DACaNGmCmTNn4qeffkKtWrWwfPlylClTRm37v//9D4ULF8aJEyfUPqOjo1GvXj2DPuvWrYv+/fsjMTFR03gbs305ODjgwYMHGbYDUr/bDg4O8PLy0jSOWvsDtK8brdvs6dOnNS+31m1W/z4AODk5wcnJyeCIk7+/P27fvg1A2/dHRDT3B2j7njs4OOD58+cG7w0aNAg6nQ4NGzbE3Llz042FTqeDvb29wXUsLi4u6valdXyM2Wazqxisv3GlZs2aOHv2bJZt08quwKc5MZkxs2HDhsHR0VE9BVG8eHH1vXv37sHZ2Rljx45F/fr1UbRoUVStWhXLli3Dpk2bEBoaigsXLuD+/fvYuHGjUX2WL18ehw4dynTnpCgKRARhYWEICAhA586dM2x37NgxdaczatQotGvXDtu3b0fDhg3h7e0NRVFw69YtbNq0CRs3bsSSJUvQunVrzJkzB87OzrCzszOojAmkFhOzs7PD5MmT1SJlGfV35coV/PXXX0hJSdHUDgDy5cuHmJgYBAQEAAD69u2r7rCA1B2Dk5MTihQpguPHj6NYsWIAoP7B1Ttz5gyCg4NRpEgRTeOoFxoaCiD1wsJDhw4ZJKEnT55E/vz5UblyZfTs2RM///wzKlSoYNDfwYMH0atXL7Ro0QJbtmzB3bt31WVp2bKlwQ7vyZMn6o66ZMmSqFy5Mnr37p1hnPrTWuXLl8fhw4czTWbSLk9oaCgURcGTJ0/wzz//GPxhOn/+PHx8fHDlyhVN30cAmr+TWsfxwoULmr5nQOop0PDwcNSqVQsVKlTA5MmTsW3bNhQrVgxnz57F3r17sXLlSjRr1gzr16+Hm5sbHBwc0pVWePbsGXQ6nebxNmb7ateuHTp37owpU6agQYMGcHNzU5dj06ZN+Oyzz/Dee+/hxo0bmsZRa3/GrBut2+zgwYM1L7fWbdbNzQ1Xr15Vi8xNnDjRoIjd3bt34eHhgdu3b2v6/qSkpGjqT0/L97xYsWLYvXs3SpcubbC8AwcOhIigffv2AIDg4GBcuHBB/Wdlz549BsXzrl27piZWWsfn+fPnmrZZAGjVqlW6fdfLFEUxeLRJbsdkxozSZrXFixc3uJsCAP766y+UKFECbm5u2L17N37++WesWbMGwcHBSElJwYsXL9C+fXv07t0b/v7+RvU5atQoPH36NNPY9J9duHChWq47I3ny5FGvG3nnnXewY8cOfPvtt5gyZQpu3boFAPDx8UHVqlWxfft2VK1aVT1nDKT+F3T48GHUqFFD7TMqKgpFihRBrVq1cOLECcycORN79+416K9Zs2bo1auXeu2P1nZhYWHYs2eP+l/6+PHjDZZn586dKF26NEaNGpVpuXogtdz5hx9+iLp162oaR/1ypfXydQqXL19Gz5490bNnT7Rv3x6VKlWCu7s78uXLB0VRcPv2bcTGxqJRo0b47rvv0L59exw4cADlypUDkHpdR1oHDhxQk7Hq1atn+R+Ui4sLatasic8//9zgiNTLChcujKioKINHOQCp//2ltXfvXrz11ls4fPiwpu8jAM3fyZfnndk4zpgxQ9P3DEg9snXkyBGMHz8ea9asgYhg//79uHbtGsLDw7Fr1y41sUz7B3jLli0G1zXt2bMHhQoV0jzeDRo00Lx9TZ48GUlJSXj//feRlJSklq9/8eIFrK2t0b17d0yaNAkXLlzQNI6+vr6a+gO0r5ugoCBN22JERITm5da6zYaEhODMmTOoXr06AKRLJDdu3Ihy5cqlO3Kc2ffn4sWLmvoDtO93O3TogO3bt6NXr17plvnzzz+HiGDmzJno3bu3wfVULx/pWbdunXptlNbxefn6vsy2Wf2YaDlCa0l4AXAOunTpEmxtbdVEJbf2aWp79+6FnZ1dto96MIcDBw7AwcEh3c4jJ5w5cwZ79uxJlxQWLVoUAPDgwQPodLpMb6Nct24dHBwcULt27dcUsfFy8vtoju/Zn3/+CRsbGzRq1Mhkfb4sLi4Ohw4dMvhelC9fPt1pg5zq73XTus1GR0fD3t5e823E2TGmv5z8nr/KPq1FixYICwtTbxN/2bFjx1C2bFmkpKSYKkyzYzJDRDnq9u3bEBH1EDi9GnOMI9fNf9Pff/+N+Ph4NG7cOMP34+PjcfDgQdSqVes1R/bqWDTPAmT3cMd/2+exY8cwb9489dDpyZMn0adPH/Tq1QsbNmww6Xxfl8z+o9CfJwdSb3nv0aMHBg0apBau0nv48KGmooLmWDfmkFGRsoykLY5miqKCaT148ADvvPMOgoKC8NFHHyE5ORk9evSAr68v8ufPj2rVqqkXemol/6LoWVovF5HUUsDtzp07iIqKQlxcHIDUP/wTJ07E+PHj1SKYgOm3L3OMozn6NHa5tWyzAHD9+vUMH5CYmJio6Tv+MlP2V6pUKYwePTrdNXgZ0VpcT0/r+GhRo0aNTBMZIPWCaEtKZADw1mxLoLUA16v0uXz5crGyshJPT09xcXGRzZs3i7u7u9SvX18aNWokVlZWsnDhQpPO25xiY2OlTZs2Ym9vL/ny5ZOvv/5akpKS1Pf1t0YuXLhQrKysJCIiQqpXry729vayYMGCdO2yY451o/fixQv5/PPPpVChQlKxYkWZO3euwftaYxQxvoibqYoKptW1a1cpWbKkTJ8+XWrVqiWtWrWS0qVLy86dO2X37t1SsWJF6dSpk+b+jCl6pnW5tRYoi4qKUm+T9vX1lWPHjom/v7+EhIRIkSJFxM7OTjZs2GCW7cvU42iOPo1Zbq3b7M2bN6VixYqi0+nEyspKOnXqJI8fP07XTitT9yeSegu3p6enWFlZSaNGjWT58uUZFpHTWlzPmPExlZSUFJP19TrxAuBcYMCAAVm+f/fuXbP1OXbsWIwcORJDhw5V70QaMGAAhg0bBiD1osRJkyapdz3kdsOGDcOxY8fw22+/4dGjRxgzZgwOHTqEFStWqBdAigi++eYbTJ06FR9//DEAYPny5ejatSueP39uUBDMHOtGq8jISPz6668YOHAgHj16hP79+2Pv3r348ccf1TZi5FliLcXRAODHH3/E7NmzMy0q6O/vjxkzZhgUnsvO+vXrsXz5clSrVg1t2rSBr68vNmzYgPDwcADA1KlT8e6772ru74svvkCZMmWwZs0azJ07F82aNUNISAj27NmjFj0bNWoUfvvtN83L/cUXX0BEsHLlSixcuBDNmjWDlZUVrl27phYoi4yMxNGjR9GlSxeMHz8es2bNQkREBFq2bInvv/8eQOrFniNHjsTz589Nvn2ZehzN0acx+xWt2+zgwYNhZWWFffv24dGjRxgyZAhq166NTZs2qXcdGbM9mLo/vePHj2P//v2YO3cu2rVrBw8PD3Tq1Andu3dXL9j/8ssv8fbbbxsU16tVq5ZaXC8treNjjISEBHz55Zc4ePAgmjVrhs8//xxjxoxRH53TsmVLzJo1y2KuqwLAIzO5gU6nk3Llyknt2rUz/KlQoYLRmbfWPp2cnCQ6OlpEUjNyGxsbOX78uNrPxYsXxdnZ2ZSLa1aBgYESFRWlvr53755UrlxZGjZsKM+fP1f/i3FycpJLly4ZfDYqKkpcXFxk5syZajtzrButChcuLGvWrFFfX7hwQUJCQqRLly6SkpJi8B+Z1iJlWoujaS0qaAxHR0e5fPmy+trGxkb++ecf9fWlS5fUIm5aGFv0TMtyay1Q5urqKhcuXBARkcTERLG2tlZjEUktdOnm5maW7cvU42iOPo1Zbq3brJ+fn0E5/ufPn0vLli0lLCxM7t+/b/QRClP3J2JYXE9EJCYmRsaOHSshISGi0+mkatWq8vPPP2surmfM+Bijf//+4ufnJ5999pkUK1ZMPvroIwkMDJQFCxbIokWLpHDhwvLxxx8b1WdOYzKTCxQpUkR+++23TN8/cuSI0V9WrX36+PiolVEfPHggiqIYbDj79+8XHx8fzfOdP3++upM3hZEjR8r27ds1t3N0dEyXpMTFxUnVqlWlbt26cunSpXR/tNLatm2bODs7y9ChQ0Wn05l83WhdHhERBwcH9Q+C3o0bN6RIkSLy/vvvy40bN9R5Ozo6ymeffSa//PJLhj8jR44UnU4nwcHB6aqXZrQ8np6ecurUKXW6v7+/wR+78+fPG/1HuEyZMvL999+LiMhff/0lLi4uMnnyZPX9mTNnSsmSJTX1NXLkSLGzszNIuJydnQ2+e1evXhU7OzsREc3LbW9vL1evXlWnOzk5qZVaRUSuXLkiDg4OkjdvXjW5i4+PF51OZ/B9OnbsmOTNm9fk25eIacfRHH2OHDlS8uTJo3m5tW6zTk5O6U4bJiYmqqfEjh8/rnlbHDlypDg4OJisP720z0d6WVRUlHTo0EGcnJzEw8NDjh07lq7NpEmTxN3dXVasWGGwbWsZHy30++eAgADZtGmTiKQmljqdTlatWqW227hxowQFBWnqM7dgMpPDFEURb29vad++faZtjh49KoqimKXPDh06SOXKlWXBggXSvHlzady4sVSpUkVOnz4tZ86ckVq1aknr1q2Nmretra307ds3y3bBwcHSrVu3bB90FxwcLA4ODtKsWTNN7ZycnGTt2rXp3n/8+LFUrVpVypQpoz7M8euvv86wL/31EDqdTt577z3p169fpvM1dt0YszzOzs6yZMmSdO/duHFDQkNDpX79+upOrFq1ajJt2rQs49TpdPLOO+/IoEGDsmynKIqEh4dnOG+9NWvWaP7jpiiK1KlTR0aPHi1WVlZSuHBhsbe3l+XLl4ufn5+0bdtW2rVrJ7a2tuof1OwEBweLoihSrVo1ddoPP/ygPgxQROTQoUPqH0ytyx0YGGjw3/oXX3wh9+/fN2iXN29eadmypTRr1kx27twpH3zwgVSoUEEiIiLkyZMnEh8fL61bt5bGjRubZfsqVqyYycbRXOvGyspK3N3dNS13kSJFNG2zpUqVkuXLl6drp09AAgMDNf9R139/ypcvb5L+RFLHEoBs3Lgxy3axsbFSo0YNmTlzZobvT5w4Uezs7NR5ax0frTHa2tqKtbW1wT8CNjY2Bkdeo6OjxdHRUVOfuQWTmRw2b948+eyzz6RcuXI50uetW7ekfv364uzsLE2aNJHY2Fjp27ev+oyekJAQo4+0REdHy6xZs7JsM3z4cOnSpYsULFgw2/6ePXsm69ev19SuRYsWmf5xiIuLk8qVK4tOp5Nt27bJ2LFjM+0rKipKunTpIjExMQZHJExBy/IMHz5cQkJCxMXFJcP3r1+/LoULF1Z3YpGRkTJixIhM+7t69ap06dJFTp48KQcOHMi03YsXL+Ty5cuyc+dOg9MmL5sxY4ZMnz49y2XQmzdvnowYMUKqVasmf//9t3zzzTeye/duEUl9Tk7Hjh3lnXfekV9++UVTf3o9evSQTz/9NNP3x40bJ02bNlXno2W5W7RokWVS+P3330vdunXl3LlzUrhwYVEURUqUKCE3btyQFi1aiLW1tVhbW4uXl5ccOnTI5NuXfixLlixpknE017q5fPmylC1bVtNyf/zxx5q22UGDBknDhg0zbJeYmCgtWrQwKvkYMGBApvvIV+lv3rx5EhYWJpUrV8627Zw5c6RDhw6Zvj9hwgQJDg4WEe3jo1V0dLR4e3ur/6zs379fbG1tDW4wWLJkifpMNUvBZIYydPHiRfnnn39y7HHur+rBgwcG/2G87PHjx7Jt27bXGNGru3z5cpZJz82bN43+I/MmuXTpkty8edOkfe7fv9/gWpJ79+4ZvL9582ZZs2ZNuukvs9Tt69/KaLm1brOJiYkSGxubabukpCSj/vEwdX/mYo592tSpU8Xe3l7q168vHh4eMn36dPHx8ZFBgwbJ4MGDxc3NTUaNGvVvQ3+tWDSPTCYpKQk3b940eMbIvxEfH49Dhw6hZs2aJmn3upw/fx67d+/GrVu3oCgKvL29Ua1aNYSEhOR0aBbp0aNHWLZsGa5evYqgoCC0adNGfc4Q5S65bVvUe/jwIS5cuABfX99cXR3dnF7ePy9cuBB79+5F9erV8e6772Lbtm34+uuv8fTpUzRv3hzDhg2DTmdBpehyOpv6L0tISDB4feHCBfn000+ladOm0r17d/UCOVO6cOGC1KlTx+jPXbt2zaDGgt6LFy80X7Cqvy7j8OHDBhes/fbbb1KtWjXx9/eX8PBwWbx4sVH9maqduT169EhatGghiqKIu7u7hIaGSkhIiLi7u6vX6WT1n6De1atXpWvXrq8hYvN7+fv4559/Svfu3eXzzz+X06dPG7R98OCB1KlTR9555x35/fffRST1VEfevHnFy8tLKleuLN7e3uLj42NwcXJOSk5OznR62msSTLF9pR1LLeNoTH+m6vPlbdEUy22sIUOGSHx8vDqfnj17qnfy6XQ6eeutt+TZs2cmnafW/W5O7qtyy37SXJjMmFHaK9uPHDkijo6OEhYWJj179pSKFSuKra2twcWGpmDsF9aUhaP08y5btqxs3bpVRFLPDTs4OMgnn3wiM2fOlH79+omzs7P8/PPPJluW3LKRduzYUUqVKiV79+5N997evXuldOnSmgqP5ZblMYW0y6K1UGHaYndNmjSR9957T/3H4MWLF9K9e/dMr514XXKi0Jt+LM1R8NHUfZqjIJ1Wafe7kZGR4uXlJb///rvcuHFD1qxZI/nz5zf5KRRj9lXG3DBgSv+l/UpGWDTPjCTNGbxhw4ahadOm+N///gdFUQAA3bp1w/Dhw7Fu3TrNfWot/qWVMYWj9E+QzcyzZ88AAGfPnlWf2PrDDz9g2rRpBsXVKlasiMjISAwcODDL/vQl5dM+7j6rdjlt9erV2LBhg8FTlvUqV66MH3/8EY0bN8bq1auz7OfSpUvmCtHkjPk+ai1UGB8frx7ePnr0KNauXasWB7OxscGgQYPUJwjnFHMUetM6llrH0RzrRuu2aK6CdFqk7XfZsmUYP3483n77bQCpT0+fMmUKRowYoRbw00LrWOrnk5nY2Fh1/29qWvfPeseOHcOaNWuQJ08etG3bFnnz5lXfi4uLQ79+/TB37lyzxGoOTGZek6NHj2LJkiUGX+RPP/3U6Kfv9uvXD76+vuoO82UvP+MjO5s3b8bKlStRoUIFAKnP7Hj33XdRt25dbNmyBQDUmE+dOoV27dqhQIECGfYVExODc+fOwcHBAXfv3kVgYCBu3LiR7o975cqVER0dDUVR0Lt3b5QqVSrD/q5cuYKRI0ciISFBU7vcIKsdlf69Vq1aQVGULHfm5trhmZox38dz586hWbNm6uvWrVsjb968aNGiBRITE/HWW28BAEqXLo2tW7eiUKFC8PHxwZUrVwyqol65cgUODg5mWiJtVq1ahfnz56tPLH/rrbcQERGB5s2bq8mqoihGbV9ax1LrOJpj3WjdFo1ZbnPQ933t2rV0iW+lSpVw5coVo/rTOpZr1qxBgwYN4O3tnWE7c/7jpXX/DAAbN25E8+bNERISgsePH2P48OH43//+hzp16gBITXzmz59vUckMTzOZkU6nkzt37oiISFBQkEEFTJHUuy3s7e019aUvtqa1+JfWPo0pHFW+fHn54Ycfsp13hw4dpHv37iIi0qZNG/nqq68M2o0dO1ZKlSqluTaK1nam1rVrV/n111+zbadfNx06dJDSpUtneAvwgQMHJCwsTDp27Ch+fn6ycuXKTPt7lSKJpqS18OHIkSPFx8dH8/dRa6HCP//8U/LkySPz5s2TefPmSXBwsPz000+ya9cumTt3rgQEBMjnn3/+6guYCWMKPpqj0JvWbVvrOBqzr9Dap9Zt0ZQF7oxx5coVURRFIiMj5dtvvxU/Pz/ZsWNHuhg9PDw095eUlKR5LEuVKiU//fRTtu20MLYAqdb9s4hI1apV5csvvxSR1ArNEydOFGdnZ1m3bp2ImPc0oLkwmTEj/YWgHh4eYmNjk+6Bchs2bFBrCWRHX2xNf/tcZow5J2ts4ahPP/00y7oeFy5ckNq1a8uNGzckODhYatasKQMGDBAHBwepXr269OzZU2rWrCm2traydu1azbVRtLbTSl8oLLsLsGvVqiXBwcFSunTpLNvp102jRo2kcePGoiiKeHh4SJEiRaRo0aLi4eEhOp1OmjRpIg8fPpTmzZvLsGHDMu3P2HWopfigMW2NKXyo0+mkUKFCmbZJuyxaCxWKpD6o0N/fX71wU/9jb28v/fr1M7g+Jbtl0bKu9W21Lrebm5vMnz8/3Xv/ptCb1sJ+WsdRa38i2teN1m3RlAXuRLSvR0VRxNraWvLly6c+quLl5Gvq1KlSpUoVzfMNDQ2VKlWqaBrLLl26SJ8+fTJtd+rUKc37fK3fR31bf3//LAul6vfPImLwOA69RYsWiZOTk6xevZrJDBl6uaT8yxeGjhw5Uvr376+5v2fPnsmPP/6oqfiXVqYuHKX38OFD+eKLL6R48eJib28vtra2EhQUJO+9916W8b8OaQuFaXHmzJls26QthHf69GmZO3eujB07VsaOHStz5841uDtkx44d6n9AGXny5InmuhHGFB80pq2WwociIocPH5bvvvsu0/fTfh+1FirUS0pKkv3798uSJUtk0aJFEhUVZVDhVwtj17XWgo/FihXL9FlFr1roTWthP63jqLU/EePXTXZMXeBO63rctm2bzJs3L8s/6nv27JHDhw9rmq++v6ZNm2oay+fPn6t3UpmC1u3Q2O+5l5dXhonhkiVLxNHRUWbOnGlxyQzrzLzhkpKS8PTp00yfjpqcnIzr168jKCjoNUdGlHs9fPgQN2/eRIkSJTJ8/8mTJzh06BDCw8PfyO2L+5XcrWHDhmjYsGGGN2EsXrwYnTt3RnJycq65uUILJjO5lLmLPL1qcas7d+7g5MmTKF++PFxdXXH79m3Mnz8fKSkpiIiIyPTCwMwcO3YMhw8fRu3atVGgQAGcPHkSM2bMQEpKCt566y31Ammt7TKTlJQEa+v/u949ISEBOp0ONjY2AICLFy9i7ty5amG27t27G1xI97oK4YnIa734NykpCVFRUepy16lTB1ZWVunaGVu47vbt2xAR+Pj4ZBvD3bt34e7urq6L7Dx8+BBr1qxBp06dNLU35bxzgtax1Los/3bd/Ntt8XUQEVy+fBkBAQGwtrbGixcvsHLlSiQkJKBp06YGd+6klZCQgOvXr8Pf3x92dnYmi+f27dtISEjItKCo1u3w5c+k3adlJ+0+f+XKldixYwemTp2aYdvFixdj9uzZiIqK0tx/jsuxY0JviDlz5kinTp3U514sWbJEihYtKgUKFFDPUZuyyNOpU6ekQIEC2bbL7KLZBw8eyNSpU6VPnz4yevRogycI68+dK4oivr6+cuzYMfH395eQkBApUqSI2NnZyYYNG7Kdt77A1PLly8XKyko8PT3FxcVFNm/eLO7u7lK/fn1p1KiRWFlZycKFCzW3ExFZt26deqF1cnKyjB49Wvz8/ESn00n+/Pll3LhxkpKSInXq1FELs+3cuVPs7OykdOnS8u6770rZsmXF0dFRdu/ebbJCeGk9f/5cBgwYIDVr1pSJEyeKiMjo0aPFyclJHB0dpX379hn2mdW6yY6+EN/HH38sf/75p4ikFjQrWrSoWFlZibe3t1hZWUmpUqXk+vXrmgvX3b9/X95++20JDAyUPn36SFJSknTv3l397latWlV9pMCPP/4oz58/F5HUiw4jIyPVcXR0dJT+/ftnWoQuLa0XfKctZGaqeWdW0PDFixeycuVKmThxovz222/y5MmTDD+f1TrUOpZal8Uc68aYbVHrcmdHvx7Pnj0rKSkp6vS///5bWrZsKcWLF5d69eqpT30+c+aMBAUFiU6nk8KFC8ulS5ekfPny6valr2M0b9489aLnZ8+eSffu3cXKykp0Op1YW1vLhx9+qI5JdvT73bi4OHn//fclMDBQOnXqJAkJCdKnTx91zGvWrCmxsbGat0MR7fu07PzX68wwmTGjqVOnipOTk7z99tvi6+srY8aMEU9PTxkzZoyMGjVK3Nzc5McffzRpkSdjC835+vqqz5G5dOmS+Pj4iI+PjzRo0ED8/f3Fzc1Nvd4jPDxcPvroI3n8+LFMmjRJ/P395aOPPlL7HDhwoKZztvp5lytXTsaMGSMiIosXLxZ3d3eD5fzmm28kLCxMczsRkeLFi8uuXbtEJPWuKU9PT5kyZYqsW7dOpk2bJt7e3jJ+/Hhxd3dXL4CrVatWumuXvvrqKwkPDzdZIby0+vfvL35+fvLZZ59JsWLF5KOPPpLAwEBZsGCBLFq0SAoXLiwff/yxUesmO2nXt756btu2baV+/fpy9+5dEUn9Y9qsWTNp3bq15sJ1Xbt2lZIlS8r06dOlVq1a6t0qO3fulN27d0vFihXV8Un7PZ81a5Y4OTnJ5MmTZdeuXTJ9+nRxc3OT6dOnS2xsbJY/f//9t9FF4bTOW2ufVatWlYcPH4qIyJ07d6RUqVJia2srISEhYm9vL4GBgXL9+nWj1qHWsdS6LOZYN1q3RXN8d9PGGBUVJTqdTpo3by6RkZHyzjvviE6nk/Xr10vLli2lRYsWcvz4cenXr58UL15cWrZsKS9evJCEhARp2bKldOjQQQoXLqxeBzNw4EAJDg6WFStWyOnTp2XVqlUSGhqq+a45fYx9+/aVokWLynfffSe1a9eWli1bSsmSJWXnzp2yY8cOKVmypHz55Zeat0MR7fs0rTH+VzGZMaOiRYuq/6UcPnxYrK2tDW7bmzt3rpQvX14URVE30rCwsHTVcZcuXSrFihUTkdQ/hFn9dOjQQXQ6nXh4eGT54+rqqh790c+7Xbt2Urt2bfUo0fPnzw02qrRXwCcmJoq1tbXBk5XPnTsnbm5u8u2332b5M2jQIPX2zejoaBFJ/W/QxsbG4Pb1ixcvirOzs+Z2IiL29vbqf30lS5ZMdzvln3/+KYULFxYnJyd1Z+rt7S1Hjx41aHfhwgVxdnYWNze3DBMZvT179oibm1um72ckICBANm3apMau0+nU/ypFRDZu3ChBQUFGrZs//vgjy5+pU6eKTqcTe3t79ZZif3//dBWo//nnH8mbN684ODio69rX1zfdBZNnz54VNzc38fX1VXe0t27dEkVRZOPGjWq7nTt3Sv78+UVEDJanYsWKMmXKFIM+58yZI6VLl1b/i83sR/++1u+ZMfPWOo5p++vZs6eEhYVJTEyMiKQ+fLJatWrSrVs3o9ah1rHUuizmWDdat0VjllvrekzbZ7169dLdNTR48GCpWbOmeHl5qfulJ0+eiKIo8vfff6vtdu/eLYGBgWJnZ6c+ciI0NDTdRfnbt2+XwMBAEdG+3w0ICFCrn9+4cUMURZHVq1erfa5du1aKFCmieTsU0b5P07rPF0m9kzbtwz4XLlwoZcqUEUdHRylUqJB8++23YmlYNM+Mrly5gurVqwMAypYtCysrK1SpUkV9v0aNGhgwYAAA7UWevv32W4SFhWV6Yd2TJ08AaC9ulda+ffvw008/wdHREQBgZ2eHr776Cq1btwYA2Nra4vnz5wBSi0SlpKSor4HUQks2NjaaC0y5uLjg/v37CA4OxqNHj5CUlIT79++r7e7fvw9nZ2f1dy3tPDw8cOPGDQQEBODu3bvprmkJDQ3FjRs3ULVqVaxZswZFixZFoUKFcOzYMZQpU0Ztd/ToUeTJkyfbip2vcn3LvXv3EBoaCgAoWLAgrKysULhwYfX9kJAQ3L171+Az2a0brYX4QkNDsX//fhQoUAAuLi6Ii4szaPP48WOkpKRoLlwXGxuL/PnzAwC8vb1hbW0NX19ftZ2fnx8ePXpkEAMAREdHo169egbzrlu3Lvr37w8XFxcMHTo0w0rKQOr1Sx9++KHRBSS1zPtVChpu374dU6ZMUa9B8fT0RGRkJLp27WrQLrt1aMxYalmWlJQUs6wbLdtifHy85uV+lUKgp06dQmRkpMG0jh07Ys6cOXj69KlaqdjJyQlOTk4Gy+3v74/bt2/Dx8cHFy9eRGBgIOLj49NdR+Pl5aUum9b97p07d9Rt2c/PDw4ODihSpIjarkSJErh27Zrm7RDQvk/TWoQUAJo0aYKYmBjky5cPv//+Ozp16oQ+ffpg0KBBOHz4MAYNGgQvLy+0b98+w75ypZzOpv7LPD09DR6I5+/vb3Db9Pnz59X/YrQWeSpSpIj89ttvmc5TXxhJa3ErRVHUwn5+fn7pHjUfHR0tdnZ2IpJai6JZs2ayc+dO+eCDD6RChQoSEREhT548kfj4eGndurU0btxYc4GpDh06SOXKlWXBggXSvHlzady4sVSpUkVOnz4tZ86ckVq1aknr1q01txMR6dOnjzRr1kySkpLkgw8+kB49ehicT/7kk0+katWqsnv3bnFzc5Phw4fL9OnTJW/evPLVV1/JwoUL5euvvxZ3d3eZMGGC5kJ4WulrlOiLW+3fv19sbW3Va6pEUq+rCgkJMWrdaC3EN2/ePPH395eoqCj59ddfpVixYrJ582a5ceOGbN26VUqVKiU9evTQXLiuTJky8v3334uIyF9//SUuLi4yefJkdb4zZ86UkiVLikjqf/+//vqr/PHHHxIQEJDuiNeJEyfE1dVVateuLRMmTMh0WfQ1PYwpCqd13lrHMe26yZcvn5w8edKg3eXLl8XOzs6odah1LLUuiznWjdZt0Zjl1roeFUWRCxcuSGxsrBQsWNDgqLBI6v5Uf2Qh7ZGYH374weCW/kOHDomPj498+eWX6unCwYMHS/PmzdXnR8XHx0vbtm3V28u17nf9/Pzk0KFD6vT27durR5P04+jh4aF5OxTRvk8zprho2qNc4eHh6WoMTZo0SSpWrJhpX7kRkxkzCg8PlyVLlmT6/po1a6RkyZISFBSkFnjKrsjTe++9J/369cu0T/2OXmtxK0VRpFSpUlK2bFlxdnaWFStWGLTbvn27eij63LlzUrhwYVEURUqUKCE3btyQFi1aiLW1tVhbW4uXl5ccOnRIc7GuW7duSf369cXZ2VmaNGkisbGx0rdvX/U0QkhIiFy4cEFzO5HUJ1dXqFBBChcuLB07dhR7e3sJCgqSBg0aSIECBcTV1VXdUe/evVuqVKliUJRNURTJnz+/ug4ePnyoqRCeVsOHD5eKFSuKoihSv3598fDwkOnTp6vFEAcPHixubm4yatQoo9aNMYX4Jk+eLI6OjuLg4CC2trYGp3BatWql7tC1FK5bsGCBWFlZSeHChcXe3l6WL18ufn5+0rZtW2nXrp3Y2tqqf1BfHufIyEiDGOfMmSNly5aV2bNnZ3mY+9atWzJixAijisJpnbfWcVQURZo2bSpvvfWWeHh4yF9//WXQbs+ePeLt7W3UOtQ6llqXxRzrRuu2aMxya12PaU8/KoqSrtLuqlWrJCQkRD788EOZM2dOpv2NGzdOmjZtKgkJCdKiRQvx8PCQBg0aiL29vTg6OkpISIg4OTlJYGCgnD17VkS073cbN26cZV2YefPmqdcVat0Ote7TjCkumjaZyZcvn0ECJvJ/p5EtCW/NNqNdu3bByckJYWFhGb7/ww8/ICUlBX379s2yn71798LOzg5ly5bFrVu3kJCQYLL6DC+faqpSpYrBrZWff/45rl+/jsWLF6vT7t+/D09PT/X1li1b8OzZM1StWhWenp44deoUnj59qj6X5WWJiYm4efNmpstw6dIlPH36FEWLFs3y1sPM2iUmJuLnn3/GmjVrcOnSJaSkpMDX1xfh4eHo3bt3ulvd7969a9AuODg43bzOnDmDPXv24NatWwAAHx8fVK1aFUWLFs00vqwsXLgQe/fuRfXq1fHuu+9i27Zt+Prrr/H06VM0b94cw4YNw+jRow0+k9W6+fvvvxEfH4/GjRtnOL/4+HgcPHgQtWrVApB6q/WmTZvSjc/Lh7CTk5Nx+PBhg3bly5eHi4uL2mbnzp3Yt28fqlWrhqpVq+LUqVMYP368uiydO3fWNCZ//vknbGxsNN/a+2+/ZxnN29HRUdM4/vLLLwbTmzZtijZt2qivP//8c/zzzz+oWrWqQbvsti9TjGXacXxd6+blbdGY/YrW9Xj58mWD6b6+vurpWiD1VNCLFy/w+eefZ7ks0dHRsLe3V089rV+/PsN9xXvvvQcnJycA0LzfffDgAXQ6Hdzd3TN8f926dXBwcFCf6aV1OzR2n5YdnU6HrVu3Ik+ePGjRogWWL19uMP5nzpxBxYoV8fjxY6P6zUlMZoiITCw+Ph5WVlawt7fP6VCI0tHpdAbXhk2dOhWffvqp+v7ixYsxZswYnDx5MqdCNJoupwOgVCKC6OhoJCUlAUi96G3p0qX49ddfce/evVfq89ixY5g3bx6io6MBACdPnkSfPn3Qq1cvbNiwIcvP6uPIiP7CtIymX7161eg4r1+/rl5Al1ZiYiJ27NhhdDtTuH37NkaNGmXSPq9du4Zu3bqZtE8tjPl/JT4+XtNYPnz4EL/++uu/CQuPHj3CnDlzMGzYMPz000+IjY39V/1p8XJF03379mHHjh1ITEw06XycnJyyTGSy2r6MZY5xzK7PV90WTbnclkjr9qW13auKjo7GpUuXEB0djejoaHTo0MHg/cTERHzxxRdmm79Z5OApLvr/tBZ5MobW4lbGFGSKjY2VNm3aiL29veTLl0++/vprgwf+Gftwsps3b0rFihVFp9OJlZWVdOrUST1PnLY/re20+LdFBf+NtH0ePXpURo8eLTNmzFDrS+jFxsaqhdk2btwoX3/9tWzZskVEUq81aNy4sdSpU8fgouFXLcSXVYxa2/3555/SvXt3+fzzz9PVDnnw4IFauE5rIT5TSbuub968KeHh4WJlZSU1a9aUBw8eSEREhHotRmhoqFpALiv6Am6HDx82eGr2b7/9JtWqVRN/f38JDw+XxYsXi4jxBc+0jKUx42jqdaN1WzRVoTcR7dusqZl7H2CqeefU+OQ2TGZyAa1FnoyhtbiVMQWZPvnkEwkNDZVly5bJnDlzJCgoSCIiItRiavpaFlp16tRJqlSpIgcOHJBNmzZJhQoVpHz58vLgwQOD/rS200K/kzh27FiWP0uXLjV6R6a1RsmGDRvE1tZWSpQoIYGBgZI3b161NoV+eXQ6nfz2229ibW0t5cqVE2dnZ5k3b564u7tLjx49pHv37mJrayvLli0TEe2F+LSOj9bCdQsXLhQrKyuJiIiQ6tWri729vSxYsCDdsoiI5kJ8ppL2D0LHjh2lWrVqsnr1ann33XelWrVqUqNGDbl+/bpcvXpVatSoYVAAMrs+y5Ytq66zOXPmiIODg3zyyScyc+ZM6devnzg7O8vPP/9s1PaldSy1jqM51o3WbdFUhd5eXo+vkzFPrzemT1MnM6YYn8TERNm4caP89NNPsmnTJs1Ppc9NeM1MLpAvXz5s3LgRYWFhiI+Ph4uLC3bs2KHWqNmzZw/atWun1prRwtnZGSdOnEBwcDBEBHZ2djh06JBag+DSpUsoU6YMkpKScO7cOQQEBKBUqVIYNmwY2rZtq/azdu1a9OvXD+fPn0dQUBDmz5+vXrx2//59REREwM3NDatXr8ajR4/g5+en+eFk+fPnx8qVK9W6OgkJCXj33Xdx5coVbNmyBYmJifDz84OPj4+mdsnJyWrdnszcvXsXixYtUp+BlNHXXz9dURSjHrT28nnojCiKgsqVK6NOnTqIjIyEiOCbb77BqFGjsGzZMjRu3Bi3b9+Gn58fSpcuja5du+KTTz7Bli1b0Lx5c0RGRqJ///4AgClTpmDFihXYuXMnAgMDMXfuXNSvXx+XLl1CSEgIVqxYgZYtWwIANm3ahJ49e6arZ/Gy5ORkPHnyJNtnROnfL1OmDLp27YqPP/4YALB8+XJ07doV06ZNQ/fu3dVlSU5OhqOjI/755x8UKlQIfn5+WLt2rUHtmnPnzqFSpUoGtU+yonVdJycnw8/PDytWrECVKlXw4MED5M2bF5s2bVLrqURFRaFHjx4G1w1k5MaNG/jmm29gb2+P06dPIzAwEOXKlUOvXr3wwQcfqO0WLVqEyMhIXLp0SfP2Va5cOU1jaWdnp2kcCxYsaPJ14+TkpGlbtLW11bzcxqxHU3r77bezfD82Nhbbtm0zar76+jaZ0W9fWT3XLG07Y/ZpxsT5ySefoFGjRoiIiMD169fRoEEDnD9/Hnnz5sW9e/dQvHhxrFu3Tq1TZAlYNC8XePLkiaYiT8bQWtxKURRNBZmA1GJvaa/m9/T0xKZNm9CoUSM0bdoUP/30k1ExxsbGwsPDQ31tZ2eH5cuXo02bNqhTpw4WLFhgVDtAe3ErT09PTJgwIV1xML2TJ0+iefPmRi2Pr68vZsyYgVatWmX4/tGjR1G+fHmcPHkSv/32G4DU5Obzzz+Hv78/WrdujcWLF6t/KM6fP6/GUK9ePSQlJRnEGxERgbFjxwIwrhCflsJaWgvXnTt3Ds2aNVOnt27dGnnz5kWLFi2QmJiIt956S31PayE+rbSuayD1Gh/9jjlPnjxwdHQ0+C4XKlQIMTExmgu4OTg44O7duwgMDMSNGzfSjVPlypURHR2tueAZAM1jqXUczbFutG6Lxiy3MevRlNasWYMGDRrA29s7w/dfJXnSWqzUmKKm5hif5cuXo3fv3gCAzz77DP7+/vj777+RN29ePHjwAJ07d0a/fv2wbNkyo/vOMTl1SIhSjRw5UvLnz6+pyJMxfTZo0EBTcSutBZlEUgtHrV27Nt38Hj9+LFWrVpUyZcpoPtw5f/58KVKkiCxfvjzde4mJidKqVSsJDAwUnU4npUqV0tROH6OW4laNGjWS0aNHZ9rOmEPMwcHB0q1bN2nQoIGmGiVeXl5y8ODBdO8vWbJEHB0dZebMmaLT6cTd3V3OnDmjvu/s7CwXL15UX1+6dEkcHR1FJHW59TWNsirEp7WwltbCdb6+vurD+tLatm2bODs7y9ChQw2urdFSiE8LRVHE0dExy2eWpS2aFxgYaFAy/osvvpD79+8bLE/evHmNKvjYvXt3ERFp06aNfPXVVwbtxo4dK6VKlTJq+9I6llrH0RzrRuu2aOx+Rcs2q8XIkSNl+/bt2bZTFEWcnJzSrbdXna9+3iVLltS0fRlT4M6U46PfV9nZ2Wl+nIKlYDKTw4KDg8XKykpKly6daRt9kSdj+rS3t5e8efNmW9zKmCJzH3/8sVpp92VxcXFSuXJlzRuVPo6AgIAM309MTJQWLVqITqeTQYMGZXotRdp2ItqLW61YsSLLHcSDBw/kl19+0bQsw4cPly5duoivr2+657uk9eTJE9m2bZs0aNBAJk2alGGbRYsWiY2Njeh0OqlQoYLBM5tiY2MN/iBs2rRJQkNDRSS1sKK9vX22hfi0FtbSWriuZcuW6aqH6umfsp72O6GlEJ8W8+bNk1KlSomvr2+mbdImpC1atMjyj8f3338vdevW1VzA7caNGxIcHCw1a9aUAQMGiIODg1SvXl169uwpNWvWFFtbW1m7dq1R25cxY6llHM2xbrRui8Yst9ZtVovg4GBxcHCQZs2aZdlu3rx5EhYWluU/iadOnZLg4GBN89XP29raWt0mM6LfvowpcGfK8dHvq2xtbdV/fooVK6Y+K05v9+7dkidPHk195hZMZnKBZ8+eyfr16zN9/9KlS5rutNDS58WLF+Wff/4xeMjYixcvZObMmdK0aVMpWrSohIaGSq1ateTLL7+Ua9euqe0ePHiQrix5Wo8fP5Zt27ZpjvH8+fMyderUTN9PSkqSy5cvS2JiYpZ34ujbiYjExMQYPDIiN1qxYkWWO6dFixZJ7dq1ZcWKFVn+lzlu3DiD/ywXLFggffv2VXdSUVFRUqNGDSlfvryMGDFCkpOTTbcQ/9+2bdtk7Nixmb4fFRWl7pT1kpKSZP/+/bJkyRJZtGiRREVFGRyJ1MqU63r//v3yzz//yMmTJzN8dIXeixcv1Hk+fPhQvvjiCylevLjY29uLra2tBAUFyXvvvWfQh9bty9ixzG4czbFujNkWtS63qbfZ7Panes+fP1cffvm6520Mc+zTjHmcgqXgBcBEr4Fkc0EtEdHrNGXKFAwbNgwiguTkZIMaQC1atMBvv/2mPsDXEjCZyUHx8fE4dOgQatasabY+r1+/Dnd393RfysTEROzZs+dfzTsxMRFr167F+fPn4evri7feekst/52Ru3fvwt3dHTY2Nhm+//DhQ8yfP1/tr3PnzggICHjldq/DtWvXMHz4cMydOxcJCQn48ssvcfDgQTRr1gyff/45xowZg/Hjx0NE0LJlS8yaNSvTC/n+S7Jb11l58eKFwQW4Fy9exPTp09X13bt3b5QvX96U4eZq/2Yszd1fTm+LDx8+xIULF+Dr66u5pP/FixfRs2dPbN261ej5nT9/Hrt378atW7egKAq8vb1RrVq1dBc450Zp91V6Wh+nYBFy8KjQG8+cRZlMWWhOT/+EWRGRO3fuSKlSpcTW1lZCQkLE3t5eAgMD5fr16/Ljjz/K8+fPRUQkJSVFIiMjxd3dXXQ6nTg6Okr//v0lOTlZfH195d69eyKSeirNx8dHfHx8pEGDBuLv7y9ubm5y+vRpze1yQtp1qLXWy4YNGwxO8y1cuFDKlCmjPvE3q2tVsotFSyE+U9K6ro2h0+nUh+AdOXJEHB0dJSwsTHr27CkVK1YUW1vbdBcs/heYeizNsW5yclscMmSIelroxYsX0rNnT/X6Hp1OJ2+99ZY8e/Ys235eZb/76NEjadGihSiKIu7u7hIaGiohISHqeLZs2VJTUcqclLbGljHr/cSJEwb7q9yKyUwOMmcyY8pCc3ppn7Tas2dPCQsLk5iYGBERuXfvnlSrVk26detm8Mdo1qxZ4uTkJJMnT5Zdu3bJ9OnTxc3NTaZPn27QX7t27aR27drqzur58+fSrFkzad26teZ25qC1EJ6ISEBAgHoh3cWLF0Wn0xlcwLtx40a10rN+efSVmj/++GNZuHChfPbZZ2JnZyeLFi0yKk6thfhMTeu6Nkba9a1ft2kvfO7atas0btzYdAuRS5h6LM29bl73tph2eSIjI8XLy0t+//13uXHjhqxZs0by588vo0aNkm+//TbLn0GDBhm9LXTs2FFKlSqlXrSc1t69e6V06dLSqVMnkyznq9K6r9LpdHLnzh3N/bq4uBjcRZlbMZkxIw8Pjyx/XF1djd6otPbp5+dn8N/r8+fPpWXLlhIWFib3799/pT9uaXdkoaGh8ueffxq8HxUVJcHBwQbtKlasKFOmTDFoN2fOHCldurRBuwIFCqgl+/X27t0r/v7+mtuZg/6/vrR3d7z8ox9HBwcHuXLlivpZGxsbgwumo6OjxdHR0WB5wsPD091xMmnSJKlYsaJRcVatWlW+/PJLEUn9L3zixIni7Oys3l1lrmRG67p+1T79/f1l586dBu8fPXpUvL29/0XUuZOpx9Lc6yYntkX9vMPCwuTnn382eH/p0qVSrFgxURRF/Pz8JDg4OMMf/WMVjOHm5pZhIqO3Z88ecXNzM3qZTEnrvkpRFPnwww+lf//+mn7s7OwsIplh0TwzMqYwkqn7NKbQnDH0F7E+evQIBQoUMHivQIECiImJMWgXHR2drjBd3bp11Sq2+nYJCQnpild5e3urhd60tjOlq1evai6EBwCBgYHYs2cPAgMDceDAASiKgv3796NEiRIAUh9qmD9/fly4cEH9/Pnz5/Hdd98Z9NmiRQuMGTPGqFi1FuIzB63r2pj+9H1aWVmlu8bI1dX1tTyUMieYYyxN2V/aPl/ntvjyvK9du5buO12pUiVcuXIFQUFBmDBhgkHF4bTSbrOvMm9j33tdtO6ratasibNnz2rut2rVqkYVs8wpTGbMKCwsDAEBAejcuXOG7x87dkxzMqPT6VC7dm0ULlxYU58FCxbE8ePHDS7ksra2xrJly9CmTRuDyqDZ+fXXXxEeHg4A6NKlC+zs7JCYmIgrV66gePHiaruYmBi4u7vjzp07WL9+Pdzc3ODg4IBnz54Z9Pfs2TPodKkPbK9Xrx6sra0RFxeHc+fOqX/4gdRkIm/evLh586amdlqNGjUKtWvXzvbi5+DgYDg6OmLhwoWZ7iDSPr6gV69e6NKlC3766SccOnQIkydPxpdffokzZ85Ap9Nh5syZ+OyzzzB8+HCcOnUKt27dgoODQ7qnkKekpGiuPqpfN3Z2dukeA9C+fXvodDq0a9cOkydP1tQfkJqU1q1bF6NGjcqynLlOp4OIYObMmQgLC8t2XWudd0pKCkJCQqDT6fDkyRP8888/Bsn7+fPn4ePjo6k/retaP28ty60f80KFCmXZ344dO1CmTJlsS9en3b60bjdalkVEsGjRIhQoUMBk60ZEULNmTTg4OPzrbVHrOAJQn2A/Z84cODs7w87ODg8fPjRoExsbCzs7O5QvXx6HDh3KNJnJ7pEjaXXr1g116tRB8+bN0bNnT/z888+oUKGCQZuDBw+iV69eaNGihaY+tX7PtLYDUscnODgYhw8fznZftW3bNk1xWhomM2YUERGR5XNm8uTJg06dOmnqa+7cubhy5Qrmzp2rqc98+fJh9uzZeOeddwze1yc077zzDq5fv65p3l26dIGNjQ2KFCmCfPnyAQBatmyZroz277//jrCwMGzcuNEg2dqyZYtBufc9e/agUKFC6nOD9BwdHQ1er1mzBjVq1EDRokU1tdNq3rx5GD9+POrVq4c1a9Zk2i4qKgobNmzA7t27M21TuHBhREVFAQD69esHLy8v7N27Fz169MC7776LkiVL4uuvv8bTp0/Rv39/DB06FMOHD0e9evXUHequXbsMdpBHjhxBYGCgpmXRrxtvb29ERUWl+4/z3XffRUpKSqbJb0Y6d+6MK1euoGbNmrh48WKm7ebOnYuuXbti+PDh6n+mma1rY+a9ZcsWnD17FpMmTQKAdJ/fu3evQSn+rGhd1/p5a1lu/Zh/8MEHmD59eqbtateuDQ8PD3z55Zf47LPPsu1PRDRtN1p07twZI0eORL9+/Uy6bv744w9cuXJFfYbVv9kWtY4jkLoeAWDMmDHw/n/tnXdYVMf79u9dYGEpAgJSpCpYEBF7QEUpdgVjiWLsXaMIGo2KlUA0lmhi1FiJDRtovlGDiIpoVNSAFcRGsYJGBQWVts/7B789Lytt1uy6AudzXXtdnJ2HZ+bMzJmdM+UeU1OIRCIkJibKxBUbG4vGjRsjODgYb9++rdCXo6Mj0tLSmNKYmpqK2NhY6OjowMrKCu3atYOBgQHq1asHgUCArKws5OTkoHv37mVGWCuCtZ6x2gEl+fPkyRMUFRVxHb8PKd1W1UT4rdk1lKKiIrx9+7bCbcDFxcV49OiRzPk0lZGeno7o6GhMnDixQpu8vDyoqalBS0urUl9HjhyBhoYGunfvzhS3Mnj//j3i4uJUkoYPDwzV1dWFkZERd71jxw4AYO7opqenY9myZRCLxVi9enW5Nnv27MGmTZs+eWNWU8ua5XnIyMhAWloaoqOjsXTp0v/sT9F5+TmUDct9S6mqHOPj46GpqSlzppSiuH37Nho3boyUlBRcuHABmZmZAAAzMzO4urqWeeFSBaps0z4H+M4Mz2dHUVER1NWrHjRktVMWxAvh8fAAUP2zyFM5taGtYp8w5VE4r1694t7CK+P+/fvw9PTkrq9du4awsDBuqDQpKQlTpkzBpEmTEB0dXWFca9aswTfffIOQkBA8fPhQJjw3NxdxcXHYt28f9u/fj7i4uDLTSFeuXJEZnt21axc6dOgAKysrdOzYEXv37pWx/3Ddx8WLF3HmzBkUFhYCKFkfcOPGDQAl60RCQkJQv359aGpqwtLSkhObY7WriFevXuHy5csy02p37tyR+Z+///4b/fr1Q7NmzeDt7Y3//e9/AEoWOc6cOROdO3fmpj1CQkKgq6sLXV1dDB06FK9fv+b8XLt2DSEhIVi/fj3+/fdfmXS8fv0aY8aMKZO+oqIixMTEYOvWrThx4gTzepmioiI8ePCAyZaVhw8flpvGD/mwTkrJzs7G5s2bsWDBAmzZskWuhbql496yZQtGjhzJTS/s27cPTZs2RYMGDbBo0SImf3l5eThz5ozccVdGVXlORGXWQJVHVlZWlWVXWV7eunULYWFhSElJAQCkpKRg8uTJGDNmTIVicFWVDYvP//osPn/+nHv+P+Tu3bvYvn07fvzxRyxfvpwT4ysNESEtLY1Tqy0oKMC+ffuwY8eOMs9bRdy6dQsNGjQoN+zq1as4cOAA/v77b+Z1NZVRVbsrr11p+w/bNHnbqhrHJ907xSMDq85MaTupLomRkRHp6enRiRMnyMDAgLy9val79+6kpqZGu3fvZha3KiwsJH9/fxKLxSQQCEhTU5NEIhEJBAISi8U0ffp0KigoICKili1bctolmzdvJrFYTP7+/rRhwwYKCAggXV1d2rp1Kz158oQ6dOhAampq5O7uTi9fvqTevXtz2wMbNWpET548IUdHRzp37hwRlZwybGRkRD/99BNFRUXRmjVryNTUlJYtW8ZsR8QurFVasyI2NpaEQiH17duXQkNDacCAASQUCunYsWPMQnhE7Fov06ZN47a1P3z4kJo0aUJqampkampKampq1Lx5c3r06JFc9UJRQnzy1skBAwZQZGQkERElJSWRsbExmZiYUPv27cnU1JTMzMwoOTlZrrhXr15NOjo61L9/fzI3N6eQkBAyMjKikJAQCg4OJn19fdq4caPC7kUeW6ldYWEhBQUFkbu7O7e1fvny5aStrU0ikYhGjBhB+fn59Pr1a/r666/J2tqa+27KlClcfXR3d+fE1ljzMioqikQiEdWtW5e0tLQoKiqKTExMyNvbm7y8vEhdXZ1OnjwpV9mw+mR9FuUR7GMVpEtJSeF0muzt7Sk1NZVat25NOjo6pK2tTcbGxnTnzh3mMvTz8+POnXrz5g1169aNBAIB1/61adOGEwllRRlCoKxtmjxtVU2E78wokZycnEo/Z8+eJaFQKJfIU6tWrSgkJISIiPbs2UMGBgYUHBzMxbly5UpycXFhFrfy9/en+vXr0969e2Ue3FevXtHevXvJysqKpk+fTkRE2tranI5Ky5Yty/yg7N69mxwdHWn48OHk5uZGf/75Jw0ePJjc3NyoU6dO9OjRI3rw4AF16tSJvvnmG9LS0qIHDx4QEZGTkxPt27dPxt+RI0fI3t6e2Y6IXVirdP54eXnRlClTZHzOmTOH3N3dmYXwiNi1XszNzbkfka+++oq8vb05xd4XL14wC4+V/gFmFeJjFdZirZOlf0B69uxJQ4cOpfz8fCIqaXjHjh3LnbLMGneTJk1o9+7dRESUmJhI6urqtGXLFu6+t23bRq1bt5Yrf+QRP2TxOX/+fDI1NaUZM2aQo6MjTZo0iaysrGjXrl20Y8cOsrS0pB9//JGmTp1KTZo0oV9++YW6dOlCvr6+5OTkRH///TedOXOGnJycuDrDmpeurq4UFBRERCVtgKGhIeeDiGjevHnUtWtXucqG1SfrsyiPYB+rIJ2vry/5+PjQ9evXKSAggBwdHcnX15cKCgooPz+ffH19adiwYVXqpgwbNowTj5Om8dtvvyU7OztKSEggIqIbN25Q06ZNKTAwsMo6URplCIGytmnytFU1Eb4zo0SkPeeKPqVFjFhFnnR0dCgtLY2ISn4wNTQ06Pr161yc9+/fJ11dXWZxK2Nj4zJhpTlx4gQZGxsTEZGRkRH9888/RERUr149unr1qoztvXv3SCwWk7m5OV24cIGISn6cBQIBnThxgrM7deoUNWjQQMbO1NSUEhMTZfzduXOnjL/K7KR5ziqsJbUzNzcv05AmJSWRkZERsxAeEVGdOnXo3r17Mn7Cw8NJR0eH/vzzT64zo6WlRampqURUIgr3oTT/jRs3yNjYmFq2bFnpp0mTJly9YBXik0dYi6VOisVi7p7Nzc3LlM3t27c5MTHWuD/Mc01NTZk8v3v3LhkYGMglSskaN2ueN2jQgA4fPsylRygUcqeVExHt37+fnJycyMrKihude/z4MQkEAvrzzz85u6NHj1Ljxo2JiJjzsk6dOnT37l0iIiouLiZ1dXXuR1haf0xNTeUqG1afrM+iPIJ9rIJ0JiYmdOXKFSIiys3NJYFAQGfPnuXszp8/T9bW1iQUCqlVq1bUpUuXcj9t2rTh6oI0jc2aNSvTMTt69Cg5ODhUmK7yUIYQKGubJk9bVRPhV2wpET09PQQFBclshyzN3bt3MXHiRLlEnvT09PDixQvY2toiOzsbRUVFePHiBWf74sUL6OrqIi8vj0ncSl1dvVJdCCMjI06fomfPntiwYQO2bNmCzp07IyIiAi1atOBs9+/fD3t7e9y9e5fTRahbty60tbVldk01bNgQT58+xejRoxEaGoo//vgDvr6+WL9+PTZt2sSl+9dff4WLiwtatmzJZCeFRVgLAN68eQMtLS2IxWJoamrK2IlEIrx7945ZCA8As9ZLo0aNcOnSJdjZ2UFPT6/MPPabN28gkUiQnJyMIUOGlBEnlPL06VPcuXOnzPeVCfGxCmux1klnZ2ecOnUKDRs2hJmZGTIyMmR2k2RkZHCCW6xxa2trIy8vj/vexMSkzEGpRUVFkEgkzKKUrHGz5vmTJ0+4um9vbw+RSCTzLLRp0wYZGRkoKCiAvb09AMDCwgJisRiNGzfm7Jo1a8atj2DNy9LbjoVCIbS0tGBgYMB9p6enh5ycHLRo0YK5bEpTmc8xY8YwPYvx8fFyCfaxCNLl5uaibt26AAAdHR3o6OjA3Nycs7O0tERWVhYcHBwQGBiIYcOGleuvdHsq9Z2VlQUnJycZu9JlIw/KEAJladPkaatqJKruTdVkunTpQj/++GOF4VevXiWBQEADBgyg2bNnV2lHRDRs2DBq37497dq1i/r27Us9evSgL774gm7dukUpKSnUuXNnbhizefPm1LJlS9LV1aWDBw/K+IyLi6P69etTnz59yMvLizIzM8vEm5mZSV27dqW+ffsSUcmbpa2tLbm7u9OMGTNILBZTx44dafz48eTu7k4ikYiOHj1K1tbWMqMN3333Hb148ULmfoyNjSk7O5vatGlD9vb2NHz4cNLS0iIbGxvq2rUr2dnZUZ06dSg+Pp7ZjqjkLSY0NJR+/vlnsrCwoDNnzpTJS0NDQ5lRM4FAIDONQUT0xx9/kIODA61evZq0tLTI29ubDA0Nae3atWRmZkazZ8+mOXPmkL6+PjfN17VrV1qxYkW5ZRgeHk4aGhokFAopLCyMLC0tKTY2lnbs2EFNmzalEydO0OPHj+nUqVPUvHlzGjduHLVu3ZrWr19fYb24cuWKzMhDbGwsXbt2jWxsbOjy5csytrdu3SJdXV3q27cvLViwoEKf8tbJI0eOUN26dSksLIzCwsLI1taWtmzZQufOnaNt27aRlZUVzZo1i4iIOe4OHTrIjHJ8yOHDh8nJyYnc3NxozZo1lfqT5g9r3Kx5bmpqKjMi6ubmJrPO6datW1SnTh2ysLCQGeHw8/Pj3rKJSg7xMzQ0JCJizktnZ2du6pKoZNSk9Hqps2fPkp2dnVxlw+qT9VkUCAS0Y8cO+t///kdWVlZlRl5u3rxJderUIaKSNs3Z2blMnSUiunz5Mrm4uNDw4cOpYcOGMiMx69ev59a8EBElJCSQmZkZDR06lAICAiosQ2lZl5b1r1evXpnRkX/++YcblWbB1taWAFCTJk2qbHdZ22ci9jZNnraqJsKPzCiRoUOHllHdLI2ZmRkWLVqEQYMGMYs8rVy5EsOGDcOkSZPQqVMn7Nu3D0FBQXB0dIRAIEDDhg2xdevWMm8ZFYlbLV++HL169YKlpSWcnJxgamoKgUCAzMxM3Lx5E46Ojjh69CiAkjfLK1euYNmyZTh8+DCICJcuXcLDhw/RoUMHTvzNxcUFFy5c4N4gli1bJhP333//DWdnZ+jr6+P8+fPYunUrDh8+DFtbW0gkEhQUFMDPzw+TJ0+GpaUlADDbWVtbY/PmzQBQqbDWh2kq/YYHlOhfjB8/nlkIDwAmT55c4e4ZPz8/AMCmTZswatQovHz5Er179wYRobi4GN26deNsfXx8sHr1asyfP79S2XE9PT0ZZVsWIb5Zs2bJjHp8iFRYy8TEhKlO2tjYYNOmTQgICMCTJ09ARBg/fjyAkpGqSZMmcRorrHGrq6tDR0enQrsHDx5g4sSJeP36NbMoJWvchw4dYspzgUCAxMREblTo3LlzMnY3btyAg4MDTExMcPnyZbRq1QoAEB4eLmN3+fJlNG3aFECJyCZLXjZo0EBmx9uHz3pUVBQ8PT2Z/QEldZfFpzzPLKsA4Nq1a+Hn51elIN2cOXOQkpKCjh07cmkuzfHjx9GqVSusWrUK+fn55RUfAKBFixaQSCTo0qULV9blCen99ddfMurGVSGPqKA8QqCsbZo8bVVNhNeZqSGkpqbi7du3aNKkidx6DxKJBNHR0YiPjy8jBtWtWze5JM9ZuHz5MsRicZkG81OgTGEtecnOzkZMTAxSU1MhkUhgbm6ODh06yBxBwYqihfjkpbi4GImJiTL30rp1a+jp6SklPlVz584daGhoVDgdFR4eDnV1dXh7e0MoFMpM2ZQmKioKYrEYXbp04b5TdF5+jmVTnmDffxWkS0tLg5aWVpkXk48lNTUVIpGI65x9rnxObZoq4TsztQxe3IqnIohBWCsrKwtExHw2Eg8PD8+ngBfNqwaUJ/L06NGjMqJ2AFBYWIgzZ84wiVtdu3aNSeBLSlJSEidW9TlDChDWKg2rEN7x48dl8ic8PBwuLi7Q0dGBvb09fvnlF1y/fl1heV6ZKFxFQnyswlovX77EgAEDYGNjg2+++QbFxcUYN24czM3NUb9+fbi5uXEnpCuKioT4KuNjBCSrivvZs2eIjY3lFmZnZWVh+fLlWLZsGfdMqZqnT59i165d+Ouvv1BQUCATlpeXV+H5PJ/CpyLreHWhtOhiTEwMFi1axAkNnjlzBj179oSnpycnAimPnTx8jGhnjUElK3V45KL0QsYnT55Q27ZtSSgUkpqaGo0YMYLevHnD2Uq3/7KIWwkEAnr27BlzOvT09Oj+/fuKvTkFoyhhLSmsQnhEbFoviszz0vWCVYiPVVhr9OjR5OTkRGvXrqXOnTtTv379yNnZmf7++286f/48tW3blkaMGMF8HyzII3JHxC4gKU/csbGxpKOjQwKBgMzNzenatWtkaWlJDg4O1LhxY9LU1KTo6GgiIlq3bh15eXnRoEGDyiweff78OdnZ2VFBQQHNmjWLGjZsSG3btqVt27bJ2JWuP6xcunSJDAwMqE6dOiQWi8nBwUFmC66qfQqFwhrXrlSFtP7s3LmT1NXVqVWrVqSrq0thYWFkYGBA48aNo7Fjx5JIJKIDBw4w28mDPG1VTYSfb/gMmDFjRqXhpbfozZkzB2pqarh48SKys7Mxd+5cdOnSBTExMTA0NARQMjKRmpoKKysrACUjBOvXr+e22fbo0QP29vbo06cPFixYUGbxWUV8+Lb2OfLdd9+hRYsWOHz4MLZt24Y+ffrAwcEBFy5cABHhq6++QnBwMHbu3Mnkb/Hixfj2228RGhoKIsLKlSvh4+ODAwcOoEePHjK2VGrGdvXq1QgKCuK2Bg8dOhRmZmaYNWuWUvI8IiKCWxA5c+ZMWFpa4uzZszA2NsbLly8xcuRIBAQE4OLFi9i+fTu8vb0xZcoUODg44ODBg9wJ5sbGxhg/fjwKCgoQEREBNzc3DBo0CObm5oiOjkaHDh24+xs8eDBT2qRUdarw48eP5fL3ww8/YMmSJQgKCsLevXsxcOBAzJgxAwsWLAAArFq1CitWrMDQoUOZ454/fz5GjRqFZcuW4bfffkPv3r3h6+uLX3/9FUDJQuIlS5YgJSUFc+fOxejRo5GTk4NevXph0aJFmDt3LoCSdSoZGRkIDQ3Fjh078O233yI7OxuBgYGIj4/Hxo0bubhJzpn+efPmoX///ti8eTPy8vIwZ84cdO7cGTExMR+9bkKRPomoxrUrf/75Z6XhqampAErq3KpVq+Dv74+TJ0+ib9++CA0N5bahOzo6Ys2aNcjLy2OyGzhwIHMa5WmraiL8mpnPADU1Nbi4uFR4wnVubi4SExNRXFyM+vXr49ChQ9xOofz8fAwePBgZGRk4efIkCgsLYWFhAVNTUxw8eBBffPEFzMzMEBUVJdMo3b17F02aNJHZDcNCeHi4whbYKYN69erh+PHjcHFxQV5eHvT09HDmzBluB8SFCxcwZMiQMgtmK0JfXx+JiYnczgug5ATq8ePHY8+ePWjXrh0sLCxQXFwMoVCIzMxM1KtXD6ampoiKiuJ2sQAli0YdHR1ldiJUxt9//w2gZDFveWtZiouLkZubi+LiYojFYiQnJ8POzg5WVlaIjIyU0aO4efMmPDw8kJeXh5SUFFhbWwMo2R1x5coVbtdGeno693dycjKnDyTdRSFdtJ2WlobmzZuXO9VZEUKhEObm5hCJROWGFxQUIDMzk/lsKl1dXdy8eRO2trYgImhqaiIhIYHbYZSamooWLVrgzZs3zHHr6upy5V1UVASxWIzLly9zOkZ3795F27ZtUb9+fQQFBWHo0KEASupVv379MHHiRAQHByMrKwsWFhZo0KABVq9ejT59+gAomc7q2bMnOnTogG3btuHZs2dc/WGlbt26iI+PR6NGjbjvpNNg0dHRsLa2VqnPLl26yH2o4efergiFQggEgko7ngKBAGKxGDdu3OAWhotEIvzzzz9wdnYGUHL6docOHfD+/XsmO3mmxeVpq2oi/MjMZ4A8Ik85OTncCAxQssUyIiICgwYNgoeHB3bt2gUA+PLLL6sUt2rfvj1iY2MVei9nzpxBixYtoK+vrxB/O3bsQIcOHWQe0MrsWIW1WAgODoZQKGQSwpOSnJyMzMxMiMXiMusGJBIJxGIxc57r6Ogwi8KxCvGxCmtpa2vjyJEj+OabbxAVFQUtLS0cP36c68xER0dXuJOnIuQRh6yK4OBgaGpqMglIyhO3SCTC+/fvAZR0cCQSCXcNAO/evYOGhgbS0tLg5ubGfe/q6opTp07By8sLhYWFCAgIAFAy4lN6117Dhg1x+vRpeHp6Yvjw4Vi+fDnT/X5I6TQBwOzZsyEUCtGtWzds27ZNpT7HjBnD9MyqkuDgYHTp0oX5ZY5VdFFDQ0NmpElTU1NG8FEqxikSiZjs5IFVtLPGoqLpLZ7/QyAQkKmpKfn5+VVoU1o0r3nz5hQREVHGprCwkPr168fJecsjNKfo+6lbty6tXLmyUjtbW1saM2ZMlQcqSg9+mzp1KpOdvr4+k7AWC7a2tiQUCqlp06blhpcWwpOmobRk/oeCbuHh4eTo6MgUNxHJJQrHKsTHKqy1a9cuUlNT487GioiIIAsLC/rqq69oyJAhJBKJ6Ndff2W6D2lZ9+rVi1kcksWnmpoaGRgYVCkgSUTMIoC+vr7Up08f+vvvv2nChAnUpk0b6t27N+Xm5lJeXh4NHDiQevToQVZWVmXEy4hKjsEwNTWl4cOHk1AoJDs7O5mjPKQ8fvyYGjVqRN7e3nKtY7C1tSVTU1NaunRpueHLly8nTU1NZp/Ssmnfvj1t2LBBIT5Zn1l5kKe98PDw4I5dqcyfWCymPn36MPnr1KkTk+himzZtZM5DysnJIYlEwl3HxMRQo0aNmO1YkR4xMn/+/HLDP2yraiJ8Z0bFhIWF0cyZM6lVq1ZM9rNnz+YOh/uQwsJC8vHx4SpsQUEBbdiwgXr16kVNmjShRo0aUefOnWnevHn08OFDhd1DadLT0yk2NpbmzJlTqd2iRYto1KhR1KBBgyp9pqWl0W+//cZk16lTJ9q8eXOFNkuXLqVevXpV6UvK3r176csvv6wwPDw8nLp06UJEJfde+iM9FVfK9u3bafv27cxxh4aG0uLFiysMf/DgAY0aNYq7XrVqFWlra5NYLCaRSCRzDli/fv24heK7du2iqVOnciq7sbGx1KlTJ2rdujUtXryYO8347NmztHLlSjp//jwRlfxQDx8+nAYMGEC///47831Iy9rS0rJclVcpBQUFlJ6ezuw3PT2dU1Dt2bMn5eTk0NSpU7lOpYODA3c2UVJSElPcd+7cIXt7exIIBNSsWTN6/Pgx+fj4kLq6Oqmrq5OJiQklJCSQn58fdwDrh9y8eZNMTExIKBTS2LFjacyYMeXaPXr0iDuUkZVFixaRm5sb6erqVmjz448/kq2tLbO/UaNGkbGxMQ0bNkwhPonYn1lWWNuLsLAwWrx4Mbm5uVXp8927d3Ts2DEmf05OTjIKyR+Sm5tLp0+fpoMHD1JcXFyFdkuXLqX58+cz27GyaNEi8vDw4FSVy6N0W1UT4dfMVDOKiorw9u3bCtfXFBcX49GjRzJnIfH8fxQtrPW5oUghvurKfxGQlPLixQsZ0cGTJ0/i3bt3cHV1hZGREa5fv46EhASMHj263P9PSkpCREQERo0ahZSUFBlxuNI8ffoUx48fl1HL5eHhkR++M8Pzn6GSET6FKQVnZWUhPz+fW6RaEc+fP4eBgQE0NDQUEu+rV69w7949mJubK0T1s6ioCLGxsXjw4AFsbGzg4eEBNTU1BaS0dpOXl4eEhAS5F6/zKA/WZ/ZTcvfuXZw/fx6ZmZkQCAQwNTWFm5ubwjv2xCA2yfMJUOGoUK3n3r175OHh8Z98vHz5klavXk1Tpkyh77//nh48eKCg1JWlsLCQgoKCyN3dnRYuXEhEJfPp2traJBKJaMSIEZSfn1+lnwcPHtDo0aPp9evX9PXXX5O1tTX3v1OmTOGmCdzd3SknJ4c2btxI79+/JyIiiURCoaGhZGBgQEKhkLS1tSkwMJCbGmFl7ty5lJeXR0QlUwzjx4/n1rsIhUL68ssv6d27dxQdHS1z4N7u3bupRYsWpK2tTQ0bNqSff/6ZC2PVelE0165dk+v+IyIiaMmSJbRu3Tp6/vy5TFhOTg6NHj2aiEoOPhw7dizNmjWLbt26JWP38uVLprorLWtF8qEezcOHD2W0lqQUFBRUOpRfGRXlZ3FxMR07dkyu/L5586ZMHaqM5ORk2rZtG5fft27dokmTJtHo0aPLaNmowh/rM6tIWOuQtD3Nzs4mHx8fEggEZGBgQI0aNSIHBweuzfD19WVKY+n2+f379zRjxgxyd3en5cuXExHR999/z+lX+fn5Kfy+K6KiNl+etqomwndmVIi8ImFERObm5txajNTUVDIzMyMzMzPq2rUrWVpakr6+fpkfHkUxf/58MjU1pRkzZpCjoyNNmjSJrKysaNeuXbRjxw6ytLSs9JRwKdL7njp1KjVp0oR++eUX6tKlC/n6+pKTkxP9/fffdObMGXJycqJ58+bJiNH99ttvpKOjQ6tWraJz587R2rVrSV9fn9auXSvXvZT2GRoaSiYmJhQZGUmPHz+mw4cPU/369Sk4OJhJCC88PJyISsomOTmZiIi++uor8vb25joLL168oD59+nALUhWJPCJl0dHRBIAcHBwqFdbavXs3qampUe/evaljx46kpaVFu3btKmNXFR9Tx1l9sgpIykNOTg4NGjSItLS0qF69erRw4UIqKiqS8QlAKaJwUVFRJBKJqG7duqSlpUVRUVFkYmJC3t7e5OXlRerq6nJ1QBTtj4iYn1lFwlqHpHbDhw+n5s2bl7vBIT4+npydnZkEH0vHyyo2qQxY23x52qqaCD/NpERYhLpWrlwp177/0lomfn5+yMzMxNGjR6GtrY38/HwMHDgQWlpaOHDgwH9NfhkaNmyIn3/+GX369MG9e/fQuHFjhIeHc+JpBw4cQHBwMEJDQyv1k5qaipkzZ6J+/frYvn07PDw88OTJE1haWuJ///sf+vbtC6Dk1NoZM2bgzp073D23a9cOfn5+nLgUAGzZsgVr167FtWvXmO+ldD62bNkS06ZNk5H63r9/PxYvXoyUlBTOrmPHjvDy8uK2QwMlp5jv378fly5dYtZ6KS2CqAiEQiEmTJjAJFK2d+9ePHv2DHfu3IGdnR1WrlyJ4OBgTlhLqo/SokULjB49GtOmTQNQIso3evRorFmzBmPHjuXsDh06VGl80rKWV/OkMqT6OsOGDcOdO3ewdu1aTkCSiDgByaysLJibm8slrT99+nQcO3YMoaGhyM7ORkhICJycnHDw4EGIRCJkZWXBzMwMEydOZBaFW79+PZKTk8scSfIhbm5u8PT0REhICPbu3YspU6Zg8uTJ3PMUFBSEy5cv4/jx40zxKtofUHKCM8szm5KSwuyTRZBu5syZWL16daV20vZUT08P0dHRMqd0lyY+Ph49evSo8niG0u2ztbU1tm3bBm9vb6SmppYRm4yJicH48eORnp5eqc+PgbXNj4yMZG6raiQq7kzVaAQCAVlYWJCtrW25HwsLC7nfHAUCAdf7trOzK/NmFR8fT5aWlgq7h9JoaWnJTGNpaWnJjAKlpqaSnp5emS3K5X2EQiFpamrK+NPW1qbbt29z1+np6aStrS1zBICxsTFdu3ZNJl3379+vdHdHeZT2aWRkRDdu3JAJT0tL4+KW5ne9evUoISFBxu727dukr69PRETOzs7cDqGmTZtSTEyMjO358+epbt26cqWThc6dO1OXLl2YPmpqatS+fXt68uQJ9//h4eGko6NDf/75JzeaoaOjQ6mpqTLxxMbGkp6eHm3YsIGzYy1redDW1qaZM2fS77//Xu5nyZIlJBQKycLCgi5evMj93/v378nX15dcXFzoxYsXHzUyY21tTbGxsdz1v//+S+3bt6du3brR+/fvuZEZ1vyWfkrnd0XUqVOH7t69S0Ql01nq6uoy9e3GjRtkamrKfC+K9kdEzM+sPLDWIdb2VF9fX6ZefEh8fDzp6+vL1T6LxWLKyMjgfGhoaMgc9yBtL5QBa5svT1tVE+E7M0rE1taW9u3bV2H4lStXmBvbJUuWUFxcnMyPsIWFhcwDRVTyUGlqajL53L59O7d1lQVTU1O6fv06d+3m5iazBuTWrVtUp04dsrCwoEOHDlXoR3rfFhYWMg+cn58f9zASlaw1MDQ0JIFAQDt27KD//e9/ZGVlVWb4+ObNm5VuSSwPgUBAoaGh9PPPP5OFhUUZzZCrV69yccfGxtK1a9fIxsamzPbeW7ducR0pVq0XVWJiYlKuBsfevXtJW1ubNmzYQEKhkMzNzenChQtl7E6fPk26uroUFBTElSFLWbOyfft2atWqFZO+jo6OTplztqR6S87OznT9+nW54xaLxWU6ca9fvyZXV1fy9PSk1NRUpWl1lO58EBHp6urKTE+lp6eTlpYWk6/t27eTrq6uwvxJfZqamjI9s/LAWodY29Nhw4aRs7NzuVvxL1++TC4uLjR8+HC52ufGjRtzLyqXLl0ikUgkc87W3r17ycHBoapbJSL2dldqx9rmy9NW1UT4zowSYRXqYkEq8gSAmjdvzulrHDx4UMYuLi6O6tevz+RTXnErDw+PSvVF9u/fT61bt6a+ffsyCUz16NGjUi2KsLAwcnNzK/OWFhoaKmO3efNmatmyJdM9EJXct5aWlsxb2Yc/nqtXr6YvvvhCbiE8Vq0XRcEqJiala9eutGLFinLDSgtr+fr6cou8P0R6GKNQKGQua1YEAgGpqalRu3btKrSR6uuwCkjKEzeAcoXU3rx5Q66urtSiRQuFd2akZdi0aVMZLZMbN27ILOg8e/Ys2dnZMfmU1te+ffsqxF9pn5VplUifWXlgrUOs7emrV6+oR48eJBAIyNDQkBo3bkxNmjQhQ0NDEgqF1LNnT3r16pVc7TOr2CQL8gqBsrb5ihbtrG7wxxkokeDgYLx9+7bCcEdHR6SlpTH5SktLw/v37zF+/HjY29tz3384b3/48GHms38kEgnS09MRHR3NZP/bb79Vug26sLAQs2fPhrm5OfLy8iq0s7e3R2xsLJo3b17pdm5TU1OEhoaiS5culabLzMwMS5curTL9UrZt24aMjAwcP34c586dK9fmiy++QOfOncus3ygtOQ6U3PN3333HXc+YMQNjxoz5ZFovI0eOREZGBtzd3XH//v0q7SdPnowzZ86UG+bn5wcA2LRpEwIDA3H+/Ply7bp06YIjR45g+/btGDNmDFNZs8JSJ62srBAWFobvvvsOmzZtwoABA2TC1dXVceDAAQwYMACPHj2SK+5Ro0bhxo0bZcJ0dXURHR2Nrl27MvtjRVqGz58/l1lbVPoYBACIioqCp6cnk0+JRILQ0FA8ffpUIf6kPq9evVppeUqfWXmYNWsWUx0yMTFhak8NDAwQFRWFlJQUXLhwAZmZmQBK2glXV1c0adIEgHztc0BAAExMTBAfH49x48Zh8ODBcHJywsKFC/H27VsEBgYiKCiI6X5Z212p3bRp09CmTRvu+4ra/GXLlsl8X1VbVdPgFwDz8PBUS5QhIPnq1Ss8efKEO6vqQ3Jzc5GQkIDOnTt/VJp5eHiUA9+ZUQFZWVkgIpiZmTHZs4iEFRUVlat2Km18SwtHtW7dukyv/XPl+vXrcHJyYhbkS0pKQuPGjZmUX4kI6enpsLKygrq6OgoKCnDo0CHk5+ejV69eMDY2LvM/FQnhKTOdnxOKECr8r3Xy1atXOHz4MEaMGPHRafiQoqIiPHny5LMSfZMXRYpIfi6Cj1TDBOlY65ki6uPnUoafDBVOcdV4Xrx4Qf379ydra2uaMmUKFRUV0dixY7m5TVdXV6ZdDqX1DqKiorhFuMXFxfT9999zq+7r169PS5cuJYlEQoWFheTv709isZgEAgFpamqSSCQigUBAYrGYpk+fTgUFBXLf07p168jLy4sGDRpUZlX98+fPmebgpWJUBQUFNGvWLGrYsCG1bdtWZkEdkXI0PaRxp6SkkI2NDQmFQrK3t6fU1FRq3bo1J4JlbGxMd+7cYRbCk0frhSWdLHyMIB2rsJaihApLC48pqk5WpDvyXwQkK/JZUFBAhw4douXLl9POnTspNzeX2ScRUWJiosyC4p07d5KbmxtZWlpShw4daM+ePTL2T548oZ07d9LRo0fLCFDm5ubSkiVL5CobFn9E7IKPLM+svGuKFCVIl5ycLNcaIHn9Xb16lb7//vsqxSYrQ17NHCnHjx+nhQsXcm1uXFwc9ejRgzw8PLgyUJVo5+cC35lRIqNHjyYnJydau3Ytde7cmdtl8ffff9P58+epbdu2cos3OTo60rlz54iI6IcffiAjIyP66aefKCoqitasWUOmpqa0bNky8vf3p/r169PevXvp1atXnK9Xr17R3r17ycrKqsKD8iri559/Jm1tbfrmm29o2LBhpKmpST/88AMXLq+Q2qJFi8jU1JRWrFhBQUFBpK+vTxMmTJDxB4AmTpxIgYGBTB9NTc1KOwnSuH19fcnHx4euX79OAQEB5OjoSL6+vlRQUED5+fnk6+tLw4YNYxbCEwgECk0nCx8jSMcqrKUoocLSaWStkzk5OZV+zp49y+24UpSApDSdrq6uXNqePXtGzZs3J5FIRA4ODqSlpUXW1tZy/SC0bNmSEyXcvHkzicVi8vf3pw0bNlBAQADp6urS1q1biahkl4yBgQHVqVOHxGIxOTg4yOxckT5frGXD6o+IXfCR5ZmVZ8E3keIE6RQt0FjaX3R0NIlEImrWrFmlYpOKSmNpu507d5K6ujq1atWKdHV1KSwsjAwMDGjcuHE0duxYEolEdODAAZWJdn4u8NNMSsTCwgIRERFwc3PjBLxKLyI8d+4cBg8eXOkiNOD/i4QVFxdDLBbjzp07sLKyQvPmzbFgwQJ89dVXnO3Ro0cREBCA7Oxs7Nu3r8IFfidPnsSQIUPkEnBr1qwZgoKCMHToUADAhQsX0K9fP0ycOBHBwcGckBqruFWDBg2wevVq9OnTBwBw//599OzZEx06dMC2bdvw7NkzmJmZVbkAuDSPHj3CiBEjoK+vX2ncRkZGOH78OFxcXJCXlwc9PT2cOXMGHTt25O5tyJAhePbsGZMQXrNmzeQeDg8PD6/0wEtWMbGPFV2sTFjrn3/+YRIqHDt2bKXxlRYeMzExYaqTL168qDQv6f+mHoiIWUCyVatWlabz3bt3uHPnjozPCRMm4PLly4iKioKZmRlevHgBHx8fNGnSBFu3bq3UnxQdHR3cunUL1tbWaNWqFSZNmoQJEyZw4eHh4QgNDUVSUhK6du0Ka2trbN68GXl5eZgzZw727duHmJgYtGzZknu+SqexsrKpV68ekz9pu8JSzw0MDKp8ZqU+WWEVpOvfv3+lfp4/f47w8HDmuGfMmMHsz83NDR4eHggNDQURVSo2WRnSesZqV1xcjJYtW2L06NHw9/fHyZMn0bdvX4SGhnJl/tNPP+HgwYNISEhQiWjn50L1mrCvZuTk5KB+/foASlb5q6ury/x4WVhYIDs7G0SEyZMno3nz5uX6ycjI4H5wDA0N8fjxY1hZWeH58+dldsg0atQIjx8/hlAoLHfNhxQjIyO8e/dOrvtJS0uDm5sbd+3q6opTp07By8sLhYWFCAgIAFCy8t/c3BwikahcPwUFBQBKfuhK77Bo2LAhTp8+DU9PTwwfPhzLly+HUCiUazeMUCjEb7/9VmXcubm53E4lHR0d6OjoyJSNpaUlsrKy0LhxY1y6dAl2dnbQ09PD69evZfy9efMGEokEp0+fZk4jK/369eN+tCviv6wnuHv3bhmVah8fH4SEhMj4TktLg5eXl4ydp6cnAgMDmcsaKGmgWeqknp4egoKCKlRwvXv3LiZOnCjz3cWLF7FlyxZup4empibmz5+PgQMHAgCSk5MxZMgQ2NnZlevz6dOnuHPnjsx3cXFx+Omnn7i1bUZGRggNDa3wpOzyEIvFeP78OaytrfH48eMy99S+fXtux0xCQgLWrVsHoVAIPT09rFu3DjY2NvDy8kJ0dLTM+gmWsnn48CGzv0aNGjHVc5ZnVl7+/fdfNGrUCADQoEEDqKmpyezYdHBwwPPnz/Hzzz/DxcWlwgXfubm5csUrj7+kpCTs3LkTQEnez5o1C5aWlhg4cCD27NnDdRpY65k89fHu3bucwrKXlxeKiopkyrx379744YcfmMuwxqKyMaFaQIsWLejXX38lIqK//vqL9PT0aNWqVVz4hg0byMnJidzc3JhEwoiIpkyZQn369KGioiKaMGECjRs3jiQSCWfr7+9Prq6u1KdPH/Ly8qLMzMwy/jIzM6lr164yGhQsWFlZlRGXIyJKSkoiU1NTGj58uFziVnZ2dnTixIky4Y8fP6ZGjRqRt7e33MPGrHE3bNiQzp49y32/fv16ev36NXedkJBAZmZmKhXCU7QgHRExC2uxChXKIzzGWie7dOlS6RlfUv0PeQQkW7duTevXr68ynaV91qtXj5KSkmTs0tPTmUUpiYiGDRtGY8eOJSKiQYMG0fz582XCf/jhB2revDkRERkaGpZRtyYiWrFiBRkYGNDBgwe5NLKUDas/InbBR2U8s6yCdI0bN6adO3dW6Eee58HW1pb09fUrPXyxtD9WsUnWesZqR0RkYGBAKSkpXNiH4oepqamkra1dLUQ7lQnfmVEiu3btIjU1NbK3tyctLS2KiIggCwsL+uqrr2jIkCEkEono119/pdDQUFq8eHGFfqQiYURE2dnZ1KZNG7K3t6fhw4eTlpYW2djYUNeuXcnOzo7q1KlD8fHx9ODBA3JyciJ1dXVycXGh7t27U48ePcjFxYXU1dXJ2dmZHj58KNf9+Pn5VbjO5ubNm2RiYkJCoZBZjGrs2LE0ZsyYcm0ePXpE9vb2cjeMrHFPnDiRNm/eXKHd0qVLqVevXkT06YXwpChakI6orHR8RcJarEKF8giPsdbJTZs2Vfojk5mZSYsXLyaBQMAsIDl9+vRK14jdu3ePunTpQgKBgHr16kVffvklGRoa0l9//SVjd+HCBbmOAHj8+DHZ2tqSu7s7zZgxg8RiMXXs2JHGjx9P7u7uJBKJ6OjRo0RE1KlTJ9qwYUO5fpYvX06amprlyv5XVDas/qSw1HNlPLOsgnRDhw6lgICACv3I8zwsWrSIGjRoUKlyeGl/rGKTrPWM1Y6IqE2bNvTHH39wYTk5OTIvsDExMdSoUSMiUl1b9TnAr5lRMn///TcuXrwINzc3uLq6Ijk5GcuWLcPbt2/Rt29fjBw5Um6fhYWF2Lp1Kw4fPlxGmG3y5MmwtLQEUCK6FB0djfj4+DLCUd26dWPeRizl+vXrSEhIqHCYPSkpCRERERg0aBDevn0rI/T0YfqfPHkCAEhJSUH37t3LtXv69CmOHz8uVx4lJyczxV2V9khaWhq0tLS4qafs7OxPJoQn5ezZs8jLy0OPHj3KDc/Ly8M///wjl+ZJRkaGzLWuri6MjIy46x07dgBAlduejxw5Ag0NDVhZWcmV34qsk6XX+gAlQoel69KsWbPw6NEj7Nmzh9nnh3W7V69eGDRokIzPGzdu4NixY8w+s7OzsWzZsnKf18DAQC7vtmzZgri4OG4640OWL1+ODRs2VCm0KS2bhw8fyu2vqnqekZGh8GcWAHbv3o34+Hh07NgRgwcPxunTpzlBur59+2LBggV49uwZ8vPz5dINqozMzExmf4cOHcKZM2cqXA+4Z88ebNq0Sa4pcVYOHToEIyOjCqU5li1bhry8PHz//fcAVNNWfQ7wnRkeHh4eRvLy8qCmpgYtLS1VJ4WHh6cU8r2a8yiNa9euISwsjHtLSkpKwpQpUzBp0iTm4wakXL9+Xa6FXklJSSgqKvrkPj93Pud7VtQ7SFFREWJiYrB161acOHGiwp0g2dnZ2Lx5MxYsWIAtW7YgJydHrng+ZV5W9H/Pnj1DbGwstzAyKysLy5cvx7Jly8o9wqA8dHR0VNqRUVWdzMvLq/AYjOrGw4cPMWbMGJXEzZqP8ub359xWfTJUOslVCzhy5AiNHTuWZs2aVUbz4uXLl+Th4cHpfRgZGZGenh6dOHGCDAwMyNvbm7p3705qamq0e/du5jiVIeCmClE4ZSCPsJaq71lRYmKlYRXWGjBgAEVGRhJRyQJvY2NjMjExofbt25OpqSmZmZlxmhYsKCMvWQUkif7/4ZgCgYDMzc3p2rVrZGlpyS0s1dTUpOjoaLlF7v4rpUUFWVBVnVS0hkt5/v+rIJ08ccl7L6xik4qKW940qrqt+hzgt2YrkfDwcIwYMQI9evTA7du3sXbtWmzZsgVff/01gJJtq3FxccjJycGSJUsQFBSEvXv3YuDAgZgxYwYWLFgAAFi1ahVWrFjB6btUBRFhwYIFZQ4kq4jS22c/pU9VUFBQUGbdSEWo+p7nzp2Lffv2wc/PD2FhYcjIyMDhw4exceNGCIVCLFy4EPPnzy+zvboyIiIiMHnyZADAzJkzYWlpibNnz8LY2BgvX77EyJEjERAQgLi4OO7wzm+//RbdunVDWFgYRCIRCgsLMXnyZAQEBDCPGiojL2fOnInNmzcDAH788UesWbMGQUFBaNq0KW7fvo2lS5dCIBDgu+++w/z58zFq1CgsW7YMv/32G3r37g1fX1/8+uuvAErWwixZsgTv3r3DqlWrYGdnhy1btsDf3x/jx4/H8OHDcfv2bYwfPx5v375V2Jt9bm4u4uLimO1VXSeVwfHjx9G3b184ODjgzZs3WLRoEfbv3w8PDw8AJVv6t2/fjm3btjH5Y9FnkpeePXvi6dOnqFevHiIjIzFixAhMmTIFs2fPRmJiImbPng0TExPusNZPTU2sF/LCr5lRIq1atcLo0aMxbdo0ACU/JKNHj8aaNWswduxYTmhJLBbj5s2bsLW1BRFBU1MTCQkJnO5MamoqWrRogTdv3jDF26VLF4ULuCnDpzKQRwirKlR9z6xiYunp6cw+WcXR8vLycOPGDTRs2BAWFhY4evQoWrZsydnduXMH7dq1Q3Z2NlO8yshLVgHJu3fvQl9fH4mJiWjYsCGKioogFotx+fJluLi4ACjR8mjbti0KCwuZRe5YqKqjWVpUkAVl1ckPT4f/kNLCnYqGVZCONW6hUMikz6QMscl79+5V6keajxWJen5op+p6UZ3gR2aUyJ07dzilTAAYOHAgjI2N4ePjg8LCQnz55ZcAAD09Pbx48QK2trbIzs5GUVERXrx4wf3fixcv5DoYUhkCbsrwqQwUKayl6ntmFROTB1ZhLWdnZ5w6dQoNGzaEmZkZMjIyZDozGRkZEIvFzPEqIy9ZBSQBQCQS4f379wBK3kolEgl3DZS8/WtoaEBdXZ1Z5I4FeUQFWVBWnczPz2cW7lQ0rIJ0rJibm2PdunXo169fueFXr15F69atPzq9lYlNSkctq8pHRee3qtuqzwIVTW/VCszNzenChQtlvj99+jTp6upSUFAQCYVCGjZsGLVv35527dpFffv2pR49etAXX3xBt27dopSUFOrcubNcZ2ps376d7t27p8hbUTisabS1taUxY8Ywn4ejSGEtgUBAHh4e5YplfQpYxcTkgVVY68iRI1S3bl0KCwujsLAwsrW1pS1bttC5c+do27ZtZGVlRbNmzWKKUxn1USAQUP369alTp05VCkgSEfn6+lKfPn3o77//pgkTJlCbNm2od+/elJubS3l5eTRw4EDq0aOHXCJ3LMgjKsiCovNS6k8e4c6qkPeZZRWkY2HJkiXk5uamMH2mJUuWUFxcHLPYJGs+KjK/iapHm69s+M6MEvH19aWFCxeWGyZdkCgUCikzM5O8vb1JV1eXevbsSTk5OTR16lRO4MzBwUGuiioQCEgkEtHUqVMVdSsKhzWNixYtolGjRlGDBg2Y/CpSWCssLIwWL15Mbm5uTPaKhlVMTF5YhbUiIiLI0tKyjFCblpYWBQQEUFFREVN8yqiPYWFhNGfOHNLR0alSQJKI6M6dO2Rvb08CgYCaNWtGjx8/Jh8fH1JXVyd1dXUyMTGhhIQEuUTuWJBHVJAFReel1N8XX3zBLNxZFfI+s6yCdCzY2tqSSCSidu3aVWiTm5tLp0+fZvYnFosJAJPYJKsAqjxCqSxUhzZf2fBrZpRIXFwczp8/j7lz55Ybfvr0aWzfvh1hYWHlhqempuLt27do0qQJ1NXlmxFMT09HdHR0mTNsPieUkUZ5hLCqAyxiYvKKHwLswlrFxcVITEyUsWvdujX09PTkik9Z9ZFVQFLKixcvZEQCT548iXfv3sHV1ZX7nlXkjgVFiTiWRtF5qeq2QtGCdO/fv0dcXFyFwn7y8v79e+zfv19GnPJjxSaViarLUdXwnRkeHh4enloB/d+J6zw1D34BsAp4/vw5DAwMoKGhIfP9o0ePYGBgUGaxb2FhIS5cuCAjZ52bm4uEhARkZmZCIBDA1NQUrVu3lmuhsKooKirCkydP8OLFCxgYGHAnx+7atQsbNmzAgwcPYGNjg6lTp2LIkCEqSeP9+/cxfvx4nDp1SiXxK4Pr16/DycmJeSQnKSkJjRs3ZhoV/JT18b+UjUQiKff+JRIJHj16JHOS9OdCRe2FvBQVFSE2NpZ7vjw8PKCmpvaffL569Qrbt2/H3bt3YW5ujpEjR8LKyuo/+ayKu3fv4vz58zJ1zc3NjRtVzM/Px7x58/DPP/+gT58+mDVrFkJCQrBs2TIQEXx9ffHbb79xmwSq8lceysjLj0Xann6OdfeToso5rprOxo0b6f3790REJJFIKDQ0lAwMDEgoFJK2tjYFBgZScXExPXnyhNq2bUtCoZDU1NRoxIgRMgeCZWZmcnPGhYWF5O/vT2KxmAQCAWlqapJIJCKBQEBisZimT59OBQUFKrlfVqSL21q2bEmnTp0iopLD8cRiMfn7+9OGDRsoICCAdHV1aevWrSpNo6pRpJiYMoS1VFEfP6ZscnJyaNCgQaSlpUX16tWjhQsXyqz5Kf2MqQrW9oIVVoFEeTA3N6d///2XiEpOazYzMyMzMzPq2rUrWVpakr6+fhlx0KpgFaTLzs4mHx8fEggEZGBgQI0aNSIHBwcuj3x9fSknJ4cCAwPJwsKCZs6cSU2bNqVvvvmGrK2tadeuXRQeHk729vY0bdo0Zn/KyktF8bm0VaqGn2ZSImpqapzQ0saNGzFz5kwEBwfjiy++QGJiIubPn4+QkBBcvnwZd+7cwdq1a5GdnY25c+eCiBATEwNDQ0NkZWXB3NwcEokE06dPR2RkJFatWoXu3bvDwMAAQMk8f3R0NGbNmoX+/ftjzZo1Kr33yrh27RpatWoFLS0thWp6yIOi9T+UwYdiYm/fvpURE/sY/Y0JEyYwC2utX78eycnJaNCgQYU2yqiPyiib6dOn49ixYwgNDUV2djZCQkLg5OSEgwcPQiQSyTxjqoK1vZg6dSqTPwsLC5w8eRJNmzbF4MGD8fLlS+zZs0dGIFFLSwsHDhxgTmNpvRU/Pz9kZmbi6NGj0NbWRn5+PgYOHCi3z9L3HRkZicGDB2PKlCncff/6668ICwtDVFQUrl69is2bN5fZMn/x4kVMmDABLi4uiI2NZdJncnd3Z/K3fft2peSlopC2p6psqz4LVNyZqtEIBALKysoiIqK2bdvSTz/9JBO+efNmcnZ2JgsLC7p48SL3/fv378nX15dcXFzoxYsXMm+NxsbGdPLkyQrjPHHiBBkbGyvhbthp2bJlpZ8mTZqQUCgkIyMjbktmvXr16OrVqzJ+7t27R2KxWClpFAgEZGFhQba2tuV+pLL4qsTV1ZXmzZtHRCVv6suXLyddXV2KiooiIvlHEzp37kxdunSR6/PkyZNKfSqjPiqjbKytrSk2Npa7/vfff6l9+/bUrVs3ev/+/WcxMsPaXrCipaXFHc1gaWkp08YQEd24ceOjykaaRjs7uzJlHx8fT5aWlh/ts0OHDmV2gK5YsYLatm1L+vr63O608rhw4QLp6+uTWCymjIwM7nsNDQ26efMmd52Wlkba2trM/oiUk5essLantR1+zYySkS42S0tLg5eXl0yYp6cnAgMDQUQwNDTkvtfU1ERERAQGDRoEDw8P7Nq1iwt79+4djI2NK4zPyMgI7969U/BdyEdycjKGDBnCrYX5kKdPn+LOnTvo2bMnNmzYgC1btqBz586IiIhAixYtOLv9+/fLiMQpEhsbG/z4448yirGl+a/CWopA0WJiyhDWUkZ9VEbZ/PvvvzI7hoyMjBATE4Pu3bujV69e2LJli1z+lAVLe8EKq0Dix6YxPz8fpqamMmGmpqZyCzmWpjJButJxV5Yua2trXLhwAdbW1rh8+TIEAgEuXbqEZs2aASgZdalfvz6ePXvG5A9QXl6ywNqe1npU3ZuqyQgEAtqxYwf973//IysrqzJvATdv3qQ6depQ8+bNKSIiosz/FxYWUr9+/cja2prreffp04e8vLwoMzOzjH1mZiZ17dqV+vbtq5wbYqR169a0fv36CsOlQmGK1vRgZcmSJdS5c2eF6n8oA0WKiSkDW1tbsrKyoo4dOyq0PipSm0UqJta4ceNy69KbN2/I1dWVWrRowZyXcXFxlJ2dzWQrTxpZ2wtWWAUSWbG1tSUA1KRJE2rZsiXp6urSwYMHZWzi4uKofv36zD6JiFmQbtiwYeTs7FwmnIjo8uXL5OLiQsOHD2fWZ2L1R6TYvGQVFZTaOTs7M7WntR2+M6NESguNCQQCCg0NlQnfvHkztWzZkmbPnk3dunUr10dhYSH5+PhwlfXBgwfk5ORE6urq5OLiQt27d6cePXqQi4sLqaurk7OzMz18+FDp91YZ06dPp+nTp1cYfu/ePerSpQsREb169Yq+++47cnR0JC0tLRKJRGRjY0NDhw4tt5FRBLa2tqSpqUkdO3as0KagoIDS09OVEj8rihQTUwaLFi2iQYMGkYaGhkLrY1JSUqVlL0/ZSMXEnJ2dK1TRfv36NbVv314uZei6devSypUrmexZ0wiAqb2QB1aBRBYWLVpELi4uZGhoSIsXL6bFixfTsWPHZGy+/fZbGjJkiFxplIqDViVI9+rVK+rRowcJBAIyNDSkxo0bU5MmTcjQ0JCEQiH17NmTXr16RUREu3btoqlTp3IK2rGxsdSpUydq3bo1LV68mIqLi+Xyp8i8ZBUVlNrVqVOHuT2tzfALgFXIkSNHoKGhAS8vL7x9+7bC84SKi4vx6NEjbphcIpEgOjoa8fHxyMzMBACYmZnB1dUV3bp1+ygRtdqGooW1lIGixcSUxedeH9PT03Hw4EF0796dm2r4EOnW8tLCaBWRkZGBtLQ0REdHcyeLKyKNVQmeSdsLeessq0CiqvjwFPuqBOlSUlJw4cKFMnWtSZMmHxW/PP4+97yszfCdGR4enmpBVlYWiAhmZmaqTgoPD89nBv8K/wnJzs7G5s2bsWDBAmzZsgU5OTnl2r169Qpr1qzBN998g5CQEDx8+BBAieiZPIvMkpKSUFRUpJC0y8uzZ88QGxvLLZTLysrC8uXLsWzZMty4cYPJx/379+Hp6flR8d+9exfbt2/Hjz/+iOXLl3PCXqzcunWr0i3JtZ2HDx+iX79+SqmPL1++xIABA2BjY4NvvvkGxcXFGDduHMzNzVG/fn24ubnh6dOnnH1ubi7i4uKwb98+7N+/H3FxcVWejl5YWIg//vgDK1aswK5du5CXl1epPREpdIHn8+fPUVhYyF2r4tnOy8vDmTNnyg2rqA0CSrY2L1q0iBMtPHPmDHr27AlPT88Kj2aRh6KiIsTExGDr1q04ceLER205vnbtGkJCQrB+/Xr8+++/MmGvX7/GmDFjmH0psmwePnzIFDerHVB5OdYqVDnHVdMZMGAARUZGElHJOgBjY2MyMTGh9u3bk6mpKZmZmVFycjKzEJUyRM+UgfQQTYFAQObm5nTt2jWytLQkBwcHaty4MWlqalJ0dHSVfj5GDEoeISxFx61oWMXEVMHVq1cJgFLq4+jRo8nJyYnWrl1LnTt3pn79+pGzszP9/fffdP78eWrbti2NGDFCLsE+V1dXbv3Ds2fPqHnz5iQSicjBwYG0tLTI2tqaHj16RIWFhRQUFETu7u7cFuHly5eTtrY2iUQiGjFiBOXn5zPfM6sQniqe7dJ1nLUN2rlzJ6mrq1OrVq1IV1eXwsLCyMDAgMaNG0djx44lkUhEBw4ckCsdihaki46OJpFIRM2aNSNra2syNjbmxDmJ5Jc0UGTZsLYr8rQ/n0Nb9TnAb81WInFxcdyc+rfffotu3bohLCwMIpEIhYWFmDx5MgICApCZmcm9fcybNw9NmjQpI0S1YMECEBEWLFjALHpWUFCgtHurjPnz52PUqFFYtmwZfvvtN/Tu3Ru+vr749ddfAQCzZs3CkiVLkJKSUqmfx48fyx33tGnTkJaWhgsXLlQohDVt2jSZOfny+C/bSxVFz549ZcTERowYgSlTpmD27NlITEzE7NmzYWJiAj8/P4XH/eeff1YanpqaCgBKqY/Hjh1DREQE3NzcMGjQIJibmyM6OhodOnQAAKxevRqDBw/GzJkzERkZibCwsAoF+wBgzZo1iI+P5+IPCgqCmpoaMjIyYGZmhhcvXsDHxwcLFy6EhYUFtmzZgq+//hoRERF49uwZjh49ik2bNkEikWDevHlYs2YNZs+ezXQvkydPRr9+/VCvXj1s2rQJP/zwQxkhvAYNGqj82WZtg+7du4dVq1bB398fJ0+eRN++fREaGsptGXd0dMSaNWswcOBA5rgjIiIwefJkAMDMmTNhaWmJs2fPygjSBQQEMAvSLV68GN9++y1CQ0NBRFi5ciV8fHxw4MAB9OjRQ86cgVxlc//+fbx79w4nTpwodzpU+tywPl887PBrZpSItrY2bty4gYYNG8LCwgJHjx5Fy5YtufA7d+6gXbt2eP36Naeq2aBBA2zZskVmeuXixYsYOHAgGjZsKPchaeHh4TA3N1fYPbGgr6+PxMRENGzYEEVFRRCLxbh8+TJcXFwAlEwBtW3bFq9fv4a5uTlEIlG5fgoKCmQaWRYMDAwQHR1dpiMjJT4+Hj169MCbN2/g4uJS4aLr3NxcJCYmqlRVs7TaaseOHeHl5YUlS5Zw4StXrsT+/ftx6dIlpcQtEAhQWfNAROjSpYtcflnqo46ODpKTk7kF7yKRCImJiXBycgJQosHSvHlziMVi7Nu3r8KpyJMnT2LIkCF4/vy5TF42btwYP/30E3r37s3Znj59GqNHj4ZQKMTPP/+MPn364N69e2jcuDHCw8MxePBgAMCBAwcQHBzMPFVaOt527drBz89PRitmy5YtWLt2LQwNDRX+bNetW7fS/y8uLkZubi6Ki4tl0llZG/Tq1SvcuHGD0zwRiUT4559/4OzsDAC4ffs2OnToUGZqpzLEYjGSk5NhZ2cHKysrREZGymgo3bx5Ex4eHswvGKXbHyl79uzB+PHjOX0meZSzu3Tpwlw2Ui2nqvRr6P8OvKzs+RIIBCguLparHGsz/MiMEnF2dsapU6fQsGFDmJmZISMjQ6Yzk5GRAbFYjNevXzMJUZWet/6cEYlEeP/+PYCSDolEIuGugRKhNQ0NDaUJ17EIYTk4OCAwMBDDhg1TaNzKoioxMUVjbm6OdevWoV+/fuWGS/NHGTupHBwccOTIEXzzzTeIioqClpYWjh8/znVmoqOjYWdnh7S0NLkE+6Rln52dXUaAzM7ODk+fPoVAIOCEG+3t7SESiWSEHNu0aVNm901VsAjhvXnzRi6fLOTn52Py5Mlo3rx5ueEZGRkynWOWNkgsFsuMCmlqasocJioSieQWSVS0IJ2mpiays7NlvvPz84NQKMSQIUOwatUqudInj9hk/fr1mZ4b1ucLkL8cayt8Z0aJLFiwACNGjICGhgb8/f0RGBiIFy9eoGnTprh9+zYWLVqE4cOHY+XKlfDy8oK6ujpev36NO3fuyGwhffDgQaWN9udGhw4dMGfOHMyZMwc7duxAq1atEBISgn379kEgEOD7779HmzZtoKOjg4SEhAo7M1W9uZRH3759MX78eGzduhVt2rSRCfvnn38wadIk+Pj4oLi4GAkJCRV2Zj4mbmWQnJyMzMxMiMXiMg26RCJR2ttY69atkZiYWGFjq8z8mTVrFkaOHIk1a9bg0aNH2LVrF/z9/XHx4kUIhUIcPHgQP/30E44dO4YZM2Zg9+7dZX58s7KyMHv2bJnRhVGjRkFTUxOFhYXIyMiAo6MjF/b06VOZaSrpyc+tWrWCnp4eZ5efny/3CMqxY8egr68PsVhc5of+3bt3Stu67uLiAisrK4wcObLc8GvXrsn8CLK0Qebm5khJSUHjxo0BlEwFl86f+/fvw9LSUq50BgYG4ttvv4WpqSnmzp0Lf39/rF27lmsnp0+fjv79+zP5srOzg4aGBv74448yLyODBw+GRCKpMD8q8ufp6Yng4GDUr1+/SnvW50ae50vecqyt8J0ZJdK7d29s2rQJAQEBePLkCYgI48ePB1Dy9jBp0iQsXboUOjo6Mv/34dzs4cOH0alTp0+W7v/KihUr0KtXL3Tq1AmOjo44fvw4Jk+ezP1YGBoa4tixY9DS0sLbt28r9OPo6Ii0tDS54l67di38/PzQrl07GBgYoF69ehAIBMjKykJOTg66d++OX375Be/fv0d+fn6Fflq0aKHSQweleHl5cY3auXPnZDpoV65cgbW1tVLinTVrVqU7fOzt7ZWmb/P111/DxsYGFy9ehJubG1xdXdG0aVMsW7YMb9++xaZNmzBy5Ej4+PigV69esLS0hJOTE0xNTSEQCJCZmYmbN2/C0dERR48eBQCZHwJfX98yu50iIyPh4uKCgoICJCYmcm/B586dk7G7ceOG3JoipeM+efKkzBTohQsXZKZDFEnv3r3LjFCUpm7dupx2y6JFi2TCKmqDvvrqK5mjVz6cpv3nn38qfDmpiFGjRuHly5fo3bs3iAjFxcXo1q0bF+7j41Oh1tKHjBw5EmfOnMEvv/yC77//vky4dH3Zpk2bmP1lZGTA3d0d9+/fr9Ke9bkRCoXMz5c85Vib4dfMfAKKi4uRmJgoI7TUunVrmTeamsiLFy9kFtqePHkS7969g6ura5ULcP8rihbWUgXyionVRhQl2JeXlwc1NTU8ePAAGhoaFZ6DEx4eDnV1dbl/sCviY4XwaiK8IB3Pf4HvzHzGFBUVQV2dHzzj4eHhqe5IF/0qyo5HFl407zPg2LFj3O4IiUSCkJAQ1K9fH5qamrC0tMSyZcs+i/Ub8lLRNI1EIsGDBw8+cWqqP4oQE1MU/0XQkIWjR49i3LhxmD17dpkt/K9evUK7du3kmgaMiIiQEU3ctWsXOnToACsrK3Ts2BF79+5VWNqlVCeRS0D5YnjloQxBuuPHj8vYhIeHw8XFBTo6OrC3ty+zkL4i5BGuk5Kfn4+ZM2eic+fOWLFiBQAgJCQEurq60NXVxdChQ/H69WtmOx45+GSKNjwV4ujoSOfOnSMioh9++IGMjIzop59+oqioKFqzZg2ZmprSsmXLVJxKdnJycmjQoEGkpaVF9erVo4ULF1JRUREXLq9oVW1F0WJiikSZQl27d+8mNTU16t27N3Xs2JG0tLRo165dXHhmZqbcgn1CoZDzsXnzZhKLxeTv708bNmyggIAA0tXVpa1btxIR0bp168jLy4sGDRpEJ0+elPHz/PlzsrOzY46zOohcEpFSxPBYUIYgnVAopKysLCIiioiIIDU1NZo2bRrt3r2bZs6cSZqamhQeHl5lXB9TxwMDA8nCwoJmzpxJTZs2pW+++Yasra1p165dFB4eTvb29jRt2jRmOx52+DmMz4DU1FRu90R4eDjWr1/Pzcn36NED9vb2CAgIwHfffafKZDKzYMECXLt2DTt37kR2djZCQkKQkJCAgwcPcpoyVA1Hmj41ihYTk4eq3l4/RtCQlZUrV2L16tWYNm0agJJ8GD16NN6/f4+xY8dydvKIzEkkEk63Zv369VizZg0mTJjAhbdt2xahoaHIzc3F3LlzMXr0aOTk5KBXr15YtGgR5s6dC6Bk/Rvr1myqJiKXALBq1SqFi+GxIE8esQrSlW5bVq9ejaCgIG63z9ChQ2FmZobVq1eX2XhRkT95iIiIwPbt2+Ht7Y0pU6bAwcEBBw8ehK+vLwDA2NgY48ePh0QiYbJjHUXi4XczfRYYGhri8ePHsLKywvPnz8sseGvUqJFSfzwUzR9//IHt27dzgmpffvklevfujb59+3LKl/yccNW8evUKWlpaAIDz588jMjKS26Jft25dLF26FB4eHkqJOyAgoEpBQ2Vx584d9OnTh7seOHAgjI2N4ePjg8LCQnz55ZcASgTaWFFXV+d2jzx+/LiMqGL79u2RlpaGjRs3YvPmzRg6dCgAYMqUKejXrx/evXuH4OBgue7D3d1drjS6urpCLBbLFYeiuHv3Lvr27QugZAddUVGRjCZO79698cMPPyg8XnnySKr3MmnSpAptPmxXKtNn6tevH5NwnTz8+++/aNSoEQCgQYMGUFNTg729PRfu4OCA58+fg4iY7HjkQJXDQjwlTJkyhfr06UNFRUU0YcIEGjduHEkkEi7c39+fXF1dVZhC+dDW1qbU1FSZ716/fk2urq7k6elJqampzMO327dvp3v37ikjmVWyZMkSiouLU0ncRETOzs60d+9eIiJq2rQpxcTEyISfP3+e6tatq5S4bW1tad++fRWGX7lyRWnTTObm5nThwoUy358+fZp0dXUpKChI7riHDRtGY8eOJSKiQYMG0fz582XCf/jhB2revDmJxWJKS0uTCbt58yaZmprSnDlzauwUqYGBAaWkpHDXurq6MtM5qamppK2tzeRLWc+shYUFHTp0qMJwaZ0UCAQUGxtL165dIxsbG7p8+bKM3a1bt0hXV5fZnzw0btyYe2YvXbpEIpGItm3bxoXv3buXO6OOxY6HHb4zo2JsbW1p2LBh1KJFC7K3t6fhw4eTlpYW2djYUNeuXcnOzo7q1KlD8fHxqk4qM40bN6ajR4+W+f7Nmzfk6upKLVq0YG4kBAIBiUQimjp1qsLSZ2trS2PGjKlyvYmtrS2JxWLq06ePwuKWh7CwMLK0tKTY2FjasWMHNW3alE6cOEGPHz+mU6dOUfPmzWncuHFKiXvAgAE0e/bsCsOvXr1KAoFAKXH7+vpyhzx+iPQQU3l/ZB4/fky2trbk7u5OM2bMILFYTB07dqTx48eTu7s7iUQiOnr0KFlZWdGZM2fK/H9SUhKZmprS8OHDa1RnRvostGjRgv744w/u+5ycHJkXqpiYGGrUqBGTT2U8s0REffv2pQULFlQYLq2TAoGA69QIBAJas2aNjF14eDg5Ojoy+5OH1atXk5aWFnl7e5OhoSGtXbuWzMzMaPbs2TRnzhzS19en4OBgZjtWWNu0mgw/zaRipKJMr1+/xuzZs3H48GHY2tpCIpGgoKAAfn5+mDx5styqmqpEeqBmr169ZL7X1dVFdHQ0unbtyuxLIpEgPT0d0dHRCksfqxBWWloa3r9/j7i4OIXFLQ+KFBOTl+DgYIULGrISGBiI8+fPlxvWpUsXHDlyBNu3b5fLp4WFBa5cuYJly5bh8OHDICJcunQJDx8+RIcOHThBwo4dOyIyMrKMSKWjoyNOnjyptGk9VSF9FjIzMxUmhqeMZxZgF6SztbWV+b70cQsAUFhYiO+++w52dnYKF4YMCAiAiYkJ4uPjMW7cOAwePBhOTk5YuHAh3r59i8DAQAQFBUEoFDLZsSKvuF9NhNeZ4VE4r169wpMnT2Tk0EuTm5uLhIQEdO7c+ROnrHrCi4l9Oq5fv46EhASMHj263PCkpCRERESUUczl4eFRLXxnhuez4/nz5zAwMICGhobS47p79y7Onz+PzMxMCAQCmJqaws3Nje8ofIZ8ynrBIx+qLhuqRGiuqKgIsbGxePDgAWxsbODh4QE1NbWP9sfzmaK6Ga7aQXJyMm3bto1u3bpFRCWLzyZNmkSjR48uo2FRUykoKKBDhw7R8uXLaefOnZSbm0tERBs3bqT3798TEZFEIqHQ0FAyMDAgoVBI2traFBgYSMXFxQpNy4MHD2j06NGUnZ1NPj4+JBAIyMDAgBo1akQODg5c/L6+vpSTk6PQuOXh2rVrct37zZs3qbCwUIkp+nSool4Q1fw8f/nyJa1evZqmTJlC33//PT148EBuH6oqGyKi9+/f04wZM8jd3Z2WL19ORETff/896ejokLa2Nvn5+VFOTg6zPhOrP3m5evUqff/997Ru3Tp6/vy5TFhOTg6NHj1aLruqkLZptR2+M6NEoqKiSCQSUd26dUlLS4uioqLIxMSEvL29ycvLi9TV1Wtkh8bV1ZVevXpFRETPnj2j5s2bk0gkIgcHB9LS0iJra2t69OiRjLjVb7/9Rjo6OrRq1So6d+4crV27lvT19Wnt2rUKTZtUCGv48OHUvHnzchdWx8fHk7OzM40YMUKhcctDdRJcUzSqqBfSeGtSnpubm9O///5LRCW7kczMzMjMzIy6du1KlpaWpK+vz71ksaKqsiFiF6QzNzen5ORkIiL66quvyNvbm+ssvHjxgvr06UMDBw5UinBddHQ0iUQiatasGVlbW5OxsTGdOnWKC5fuhmO1Y0GZApbVCX6aSYm4ubnB09MTISEh2Lt3L6ZMmYLJkycjNDQUABAUFITLly/j+PHjKk6pYhEKhcjMzES9evUwYcIEXL58GVFRUTAzM8OLFy/g4+ODJk2aICwsjLNr164d/Pz8OJEuANiyZQvWrl2La9euMcct1bGpiNTUVMycORN6enqIjo4uozciJT4+Hj169Kj0tFplIhQKMWHCBGbBtfXr1yM5ORkNGjRQcsqUT+n6o6h6wRpvTcrz0vno5+eHzMxMHD16FNra2sjPz8fAgQOhpaUll+iiqsoGAKytrbFt2zZ4e3sjNTW1jNBcTEwMxo8fj6ysLCQnJ8POzg5WVlaIjIxEu3btOD83b96Eh4cHxGIxk7/09HTmNLq5ucHDwwOhoaEgIqxcuRLBwcE4cOAAevTogaysLFhYWKB9+/ZMdsXFxcxtmiqPN/kc4HczKZGkpCTuZOOvvvoKw4cPx4ABA7hwPz8/bN26VVXJ+yTExcXhp59+4hQ7jYyMEBoayi2wlM5Lp6WlyYh0AYCnp6dMQ8mCPEJYlc2Jq3q+vDoJrikDRdcLFmpynl+8eBFbtmzhOmqampqYP3/+Ryn6qqJsAHZBukaNGuHSpUuws7ODnp5emTOO3rx5A4lEwuxPHpKSkrBz504AJfk0a9YsWFpaYuDAgdizZw/XqWK1A+Rr02ozfGfmEyEUCqGlpQUDAwPuOz09PeTk5DD9/44dO9ChQwc0bNhQSSlULNKHKzs7G3Z2djJhdnZ2ePr0KYCSQzb19fUhFovx7t07Gbt3795BKJTvLFRzc3OsW7cO/fr1Kzf86tWraN26Nfr27Yvx48dj69ataNOmjYzNP//8g0mTJsHHx0euuBWJVO30c0aZdVLR9YKF4OBgtGjRAvr6+gr3rSqkz2F+fj5MTU1lwkxNTeX6sZa+mKmibICSkZkLFy7A2toaly9fhkAgwKVLl7hdkxcvXkT9+vURGBiIb7/9Fqamppg7dy78/f2xdu1aNG3aFLdv38b06dPRv39/nD17lsmfPGhqapYZzfXz84NQKMSQIUOwatUquewA9jat1qPKOa6ajrOzM0VFRXHXN27ckFkwePbsWeZD65QlRKUMBAIB9erVi7788ksyNDSkv/76Syb8woULZGpqyolaST+hoaEydps3b6aWLVvKFTerENarV6+oR48eJBAIyNDQkBo3bkxNmjQhQ0NDEgqF1LNnT27dD0/5KKtOKqNesMZbt25dWrlypcJ9qwKBQEDNmzenli1bkq6uLh08eFAmPC4ujurXry+XPwAE4JOXDRG7IB0R0apVq0hbW5vEYjGJRCLuAEqhUEj9+vWjN2/eKFy4joioa9eutGLFinLDwsPDSUNDg4RCIbMdEXubVtvhR2aUyOTJk2XmMZ2cnGTCo6Ki4OnpyeRLWUJUymDkyJHc376+vsjNzZUJj4yMhIuLC44dO1apHzMzMyxdulSuuFmFtQwMDBAVFYWUlBRcuHABmZmZXJyurq5o0qSJXPHWRpRVJyUSSaXhH1MvWEhLS0NaWlq1eMZY+FAL58O1QIcPHy4jDlgZpct74sSJ5dooq2wAdkE6AJgxYwbGjBlTqT6TPP5YmTx5Ms6cOVNumJ+fHwBg06ZNzHYAe5tW2+EXAPN8cvLy8qCmpsYdosjDw8PDw/Nf4DsznxFSZdzSAm6tW7cuI8ddE7h+/TqcnJyY59eTkpLQuHFjqKt//GAi8UJYcvM51Mns7GwcOHCAEz0bNGiQwta1UIk8hdLWeVR3SovhqeKZlZfqkEZFwrdppVDdDFft4MmTJ7Rz5046evQo5efny4Tl5ubSkiVLqLCwkPz9/UksFpNAICBNTU0SiUQkEAhILBbT9OnTqaCgQEV3ID+JiYkyp2bv3LmT3NzcyNLSkjp06EB79uxRmqaHsoSwahuqrJMDBgygyMhIIio54NHY2JhMTEyoffv2ZGpqSmZmZpyOCCuFhYUUFBRE7u7u3CGWy5cvJ21tbRKJRDRixIgyz2dN4Pjx47Rw4UJOzyouLo569OhBHh4eMqc0s4jhfQ46PFUJzQkEArnSKL23/ypcJyU6OlpmXeTu3bupRYsWpK2tTQ0bNqSff/5ZLjsivk1jhR+ZUSKXL19Gt27dIJFIUFhYCEtLSxw6dIhbLS/VEpg6dSoiIyOxatUqdO/endvxlJ2djejoaMyaNQv9+/fHmjVrVHczctCqVSusWrUKHh4e2LJlC/z9/TF+/HhuN8GWLVuQl5enFE2PGTNmYN++ffDz88Nff/0FT09PHD58GD/88AOEQiEWLlyInj174pdfflHErdZYpk+frrI6aWJigvPnz8PBwQG9evWCoaEhwsLCIBKJUFhYiMmTJ+Phw4dyrW1ZsGABNm/ejK+//hrHjh2Du7s7jh49iqVLl0IikWDevHmYNm0aZs+erfD7URW7du3C6NGj4ezsjDt37mDt2rUIDAzEwIEDQUTYuXMndu/ejYEDB0JNTQ1Pnz5FvXr1sHHjRsycORPBwcH44osvkJiYiPnz5+P169cq1eE5fvw4+vbtCwcHB7x58wZv377F/v37ucM/s7KyYGZmhokTJzKlMSMjAwcPHoSDgwPy8/PL9SfVemGldD5GRkZi8ODBmDJlCpePv/76K8LCwjBs2DAmOz8/P75NY0XFnakajbe3N40ZM4aKi4vp9evXNGXKFDIyMqLExEQi+v8qj8bGxpUqAZ84cYKMjY0/VbL/M9ra2pSRkUFERC1btqSNGzfKhO/evZu0tbWpS5cucn2ePHlSZdxWVlYUExNDRET3798noVBIf/zxBxd+/PhxsrGxUdzN1lBUWSfFYjHdu3ePiEpUbKXPi5Tbt2+Tvr6+XD4bNGhAhw8fJiKiu3fvklAopL1793Lh+/fvJycnp/+W8M8MFxcX7g3/xIkTJBaL6aeffuLCV61aRR06dCCikp1KUmXftm3bytgRlexS0tHRUcozy4qrqyvNmzePiEpGj5YvX066urrcjtHMzEwCwJy2OnXqkLW1NT158qRCf/Iq65bOxw4dOnCjgFJWrFhBbdu2ZbYj4ts0VvjOjBIxNDSk27dvy3z3448/kqGhIV26dIl7WHR0dOjatWsV+rly5Qrp6OgoO7kKw8jIiP755x8iIqpXrx5dvXpVJvzevXskFouVErdYLOY6UkREGhoadPPmTe46LS2NtLW1lRJ3TUKVdbJ9+/a0adMmIirpDB86dEgm/Pjx42RmZiaXTy0tLZmziLS0tGSk/FNTU0lPT+/jE/0ZoqOjIzPdq6GhIVOmKSkpZGRkREQkMz1jbGxcpuzv379Purq6nyDVFVOnTh2ukyslPDycdHR06M8//5S786Fof0SynZl69epRQkKCTLi0I85qR8S3aazwq96UzPv372WuZ8+ejXnz5qFbt244f/48AMDDwwMzZsxAVlZWmf/PysrC7Nmzmbdwfw707NkTGzZsAAB07twZERERMuH79++XUdpUJFJhLQAyQlhS5BHCsrOzw9ixY/H48WOlpPVzRpV1csGCBZgzZw5+//13+Pv7IzAwEFu3bsX58+cRFhaGsWPHYvjw4XL51NfXlxEpa9WqFfT09Ljr/Pz8GreQUkNDAwUFBdy1pqamzMJtkUgkI3p37Ngx/Pnnn59cDI+VioTmtm7diiFDhuDQoUMq9SclOTkZ169fh1gsLiMzIJFIuGkrVjtFtmk1GlX3pmoynTp1og0bNpQbtnz5ctLU1CShUEgPHjwgJycnUldXJxcXF+revTv16NGDXFxcSF1dnZydnenhw4efOPUfz+PHj8nW1pbc3d1pxowZJBaLqWPHjjR+/Hhyd3cnkUhER48eVUrcihTCWrRoEY0aNYoaNGiglLR+zqi6TkZERJClpSUJhUIZAT0tLS0KCAigoqIiufx5eHjQ77//XmH4/v37qXXr1v812Z8Vbdq0kZmOyMnJIYlEwl3HxMRQo0aNiEh1QoXyII/QnCr8EZXkY+k6u2bNmjJ+HR0dme2IFNum1WT4BcBKZMuWLYiLi+PO4PiQ5cuXY8OGDUhLS4NEIkF0dDTi4+PLCLh169ZN5W9F8pKdnY1ly5bh8OHDZUSrAgMDyxwhoEh2796N+Ph4dOzYEYMHD8bp06c5Iay+fftiwYIF1S4/VYGq62RxcTESExNl6k/r1q1lRlRYuXPnDjQ0NMocrSElPDwc6urq+Oqrr/5rsj8bDh06BCMjI7i7u5cbvmzZMuTl5eH777+v0teRI0egoaGB7t27KzqZzBw6dAhnzpzB6tWryw3fs2cPNm3axCwgp2h/QMmi4tLo6urCyMiIu5YeCdG5c2cmuxEjRgDg2zQW+M4MDw8PDw8PT7WmdnflPgOuX79epXx7aZKSklBUVKTEFPEAwMOHDzFmzBhVJ0Ml8HWydsGXt/IoKipCTEwMtm7dihMnTlS4zZvVjqcSVDvLxfM5CFHVNKoS1mIRwrp69arc8+U1hZpaJ9etW0deXl40aNCgMtvOnz9/znzoa02jupS3PEJzqvBHRDRt2jQ6cuQIERE9fPiQmjRpQmpqamRqakpqamrUvHlzevToEbOdFEW0aTUdfppJxQiFQpUKUdU0WIS1LCwsqtypkJqaipkzZ9bKN6SaWCd/+eUXzJ07F6NHj0ZOTg4OHDiARYsWYe7cuQA+TiCtplBdyptVkE56UOOn9gcAFhYWOHnyJJo2bYrBgwfj5cuX2LNnD4yNjfHy5UuMHDkSWlpaOHfuHJPdgQMHmNu02lh3S8N3ZlRMly5d5N4SGh4eDnNzcyWlqHrj5uYGDw8PhIaGgoiwcuVKBAcH48CBA+jRowf34NP/nWlSWfUXCAS1soGoiXWyWbNmCAoKwtChQwEAFy5cQL9+/TBx4kQEBwfX6h+E6lLeQqEQmZmZqFevHjp27AgvLy8sWbKEC1+5ciX2798vs235U/oDALFYjOTkZNjZ2cHKygqRkZFo164dF37z5k14eHggNzeXye758+fMbVptrLsyqG5QiIdH8bAKYVlYWJQRYyvNlStXau00U01ELBZTWlqazHc3b94kU1NTmjNnzkcJpPF8WuQRmlOFPyIiZ2dnTlm6adOmnHKvlPPnz1PdunWZ7YiUI+5XE+EXAKuYHTt24P79+6pOxifnzJkzyMnJUbhfViGs1q1bIzExsUI/VY3a8KgWeQUNjY2N8fDhQ5nvmjVrhlOnTiEsLAyzZs1SRjI/e6pb+8MqNKcqf4GBgfj2229x+vRpzJ07F/7+/jh58iSePHmC2NhYTJw4Ef3792e2A5Qn7lfjUHVvqrYjEAhIJBLR1KlTVZ2UT4pAIKC6devSypUrFeqXVQjrzJkz3Bks5ZGbm0unT59WaNp4FIe8goZ+fn40ffr0csNu3rxJJiYmtfLttjq1P/IIzanCn5RVq1aRtrY2icViEolEJBQKuU+/fv3ozZs3ctkpQ9yvJqKu6s5UbUcikSA9PV2uE4BrAmlpaUhLS1P4fU+ePBlnzpwpN0y6kG/Tpk3o1KlTpX50dHTKCFvxfD4sXrxYLvs5c+YgISGh3LBmzZohNja2zLEbtYHq1P6kpaXJXJc+mgEACgsL8d1336nMn5QZM2ZgzJgxiImJKSMY6uDgILcda5tW2+EXAPPwfAD93+JgHh4eHp7qAb9m5hOQm5uLuLg47Nu3D/v370dcXBxyc3NVnaxPDhHJJc6lTPLz8zFz5kx07twZK1asAACEhIRAV1cXurq6GDp0KF6/fq3iVNZebt26hbCwMKSkpAAAUlJSMHnyZIwZMwanTp2SyxcvCifL8+fPUVhYqOpk/GcULTT3X/2x1jOpHWs9q+n1UWGodparZlNYWEj+/v4kFotJIBCQpqYmiUQiEggEJBaLafr06VRQUKDqZCqcwsJCCgoKInd3d1q4cCERlRysqa2tTSKRiEaMGEH5+flKiZtVCCswMJAsLCxo5syZ1LRpU/rmm2/I2tqadu3aReHh4WRvb0/Tpk1TShp5KicqKopEIhHVrVuXtLS0KCoqikxMTMjb25u8vLxIXV29jOhdZVQXUThFs3HjRnr//j0REUkkEgoNDSUDAwMSCoWkra1NgYGBVFxcrOJUsiOv0Nyn9sdaz6R2rPVMLBbT7du3uWtFiPvVRPhpJiUyffp0REZGYtWqVejevTsMDAwAlBzCGB0djVmzZqF///5Ys2aNStOpaBYsWIDNmzfj66+/xrFjx+Du7o6jR49i6dKlkEgkmDdvHqZNm4bZs2crPG5WIazvvvsO27Ztg7e3N1JTU+Hg4ICDBw/C19cXABATE4Px48cjPT1d4WnkqRw3Nzd4enoiJCQEe/fuxZQpUzB58mSEhoYCAIKCgnD58mUcP36cyV91EYVTNKWfhY0bN2LmzJkIDg7mnoX58+cjJCQEU6dOVXVSmWAVpDtw4IBK/LHWs9WrV6N58+ZITk7GyJEjoa+vX6X9xYsX0a5dO4WJ+9VE+M6MEjExMcG+ffvg6elZbvjJkycxZMgQPH/+/BOnTLk0bNgQP//8M/r06YN79+6hcePGCA8Px+DBgwEABw4cQHBwMG7cuKHwuFmFsG7evImUlBRYW1sDAEQiEa5cuYJmzZoBANLT09GsWTPk5eUpPI08laOvr4+EhATY29tDIpFAU1MTFy9eRKtWrQCUCIp5e3tzJ3lXRXURhVM0pZ+Fdu3awc/PD4GBgVz4li1bsHbtWly7dk2FqWSHVZCOtT1VtD/WenblyhXOztHRESKRqFL706dP4/r162jevLnCxP1qIvxuJiXy7t07GBsbVxhuZGSEd+/efcIUfRqePHmCFi1aAADs7e0hEom4awBo06YNMjIylJ6Ou3fv4pdffpH5zsfHByEhIbC2tsaFCxdgbW2Ny5cvQyAQ4NKlS1xn5uLFi6hfv77S08hTOUKhEFpaWtyoJgDo6enJpVF0+vRpxSesmiD90UxLS4OXl5dMmKenp0zn5nOnUaNGuHTpEuzs7KCnp1dmTdubN2/kWhulaH/KqmdCoRCmpqYAKm/Tajt8Z0aJeHh4YMaMGdi9ezdXGaVkZWVh9uzZFY7aVGf09fWRnZ0NKysrAECrVq2gp6fHhefn5yt1t1BycjIyMzMrFcKaNGkSRo0ahS1btiAhIQGrVq3CvHnzkJKSAqFQiA0bNmDmzJlKSyNPxdja2uLevXuwt7cHAK7TKeXhw4fVftTkU3Hs2DHo6+tDLBaXeXF69+4dhMLqswdEKjRnamrKCc2tXbsWTZs2xe3btzF9+nROaE4V/pQJS5tW2+E7M0pk/fr16NWrFywtLeHk5ARTU1MIBAJkZmbi5s2bcHR0xNGjR1WdTIXj6OiIxMRENG/eHABw7tw5mfAbN27I6CgoGi8vL06999y5c2jTpg0XduXKFVhbWyMgIAAmJiaIj4/HuHHjMHjwYDg5OWHhwoV4+/YtAgMDERQUpLQ08lTM5MmTZRpnJycnmfCoqKga+RKgDEaOHMn9ffLkSbRv3567vnDhAho2bKiKZH0Uo0aNwsuXL9G7d28QEYqLi9GtWzcu3MfHB6tXr1aZP2XC0qbVdvg1M0pGIpEgOjoa8fHx3By/mZkZXF1d0a1bt2r1ZsTKnTt3oKGhATs7u3LDw8PDoa6ujq+++krhcX84faWrqwsjIyPueseOHQCAESNGKDxuHp7qxJEjR6ChoYHu3burOilykZ2dXaXQnCr9KRq+TWOD78zw8PDw8PDwVGtq3rDAZwIv1PV5UJkQ1rVr1xASEoL169fj33//lfm/169fY8yYMZ86uTz/x9OnT7Fr1y789ddfKCgokAnLy8tDcHCwilJWPahp7Y+i76c654+ixQJrDKqRt6n51FahLinr1q0jLy8vGjRoUBmBs+fPn5OdnZ1S4mUVwoqOjiaRSETNmjUja2trMjY2plOnTnF+MjMz+cPbVMSlS5fIwMCA6tSpQ2KxmBwcHOjmzZtcOF82VVPT2h9F3091yh9Fi/vVVPgFwEqCiLBgwQJmoa4P3z6rM7/88gvmzp2L0aNHIycnB7169cKiRYswd+5cAEBxcbHStmZHRERg8uTJAICZM2fC0tISZ8+elRHCCggIwOPHj/Htt98iNDQURISVK1fCx8cHBw4cQI8ePZSSNh425s2bh/79+2Pz5s3Iy8vDnDlz0LlzZ8TExKBly5aqTl61oKa1P4q+n+qUP6xtGqu4lblSFgAAEGZJREFUX02F78woCXd3d9y+fZvZ3tXVFWKxWIkp+nRs3LgRmzdvxtChQwEAU6ZMQb9+/fDu3TulTw+8evUKWlpaAIDz588jMjKS0/qpW7culi5dCg8PDxQUFGDnzp0ASrQ4Zs2aBUtLSwwcOBB79uyREc/i+bQkJCRg3bp1EAqF0NPTw7p162BjYwMvLy9ER0fzOzcYqGntj6LvpzrlD2ubVtvhOzNKojYLdaWlpcHNzY27dnV1xalTp+Dl5YXCwkIEBAQoLW5WISxNTU1kZ2fLhPn5+UEoFGLIkCFYtWqV0tLIUzXv37+XuZ49ezaEQiG6deuGbdu2qShV1Yea1v4o+n6qU/4oWtyvpsJ3ZngUjrGxMR4+fAhbW1vuu2bNmuHUqVPw9PTE48ePlRY3qxBWRkYGYmNj0bp1a5n/Hzx4MCQSiYw+B8+nxcnJCefPn4ezs7PM999++y2IqNafQcNTu6hO4n6qhO/M8Cicjh07IjIyEp06dZL53tHRESdPnlTqkCirEFZMTAzOnDlTrg/pj+WmTZuUlk6eihkxYgTi4uIwadKkMmGzZs0CEWHDhg0qSBkPz6enOon7qRJeZ4ZH4Vy/fh0JCQkYPXp0ueFJSUmIiIjAokWLlJaGz10Ii4eHh0ce+DatcvjODA8PDw8PD0+1hhfN41EoqhSjkifu48eP49q1a1zc4eHhcHFxgY6ODuzt7cucTMvDw8PzqanO4n6fGn5khkehqKmpITMzEyYmJkz2derUwdWrV9GgQYNPGreamhrEYjGuX7+OK1euYPDgwZgyZQq++OILJCYm4tdff0VYWBi/2JSHh0dlqLI9rW7wC4B5FIoqxajkiVsikaCwsBAAsHr1agQFBWHJkiUAgKFDh8LMzAyrV6/mOzM8PDwqozqJ+6kavjPDo1BUKUYlb9xt2rSBWCzG3bt3y0wr+fj4ICQkRCHp4uHh4fkYqpO4n6rhOzM8CkWVYlTyxC0UChEaGornz59DLBaXmZeWSCT8AW6fMTt27ECHDh3QsGFDVSeFh0dpVCdxP1XDLwDmqbV4eXnBxcUFDx48wLlz52TCrly5wsvmf8aMGjUKjo6OmDZtmqqTwsPD8xnAj8zw1ErS0tJkrnV1dWWuCwsL8d13333KJPHIgUQiQXp6OqKjo1WdFB4ens8AfjcTDw8PDw8PT7WGH5nh4QFQVFSE2NhYPHjwADY2NvDw8ICampqqk1Wryc3NRUJCAjIzMyEQCGBqaorWrVuXGUXj4eHh4TszPLUSf39/dO/eHb1798ajR4/QtWtX3L17F8bGxvj333/h6OiIqKgo1K9fX9VJrXUUFRVh5syZ2Lx5M96/fw+RSAQiQmFhIbS0tDBhwgSsWLECGhoaqk4qDw/PZwK/AJinVhIREcEJS82cOROWlpbIzMxEZmYmnj17BhsbGwQEBKg2kbWUmTNnIjIyEmFhYXj58iXev3+P/Px8vHz5EmFhYTh48CBmzZql6mTy8PB8RvBrZnhqJWKxGMnJybCzs4OVlRUiIyPRrl07LvzmzZvw8PDA8+fPVZjK2omJiQn27dsHT0/PcsNPnjyJIUOG8GXDw8PDwY/M8NRKGjVqhEuXLgEA9PT08Pr1a5nwN2/eyHUmCo/iePfuHYyNjSsMNzIywrt37z5hinh4eD53+M4MT60kMDAQ3377LU6fPo25c+fC398fJ0+exJMnTxAbG4uJEyeif//+qk5mrcTDwwMzZsxAVlZWmbCsrCzMnj27wlEbHh6e2gk/zcRTa/npp5+wYMECEBGKi4tlTpv18fHBzp07+Z0zKuDhw4fo1asXUlJS4OTkBFNTUwgEAmRmZuLmzZtwdHTE0aNHYWlpqeqk8vDwfCbwnRmeWk12djZiYmKQmpoKiUQCc3NzdOjQAQ4ODqpOWq1GIpEgOjoa8fHxyMzMBACYmZnB1dUV3bp1g1DIDyrz8PD8f/jODA8PDw8PD0+1hn+94al1XL9+Xa7FvUlJSTJTUDzKgy8bHh6ej4EfmeGpdaipqSEzMxMmJiZM9nXq1MHVq1c5XRoe5cGXDQ8Pz8fAKwDz1DqICAsWLIC2tjaTfUFBgZJTxCOFLxseHp6Pge/M8NQ63N3dcfv2bWZ7V1dXiMViJaaIRwpfNjw8PB8DP83Ew8PDw8PDU63hFwDz8PDw8PDwVGv4zgwPDw8PDw9PtYbvzPDw8PDw8PBUa/jODA8PDw8PD0+1hu/M8PDw8PDw8FRr+M4MDw8PjwIRCAT4448/VJ0MHp5aBd+Z4eGpQTx79gwTJ06EtbU1NDU1YWZmhu7du+PChQuqTppcdOnSBQKBoMxn0qRJzD5sbW2xZs0a5SWyAp4+fYqePXt+8nh5eGozvGgeD08NYsCAASgsLMT27dvRoEEDZGVl4eTJk3j58uVH+ywsLISGhoYCU8nme/z48QgODpb5jlUZWJWYmZmpOgk8PLUOfmSGh6eGkJ2djb///hs//vgjPDw8YGNjg3bt2mHu3Lno3bs3Z5eTk4MJEyagXr16qFOnDjw9PXHt2jUufPHixXBxccG2bdvQoEEDaGpqYuPGjahfv36ZQyB9fHwwcuRI7vrw4cNo3bo1tLS00KBBAyxZskTmIEiBQIDffvsNvr6+0NHRQUhISIX3o62tDTMzM5lPnTp1AAA7duyArq4u7t69y9lPmzYNjRo1Ql5eHrp06YKMjAwEBgZyozpSzp8/D3d3d4jFYlhZWcHf3x95eXlcuK2tLX744QeMGTMGenp6sLa2xqZNm7jwgoICTJ06Febm5tDS0oKtrS2WLl0qc4+lp5lu3LgBT09PiMViGBkZYcKECcjNzeXCR40ahX79+mHlypUwNzeHkZERvvnmGxQWFnI269evh4ODA7S0tGBqaoqBAwdWmG88PLUS4uHhqREUFhaSrq4uBQQE0Pv378u1kUgk1KFDB+rbty9dvnyZ7ty5QzNnziQjIyN68eIFEREtWrSIdHR0qHv37pSYmEjXrl2jf//9l0QiEZ04cYLz9fLlSxKJRBQdHU1ERMeOHaM6derQ77//Tvfv36fjx4+Tra0tLV68mPsfAFSvXj3aunUr3b9/n9LT08tNZ+fOnWn69OmV3u+gQYOobdu2VFhYSFFRUaShoUGXLl0iIqIXL16QpaUlBQcH09OnT+np06dERHT9+nXS1dWl1atX0507d+jcuXPUsmVLGjVqFOfXxsaG6tatS+vWraO7d+/S0qVLSSgU0q1bt4iIaMWKFWRlZUVnzpyh9PR0Onv2LIWHh8vc46FDh4iIKC8vjywsLKh///5048YNOnnyJNnZ2dHIkSM5+5EjR1KdOnVo0qRJdOvWLTp8+DBpa2vTpk2biIjo8uXLpKamRuHh4ZSenk6JiYn0888/V5o3PDy1Db4zw8NTg4iIiCBDQ0PS0tIiNzc3mjt3Ll27do0LP3nyJNWpU6dMZ6dhw4a0ceNGIirpzGhoaNCzZ89kbHx8fGjMmDHc9caNG8nMzIyKioqIiKhTp070ww8/yPzPzp07ydzcnLsGQAEBAVXeR+fOnUlDQ4N0dHRkPr///jtn8/LlS7K0tKTJkyeTqakphYSEyPiwsbGh1atXy3w3fPhwmjBhgsx3Z8+eJaFQSO/eveP+b9iwYVy4RCKhevXq0YYNG4iIaNq0aeTp6UkSiaTctJfuzGzatIkMDQ0pNzeXCz969CgJhULKzMwkopLOjI2NDZePRCUdtcGDBxMRUWRkJNWpU4dev35dZb7x8NRW+GkmHp4axIABA/DkyRP8+eef6N69O06fPo1WrVrh999/BwAkJCQgNzcXRkZG0NXV5T5paWm4f/8+58fGxgYmJiYyvr/++mtERkYiPz8fALB7924MGTIEampqnO/g4GAZv+PHj8fTp0/x9u1bzk+bNm2Y7uXrr7/G1atXZT5ffvklF25oaIitW7diw4YNaNiwIebMmVOlz4SEBPz+++8yaezevTskEgnS0tI4O2dnZ+5vgUAAMzMzPHv2DEDJtNDVq1fRuHFj+Pv74/jx4xXGd+vWLbRo0QI6Ojrcdx06dIBEIpE5ULNZs2ZcPgKAubk5F1/Xrl1hY2ODBg0aYPjw4di9e7dMfvLw8PALgHl4ahxaWlro2rUrunbtioULF2LcuHFYtGgRRo0aBYlEAnNzc5w+fbrM/xkYGHB/l/7xldK3b19IJBIcPXoUbdu2xdmzZ/HTTz9x4RKJBEuWLEH//v3LTVNlvstDX18f9vb2ldqcOXMGampqePLkCfLy8rg1NRUhkUgwceJE+Pv7lwmztrbm/v5wUbJAIODWC7Vq1QppaWmIiorCiRMn8NVXX8Hb2xsRERFlfBKRzHqdD32yxKenp4fExEScPn0ax48fx8KFC7F48WJcvnxZpsx4eGozfGeGh6eG4+joyC1IbdWqFTIzM6Gurg5bW1u5/IjFYvTv3x+7d+/GvXv30KhRI7Ru3ZoLb9WqFW7fvl1lB0RRnD9/HsuXL8fhw4cxZ84cTJs2Ddu3b+fCRSIRiouLZf6nVatWSEpK+s9prFOnDgYPHozBgwdj4MCB6NGjB16+fIm6devK2Dk6OmL79u3Iy8vjOnHnzp2DUChEo0aNmONTV1eHt7c3vL29sWjRIhgYGODUqVPldhx5eGojfGeGh6eG8OLFCwwaNAhjxoyBs7Mz9PT08M8//2D58uXw9fUFAHh7e8PV1RX9+vXDjz/+iMaNG+PJkyf466+/0K9fvyqngL7++mv07dsXSUlJGDZsmEzYwoUL0adPH1hZWWHQoEEQCoW4fv06bty4UemupYp4+/YtMjMzZb7T1NSEoaEh3rx5g+HDh2PatGno2bMnrK2t0aZNG/Tp0weDBg0CULIr6cyZMxgyZAg0NTVhbGyM7777Dl988QW++eYbjB8/Hjo6Orh16xZiYmKwdu1apnStXr0a5ubmcHFxgVAoxIEDB2BmZlbuKMnXX3+NRYsWYeTIkVi8eDGeP3+OadOmYfjw4TA1NWWK78iRI0hNTYW7uzsMDQ3x119/QSKRoHHjxkz/z8NTG+DXzPDw1BB0dXXRvn17rF69Gu7u7nBycsKCBQswfvx4/PrrrwBKpi/++usvuLu7Y8yYMWjUqBGGDBmC9PR0ph9XT09P1K1bF7dv38bQoUNlwrp3744jR44gJiYGbdu2xRdffIGffvoJNjY2H3U/mzdvhrm5uczHz88PADB9+nTo6Ojghx9+AFCy5uTHH3/EpEmT8PjxYwBAcHAw0tPT0bBhQ279j7OzM+Li4nD37l106tQJLVu2xIIFC2Bubs6cLl1dXfz4449o06YN2rZti/T0dPz1118QCss2p9ra2oiOjsbLly/Rtm1bDBw4EF5eXlx5sGBgYICDBw/C09MTTZs2xW+//YY9e/agWbNmzD54eGo6AiIiVSeCh4eHh4eHh+dj4UdmeHh4eHh4eKo1fGeGh4eHh4eHp1rDd2Z4eHh4eHh4qjV8Z4aHh4eHh4enWsN3Znh4eHh4eHiqNXxnhoeHh4eHh6daw3dmeHh4eHh4eKo1fGeGh4eHh4eHp1rDd2Z4eHh4eHh4qjV8Z4aHh4eHh4enWsN3Znh4eHh4eHiqNf8PIdPDj0MorcEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grpd_df = train_data.groupby(['server_extensions', 'label']).size().reset_index(name = 'count_of_labels_per_attribute_grp')\n",
    "malicious = grpd_df[grpd_df.label == 'malicious']\n",
    "benign = grpd_df[grpd_df.label == 'benign']\n",
    "\n",
    "mal_benign_df = pd.merge(malicious, benign, on = 'server_extensions', how = 'outer', suffixes = ('_malicious', '_benign')).fillna(0).drop(['label_malicious', 'label_benign'], axis = 1)\n",
    "\n",
    "malicious_count = mal_benign_df['count_of_labels_per_attribute_grp_malicious'].apply(lambda x: np.log(x+1)).values\n",
    "benign_count = mal_benign_df['count_of_labels_per_attribute_grp_benign'].apply(lambda x: np.log(x+1)).values\n",
    "\n",
    "X_axis = np.arange(len(mal_benign_df['server_extensions']))\n",
    "\n",
    "plt.bar(X_axis - 0.2, malicious_count, 0.4, label = 'Malicious', color = 'red')\n",
    "plt.bar(X_axis + 0.2, benign_count, 0.4, label = 'Benign')\n",
    "\n",
    "plt.xticks(X_axis, mal_benign_df['server_extensions'], rotation = 90)\n",
    "plt.xlabel(\"Server Extensions\")\n",
    "plt.ylabel(\"Count of unique connections\")\n",
    "plt.title(\"Number of connections in each group\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "6adf8b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data[train_data.label == 'malicious']['label'], np.log(train_data[train_data.label == 'malicious']['cert_len']), 'ro');\n",
    "plt.plot(train_data[train_data.label == 'benign']['label'], np.log(train_data[train_data.label == 'benign']['cert_len']), 'go');\n",
    "plt.xlabel('Class Label');\n",
    "plt.ylabel('cert_len');\n",
    "plt.title('cert_len wrt Class Labels');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "82e7fcf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data[train_data.label == 'malicious']['label'], train_data[train_data.label == 'malicious']['subject_common_name_cnt'], 'ro');\n",
    "plt.plot(train_data[train_data.label == 'benign']['label'], train_data[train_data.label == 'benign']['subject_common_name_cnt'].apply(np.log), 'go');\n",
    "plt.xlabel('Class Label');\n",
    "plt.ylabel('Log of subject_common_name_cnt');\n",
    "plt.title('Log of subject_common_name_cnt wrt Class Labels');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f912bf",
   "metadata": {},
   "source": [
    "### Machine Learning Model Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5aaa459",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "884695db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['insert_id', 'timestamp', 'malware_family', 'malware_group',\n",
       "       'malware_info', 'src_ip', 'src_port', 'dst_ip', 'dst_port', 'p1', 'p2',\n",
       "       'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10', 'd1', 'd2', 'd3', 'd4',\n",
       "       'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'label', 'test',\n",
       "       'client_cipher_len', 'client_ciphers', 'client_extension_len',\n",
       "       'client_extensions', 'server_cipher', 'server_extension_len',\n",
       "       'server_extensions', 'cert_len', 'subject_common_name_cnt',\n",
       "       'binary_label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1d19d0",
   "metadata": {},
   "source": [
    "Converting all the categories to string type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "76005589",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['client_ciphers'] = train_data['client_ciphers'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "e669105b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['client_extensions'] = train_data['client_extensions'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "60def3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['server_cipher'] = train_data['server_cipher'].astype(int).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "61eb7ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['server_extensions'] = train_data['server_extensions'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "729e4398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['insert_id', 'timestamp', 'malware_family', 'malware_group',\n",
       "       'malware_info', 'src_ip', 'src_port', 'dst_ip', 'dst_port', 'p1', 'p2',\n",
       "       'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10', 'd1', 'd2', 'd3', 'd4',\n",
       "       'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'label', 'test',\n",
       "       'client_cipher_len', 'client_ciphers', 'client_extension_len',\n",
       "       'client_extensions', 'server_cipher', 'server_extension_len',\n",
       "       'server_extensions', 'cert_len', 'subject_common_name_cnt',\n",
       "       'binary_label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "0306e779",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload_features_data = train_data[['client_cipher_len', 'client_extension_len',\n",
    "        'server_cipher', 'server_extension_len', 'cert_len', 'subject_common_name_cnt']].copy()\n",
    "label_info_data = train_data['binary_label'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb8432d",
   "metadata": {},
   "source": [
    "### One hot Encoding the Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "6a194cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload_features_data = pd.get_dummies(data=payload_features_data, columns=['server_cipher']).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdcd3675",
   "metadata": {},
   "source": [
    "### Normalization of numerical features using Standard Scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "50797f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Scaling the numerical features for model to help with faster model convergence\n",
    "std_scaler = StandardScaler()\n",
    "normalizedd_numerical_payload_data = pd.DataFrame(std_scaler.fit_transform(payload_features_data[['client_cipher_len', 'client_extension_len', 'cert_len',\n",
    "       'server_extension_len', 'subject_common_name_cnt']]), columns = ['client_cipher_len', 'client_extension_len', 'server_extension_len', 'cert_len',\n",
    "       'subject_common_name_cnt'])\n",
    "\n",
    "#### Not scaling the one hot encoded categorical features\n",
    "### Joining both the dataframes (numerical and categorical) together into a numpy array\n",
    "### Only including the Ciphers that represented both the classes\n",
    "47,49161, 49171, 49172, 49191, 49195, 49199, 49200\n",
    "normalizedd_categorical_payload_data = payload_features_data[[\n",
    "       'server_cipher_47', 'server_cipher_49161',\n",
    "       'server_cipher_49171', 'server_cipher_49172', 'server_cipher_49191',\n",
    "      'server_cipher_49195', 'server_cipher_49199', 'server_cipher_49200']].copy()\n",
    "\n",
    "normalized_payload_data = normalizedd_numerical_payload_data.join(normalizedd_categorical_payload_data).to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bcc6eb",
   "metadata": {},
   "source": [
    "### Sampling the train and test sets using Stratified Sampling technique to account for equal proportions of both the classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1b9358",
   "metadata": {},
   "source": [
    "> - Creating 50 splits of the data to abide by Central Limit Theorem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "d37d9d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Shuffling the data and creating train and test splits using Stratified Sampling technique\n",
    "### I am taking 50 samples here to abide by Central Limit Theorem\n",
    "strat_sampling_obj = StratifiedShuffleSplit(n_splits=50, test_size=0.3, random_state=0)\n",
    "strat_sampling_obj.get_n_splits(normalized_payload_data, label_info_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3c0f56",
   "metadata": {},
   "source": [
    "### Random Forest Classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "0a2ba950",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Random Forest Classification Model with CrossValidation\n",
    "rand_for_recall_scores = []\n",
    "rand_for_precision_scores = []\n",
    "rand_for_f1_scores = []\n",
    "for train_index, test_index in strat_sampling_obj.split(normalized_payload_data, label_info_data):\n",
    "    ### Create Train and Test samples\n",
    "    X_train, X_test = normalized_payload_data[train_index], normalized_payload_data[test_index]\n",
    "    y_train, y_test = label_info_data[train_index], label_info_data[test_index]\n",
    "    ### Train and test Random Forest Classifier for each sample\n",
    "    clf=RandomForestClassifier(random_state=0)\n",
    "    clf.fit(X_train, y_train)\n",
    "    pred_label = clf.predict(X_test)\n",
    "    rand_for_recall_scores.append(recall_score(y_test, pred_label))\n",
    "    rand_for_precision_scores.append(precision_score(y_test, pred_label))\n",
    "    rand_for_f1_scores.append(f1_score(y_test, pred_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "aff74d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9857988165680474, 0.98856786286347, 0.9871654928718828)"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(rand_for_recall_scores), np.mean(rand_for_precision_scores), np.mean(rand_for_f1_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "bc3ee792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5));\n",
    "plt.plot(rand_for_recall_scores, '-o');\n",
    "plt.plot(rand_for_precision_scores, '-go');\n",
    "plt.plot(rand_for_f1_scores, '-ro');\n",
    "plt.xlabel(\"Iteration Index\");\n",
    "plt.ylabel(\"Recall/Precision/F1-Score Score\");\n",
    "plt.title('Accuracy Scores for each Iteration');\n",
    "plt.legend(['Recall', 'Precision', 'F1-Score']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6bab6d",
   "metadata": {},
   "source": [
    "#### Feature Importance sorted in the order of their importance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "7cafdcae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['client_cipher_len', 'client_extension_len', 'server_extension_len',\n",
       "       'cert_len', 'subject_common_name_cnt', 'server_cipher_0',\n",
       "       'server_cipher_10', 'server_cipher_47', 'server_cipher_49161',\n",
       "       'server_cipher_49162', 'server_cipher_49171', 'server_cipher_49172',\n",
       "       'server_cipher_49191', 'server_cipher_49192', 'server_cipher_49195',\n",
       "       'server_cipher_49196', 'server_cipher_49199', 'server_cipher_49200',\n",
       "       'server_cipher_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payload_features_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "ece0f024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_importance_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>subject_common_name_cnt</th>\n",
       "      <td>0.313293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_extension_len</th>\n",
       "      <td>0.209988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_extension_len</th>\n",
       "      <td>0.201720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49161</th>\n",
       "      <td>0.146296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cert_len</th>\n",
       "      <td>0.043464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_cipher_len</th>\n",
       "      <td>0.035172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_47</th>\n",
       "      <td>0.032375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49199</th>\n",
       "      <td>0.005839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49200</th>\n",
       "      <td>0.005417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49172</th>\n",
       "      <td>0.003560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49171</th>\n",
       "      <td>0.001758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49195</th>\n",
       "      <td>0.000659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49191</th>\n",
       "      <td>0.000458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature_importance_score\n",
       "subject_common_name_cnt                  0.313293\n",
       "client_extension_len                     0.209988\n",
       "server_extension_len                     0.201720\n",
       "server_cipher_49161                      0.146296\n",
       "cert_len                                 0.043464\n",
       "client_cipher_len                        0.035172\n",
       "server_cipher_47                         0.032375\n",
       "server_cipher_49199                      0.005839\n",
       "server_cipher_49200                      0.005417\n",
       "server_cipher_49172                      0.003560\n",
       "server_cipher_49171                      0.001758\n",
       "server_cipher_49195                      0.000659\n",
       "server_cipher_49191                      0.000458"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(clf.feature_importances_, index=['client_cipher_len', 'client_extension_len', 'server_extension_len',\n",
    "       'cert_len', 'subject_common_name_cnt','server_cipher_47', 'server_cipher_49161',\n",
    "       'server_cipher_49171', 'server_cipher_49172', 'server_cipher_49191',\n",
    "      'server_cipher_49195', 'server_cipher_49199', 'server_cipher_49200']).sort_values(by=0, ascending=False).rename(columns = {0:'feature_importance_score'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03fac57",
   "metadata": {},
   "source": [
    "### XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "370f28bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "### XGBoost Classification Model with CrossValidation\n",
    "xgb_recall_scores = []\n",
    "xgb_precision_scores = []\n",
    "xgb_f1_scores = []\n",
    "for train_index, test_index in strat_sampling_obj.split(normalized_payload_data, label_info_data):\n",
    "    ### Create Train and Test samples\n",
    "    X_train, X_test = normalized_payload_data[train_index], normalized_payload_data[test_index]\n",
    "    y_train, y_test = label_info_data[train_index], label_info_data[test_index]\n",
    "    ### Train and test Random Forest Classifier for each sample\n",
    "    xgb_cl = xgb.XGBClassifier(random_state=0)\n",
    "    xgb_cl.fit(X_train, y_train)\n",
    "    pred_label = xgb_cl.predict(X_test)\n",
    "    xgb_recall_scores.append(recall_score(y_test, pred_label))\n",
    "    xgb_precision_scores.append(precision_score(y_test, pred_label))\n",
    "    xgb_f1_scores.append(f1_score(y_test, pred_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "e32a1ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9875147928994084, 0.9887906709154387, 0.9881316946662768)"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(xgb_recall_scores), np.mean(xgb_precision_scores), np.mean(xgb_f1_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd2b838",
   "metadata": {},
   "source": [
    "#### Feature Importance sorted in the order of their importance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "f035fa1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_importance_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>subject_common_name_cnt</th>\n",
       "      <td>0.749089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_47</th>\n",
       "      <td>0.137645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49199</th>\n",
       "      <td>0.042855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_extension_len</th>\n",
       "      <td>0.027707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_cipher_len</th>\n",
       "      <td>0.012885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_extension_len</th>\n",
       "      <td>0.009076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cert_len</th>\n",
       "      <td>0.008331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49161</th>\n",
       "      <td>0.005718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49172</th>\n",
       "      <td>0.004367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49200</th>\n",
       "      <td>0.001573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49171</th>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49191</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49195</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature_importance_score\n",
       "subject_common_name_cnt                  0.749089\n",
       "server_cipher_47                         0.137645\n",
       "server_cipher_49199                      0.042855\n",
       "server_extension_len                     0.027707\n",
       "client_cipher_len                        0.012885\n",
       "client_extension_len                     0.009076\n",
       "cert_len                                 0.008331\n",
       "server_cipher_49161                      0.005718\n",
       "server_cipher_49172                      0.004367\n",
       "server_cipher_49200                      0.001573\n",
       "server_cipher_49171                      0.000753\n",
       "server_cipher_49191                      0.000000\n",
       "server_cipher_49195                      0.000000"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(xgb_cl.feature_importances_, index=['client_cipher_len', 'client_extension_len', 'server_extension_len',\n",
    "       'cert_len', 'subject_common_name_cnt','server_cipher_47', 'server_cipher_49161',\n",
    "       'server_cipher_49171', 'server_cipher_49172', 'server_cipher_49191',\n",
    "      'server_cipher_49195', 'server_cipher_49199', 'server_cipher_49200']).sort_values(by=0, ascending=False).rename(columns = {0:'feature_importance_score'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "31cdd5e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5));\n",
    "plt.plot(xgb_recall_scores, '-o');\n",
    "plt.plot(xgb_precision_scores, '-go');\n",
    "plt.plot(xgb_f1_scores, '-ro');\n",
    "plt.xlabel(\"Iteration Index\");\n",
    "plt.ylabel(\"Recall/Precision/F1-Score Score\");\n",
    "plt.title('Accuracy Scores for each Iteration');\n",
    "plt.legend(['Recall', 'Precision', 'F1-Score']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a1c044",
   "metadata": {},
   "source": [
    "### Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "f3acbcd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Logistic Regression Classification Model with CrossValidation\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "log_reg_recall_scores = []\n",
    "log_reg_precision_scores = []\n",
    "log_reg_f1_scores = []\n",
    "for train_index, test_index in strat_sampling_obj.split(normalized_payload_data, label_info_data):\n",
    "    ### Create Train and Test samples\n",
    "    X_train, X_test = normalized_payload_data[train_index], normalized_payload_data[test_index]\n",
    "    y_train, y_test = label_info_data[train_index], label_info_data[test_index]\n",
    "    ### Train and test Random Forest Classifier for each sample\n",
    "    log_reg_clf = LogisticRegression(solver='lbfgs', random_state=0)\n",
    "    log_reg_clf.fit(X_train, y_train)\n",
    "    pred_label = log_reg_clf.predict(X_test)\n",
    "    log_reg_recall_scores.append(recall_score(y_test, pred_label))\n",
    "    log_reg_precision_scores.append(precision_score(y_test, pred_label))\n",
    "    log_reg_f1_scores.append(f1_score(y_test, pred_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "5138b51a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8340236686390533, 0.9685411344916866, 0.8961034159725447)"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(log_reg_recall_scores), np.mean(log_reg_precision_scores), np.mean(log_reg_f1_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c265c34",
   "metadata": {},
   "source": [
    "#### Feature Importance sorted in the order of their importance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "0c86cbca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_importance_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>subject_common_name_cnt</th>\n",
       "      <td>7.218297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_47</th>\n",
       "      <td>3.808563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49172</th>\n",
       "      <td>0.515397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cert_len</th>\n",
       "      <td>0.284665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_extension_len</th>\n",
       "      <td>0.255846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_cipher_len</th>\n",
       "      <td>-0.246947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49191</th>\n",
       "      <td>-0.719345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49161</th>\n",
       "      <td>-1.296394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49199</th>\n",
       "      <td>-1.611057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49171</th>\n",
       "      <td>-2.057299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49195</th>\n",
       "      <td>-2.623463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_cipher_49200</th>\n",
       "      <td>-2.865931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>server_extension_len</th>\n",
       "      <td>-7.142433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature_importance_score\n",
       "subject_common_name_cnt                  7.218297\n",
       "server_cipher_47                         3.808563\n",
       "server_cipher_49172                      0.515397\n",
       "cert_len                                 0.284665\n",
       "client_extension_len                     0.255846\n",
       "client_cipher_len                       -0.246947\n",
       "server_cipher_49191                     -0.719345\n",
       "server_cipher_49161                     -1.296394\n",
       "server_cipher_49199                     -1.611057\n",
       "server_cipher_49171                     -2.057299\n",
       "server_cipher_49195                     -2.623463\n",
       "server_cipher_49200                     -2.865931\n",
       "server_extension_len                    -7.142433"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(log_reg_clf.coef_[0], index=['client_cipher_len', 'client_extension_len', 'server_extension_len',\n",
    "       'cert_len', 'subject_common_name_cnt','server_cipher_47', 'server_cipher_49161',\n",
    "       'server_cipher_49171', 'server_cipher_49172', 'server_cipher_49191',\n",
    "      'server_cipher_49195', 'server_cipher_49199', 'server_cipher_49200']).sort_values(by=0, ascending=False).rename(columns = {0:'feature_importance_score'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "3f5ddae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5));\n",
    "plt.plot(log_reg_recall_scores, '-o');\n",
    "plt.plot(log_reg_precision_scores, '-go');\n",
    "plt.plot(log_reg_f1_scores, '-ro');\n",
    "plt.xlabel(\"Iteration Index\");\n",
    "plt.ylabel(\"Recall/Precision/F1-Score Score\");\n",
    "plt.title('Accuracy Scores for each Iteration');\n",
    "plt.legend(['Recall', 'Precision', 'F1-Score']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fdaf516",
   "metadata": {},
   "source": [
    "## <font color = 'Green'> Final Prediction on the test data </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4948a1cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('test.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2090082f",
   "metadata": {},
   "source": [
    "Decoding the payload into raw bytes and concatenatenating them into a TLS stream for further preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c47985",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['payload_concatenated'] = test_data[['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']\n",
    "          ].apply(lambda x: b''.join(\n",
    "    [base64.b64decode(p) if type(p) != float else b'' for p in x.values]), axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d78968",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fetching more features from TLS payload information\n",
    "test_data_payload = dict(test_data[['insert_id', 'payload_concatenated']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c64ebf30",
   "metadata": {},
   "outputs": [],
   "source": [
    "### For each insert id extract TLS handshake features\n",
    "result = Parallel(n_jobs=1, prefer='threads')(delayed(gen_attributes)(insert_id, payload) for insert_id, payload in test_data_payload.items())\n",
    "test_data_payload_attributes = {k: v for x in result for k, v in x.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329fab38",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Generate a feature dataframe from the TLS handshake features.\n",
    "test_data_features = pd.DataFrame.from_dict(test_data_payload_attributes, orient='index')\n",
    "test_data_final = pd.merge(test_data, test_data_features, on='insert_id', how='outer')\n",
    "### Save to file since extraction of TLS handshake features is time consuming\n",
    "test_data_final.to_csv(\"test_data_final.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2dc4b3a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('test_data_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b73a2b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57823, 39)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaff5c6d",
   "metadata": {},
   "source": [
    "> Data Preprocessing before prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "a1b3a3f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload_features_test_data = test_data[['client_cipher_len', 'client_extension_len',\n",
    "        'server_cipher', 'server_extension_len', 'cert_len', 'subject_common_name_cnt']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "64b2c6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload_features_test_data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "74ee380e",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload_features_test_data['server_cipher'] = payload_features_test_data['server_cipher'].astype(int).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "628b04bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(payload_features_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "5dbb942f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(payload_features_test_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "d90e8e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload_features_test_data = pd.get_dummies(data=payload_features_test_data, columns=['server_cipher']).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "aac52d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Scaling the numerical features for model to help with faster model convergence\n",
    "std_scaler = StandardScaler()\n",
    "normalized_numerical_payload_test_data = pd.DataFrame(std_scaler.fit_transform(payload_features_test_data[['client_cipher_len', 'client_extension_len', 'server_extension_len', 'cert_len',\n",
    "       'subject_common_name_cnt']]), columns = ['client_cipher_len', 'client_extension_len', 'server_extension_len', 'cert_len',\n",
    "       'subject_common_name_cnt'])\n",
    "\n",
    "#### Not scaling the one hot encoded categorical features\n",
    "### Joining both the dataframes (numerical and categorical) together into a numpy array\n",
    "normalized_categorical_payload_test_data = payload_features_test_data[[\n",
    "       'server_cipher_47', 'server_cipher_49161',\n",
    "       'server_cipher_49171', 'server_cipher_49172', 'server_cipher_49191',\n",
    "      'server_cipher_49195', 'server_cipher_49199', 'server_cipher_49200']].copy()\n",
    "\n",
    "normalized_payload_test_data = normalized_numerical_payload_test_data.join(normalized_categorical_payload_test_data).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "2e5906fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57823, 19)"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payload_features_test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb7948e",
   "metadata": {},
   "source": [
    "### Saving the predcitions file in CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "32b46d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame(xgb_cl.predict(normalized_payload_test_data), columns = ['predicted_label'])\n",
    "predictions['insert_id'] = test_data['insert_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26b0405d",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions['predicted_label'] = predictions['predicted_label'].map({0:'benign', 1:'malicious'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df6c6c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions[['insert_id', 'predicted_label']].to_csv('malware_detection_data/prediction.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df0220e",
   "metadata": {},
   "source": [
    "## <font color = 'Green'> Conclusion </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d151a7",
   "metadata": {},
   "source": [
    "- Logistic Regression\n",
    " - As we can see above from the accuracy scores that Logistic Regression Model performed the worst, reason being it expects a linear relationship between the logit of the dependent variable and independent variables.\n",
    " - This may or may not be true in the case of malware detection\n",
    "- Random Forest Classifier\n",
    " - In case of Random forest Classifier the accuracy scores are good but it is not as efficient as it generates multiple trees and then genrates a result which is a combination of all the weak classifiers.\n",
    "- XGBoost Classifier\n",
    " - This classifier performed the best in terms of accuracy score with a Recall of 98.7%, Precision of 99.0% and F1-Score of 98.9%\n",
    " - XGBoost is also more robust towards overfitting as it evaluates Gain at each level of the tree before adding a branch\n",
    " - XGBoost is also more efficient than Random Forest\n",
    " - Hence I am going to choose XGBoost over the other two models.\n",
    " \n",
    "<font color ='blue'> *Hurray we found a winner!!! And the winner is XGBoost Classifier!* </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af97511",
   "metadata": {},
   "source": [
    "## <font color = 'Green'> Future Work </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bd35d3a",
   "metadata": {},
   "source": [
    "### Deep Learning Model Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a273e4",
   "metadata": {},
   "source": [
    " - In this approach I am trying to convert the byte array of payload into an array of pixels to convert it into image.\n",
    " - The idea is to study the images and see if there is a similarity or a pattern that can be associated with malicious traffic\n",
    " - In the end I was planning to build a Convolutional Neural Network to disctinguish between malicious and benign network traffic based on the images built in the above step"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a437051e",
   "metadata": {},
   "source": [
    "### <font color = 'red'> Warning: The following code won't run, couldn't work on it due to the limitation of compute </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f63ab8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['payload_concatenated'] = train_data[['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']\n",
    "          ].apply(lambda x: b''.join(\n",
    "    [base64.b64decode(p) if type(p) != float else b'' for p in x.values]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d02047c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['payload_concatenated_normalized'] = train_data['payload_concatenated'].apply(\n",
    "    lambda x: np.array(list(x))/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "225fad83",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         None\n",
       "1         None\n",
       "2         None\n",
       "3         None\n",
       "4         None\n",
       "          ... \n",
       "102515    None\n",
       "102516    None\n",
       "102517    None\n",
       "102518    None\n",
       "102519    None\n",
       "Name: payload_concatenated_normalized, Length: 102520, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['payload_concatenated_normalized'].apply(lambda x: x.resize((128,128),refcheck=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25edc7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.stack(train_data['payload_concatenated_normalized'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2127b45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_data['label_numeric'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1ea0c0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102520, 128, 128)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67670524",
   "metadata": {},
   "outputs": [],
   "source": [
    "sss = StratifiedShuffleSplit(n_splits=5, test_size=0.5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75cae4a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sss.get_n_splits(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a97fc0aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [79581 16491 36306 ...  4311  4972 43490] TEST: [56381 36521 22346 ... 46690 56337 42681]\n",
      "TRAIN: [15000 96166 47026 ...  8869 65790  3675] TEST: [ 78469  30361  63865 ... 101201  62121  73555]\n",
      "TRAIN: [67843 35331 61735 ... 71395 42614 46011] TEST: [20949 40776 19965 ... 49984 23457 90300]\n",
      "TRAIN: [39221 92754 67153 ... 91573 42104 36424] TEST: [37318 81386 48479 ... 32890 10234 63733]\n",
      "TRAIN: [60915 50072 53196 ... 31318 28797 32124] TEST: [54540 23967 36734 ... 43154 62891 69348]\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in sss.split(X, y):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "826b88a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16384"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.product(X_train[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63d73376",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flattening the images from the 128x128 pixels to 1D 16384 pixels\n",
    "X_train = X_train.reshape(X_train.shape[0], np.product(X_train[0].shape))\n",
    "X_test = X_test.reshape(X_test.shape[0], np.product(X_test[0].shape))\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cecb2350",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalizing the data to help with the training\n",
    "X_train /= 255\n",
    "X_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e30bc4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51260, 16384)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ad08451",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# keras imports for the dataset and building our neural network\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Conv2D, MaxPool2D\n",
    "from keras.utils import np_utils\n",
    "\n",
    "\n",
    "\n",
    "# building a linear stack of layers with the sequential model\n",
    "model = Sequential()\n",
    "# hidden layer\n",
    "model.add(Dense(10, input_shape=(16384,), activation='relu'))\n",
    "# output layer\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "# looking at the model summary\n",
    "model.summary()\n",
    "# compiling the sequential model\n",
    "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')\n",
    "# training the model for 10 epochs\n",
    "model.fit(X_train, y_train, batch_size=128, epochs=10, validation_data=(X_test, y_test))\n"
   ]
  }
 ],
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