Web_Attack_Detection_(ML_IDS).ipynb

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  "metadata": {
    "colab": {
      "name": "Web Attack Detection (ML IDS).ipynb",
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      "collapsed_sections": [
        "SCEkDfpOFPQX",
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        "pnHSPBrDodIs"
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      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JosephBless/Deep-Learning-Colab/blob/main/Web_Attack_Detection_(ML_IDS).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGLYJuXmFBZI"
      },
      "source": [
        "# Web attack detection using CICIDS2017 dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNPL1uYLFF1U"
      },
      "source": [
        "Training data: \"Intrusion Detection Evaluation Dataset\" (CICIDS2017). Description page: https://www.unb.ca/cic/datasets/ids-2017.html\n",
        "\n",
        "The data set is public. Download link: http://205.174.165.80/CICDataset/CIC-IDS-2017/Dataset/\n",
        "\n",
        "CICIDS2017 combines 8 files recorded on different days of observation (PCAP + CSV). Used archive: http://205.174.165.80/CICDataset/CIC-IDS-2017/Dataset/GeneratedLabelledFlows.zip\n",
        "\n",
        "In the downloaded archive GeneratedLabelledFlows.zip the file \"Thursday\" Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv is selected.\n",
        "\n",
        "Sources:\n",
        "\n",
        "* [Sharafaldin2018] Iman Sharafaldin, Arash Habibi Lashkari and Ali A. Ghorbani. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. 2018\n",
        "* [Kostas2018] Kahraman Kostas. Anomaly Detection in Networks Using Machine Learning. 2018 (error was found in assessing the importance of features)\n",
        "* https://github.com/bozbil/Anomaly-Detection-in-Networks-Using-Machine-Learning (error was found in assessing the importance of features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCEkDfpOFPQX"
      },
      "source": [
        "## Data preprocessing\n",
        "\n",
        "Source: https://github.com/bozbil/Anomaly-Detection-in-Networks-Using-Machine-Learning/blob/master/01_preprocessing.ipynb [Kostas2018]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJVAToeYFV2Q"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egKqxprBYmmt"
      },
      "source": [
        "Download the dataset from Github to Google Colab and unzip it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JD0FmCDYmmt"
      },
      "source": [
        "!wget https://github.com/fisher85/ml-cybersecurity/blob/master/python-web-attack-detection/datasets/Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv.zip?raw=true -O dataset.zip\n",
        "!unzip -u dataset.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4vnx_OaGAl0"
      },
      "source": [
        "We use \"engine=python\" to avoid the \"UnicodeDecodeError: 'utf-8' codec can't decode byte 0x96 in position 11: invalid start byte\" encoding error."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIqeKHKYGCoz"
      },
      "source": [
        "df = pd.read_csv('Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv', engine='python')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3U-gQIAmhamL"
      },
      "source": [
        "Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv file contains 458968 records, 85 columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "QMkibc_W4hpQ",
        "outputId": "f8965510-6788-45e3-eda8-1d29ff1dd68a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(458968, 85)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5kIR-daheeV"
      },
      "source": [
        "The columns \"Fwd Header Length\" and \"Fwd Header Length.1\" are identical, the second one is removed, 84 columns remain."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "83UFLCVlhjKF",
        "outputId": "b0c3baf0-9395-48f9-a829-953fb764f634"
      },
      "source": [
        "df.columns = df.columns.str.strip()\n",
        "df = df.drop(columns=['Fwd Header Length.1'])\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(458968, 84)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIQQvtxZhiTZ"
      },
      "source": [
        "When assessing the distribution of labels, it turns out that out of 458968 records there are many blank records (\"BENIGN\" - benign background traffic)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "HrGLjk5nhrGE",
        "outputId": "b5754c62-bc6c-43b3-8c67-032c07ce20e6"
      },
      "source": [
        "df['Label'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['BENIGN', 'Web Attack – Brute Force', 'Web Attack – XSS',\n",
              "       'Web Attack – Sql Injection', nan], dtype=object)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "HnpQqQ8fhxwN",
        "outputId": "1875b6bf-6f59-466a-a633-8268a9a8b19d"
      },
      "source": [
        "df['Label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "BENIGN                        168186\n",
              "Web Attack – Brute Force        1507\n",
              "Web Attack – XSS                 652\n",
              "Web Attack – Sql Injection        21\n",
              "Name: Label, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7lpytF7h6D4"
      },
      "source": [
        "Delete blank records."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "7ltfHW8dh7Tw",
        "outputId": "d53015b1-03a4-4d7c-9440-ae0f3fcee7b7"
      },
      "source": [
        "df = df.drop(df[pd.isnull(df['Flow ID'])].index)\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(170366, 84)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZN6DKttXiH7J"
      },
      "source": [
        "The \"Flow Bytes/s\" and \"Flow Packets/s\" columns have non-numerical values, replace them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZ2ffToLi02x"
      },
      "source": [
        "df.replace('Infinity', -1, inplace=True)\n",
        "df[[\"Flow Bytes/s\", \"Flow Packets/s\"]] = df[[\"Flow Bytes/s\", \"Flow Packets/s\"]].apply(pd.to_numeric)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_cxgFGGYmmy"
      },
      "source": [
        "Replace the NaN values and infinity values with -1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz_9O00IYmmy"
      },
      "source": [
        "df.replace([np.inf, -np.inf, np.nan], -1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JMv7gKfi4nI"
      },
      "source": [
        "Convert string characters to numbers, use LabelEncoder, not OneHotEncoder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "XmYOMG0Ti8Wh",
        "outputId": "bf6ffbc1-8b29-482c-fd6e-0c6a46951be5"
      },
      "source": [
        "string_features = list(df.select_dtypes(include=['object']).columns)\n",
        "string_features.remove('Label')\n",
        "string_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Flow ID', 'Source IP', 'Destination IP', 'Timestamp']"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySZ2eW_2jCfQ"
      },
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "df[string_features] = df[string_features].apply(lambda col: le.fit_transform(col))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K94al8HAjJEN"
      },
      "source": [
        "### Undersampling against unbalance\n",
        "\n",
        "Dataset is unbalanced: total records = 170366, \"BENIGN\" records = 168186, records with attacks much less: 1507 + 652 + 21 = 2180."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "7K7iWbNMjWrZ",
        "outputId": "7ce9991a-9f78-4ee3-a190-ad8e6e376fc7"
      },
      "source": [
        "benign_total = len(df[df['Label'] == \"BENIGN\"])\n",
        "benign_total"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "168186"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "nVs_xEHFjY5q",
        "outputId": "f1c5e3e9-5dba-4061-d578-8089dc63c61f"
      },
      "source": [
        "attack_total = len(df[df['Label'] != \"BENIGN\"])\n",
        "attack_total"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2180"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "PFvPOea2jhMt",
        "outputId": "c6ca7662-2455-48de-f3aa-41191655a252"
      },
      "source": [
        "df.to_csv(\"web_attacks_unbalanced.csv\", index=False)\n",
        "df['Label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "BENIGN                        168186\n",
              "Web Attack – Brute Force        1507\n",
              "Web Attack – XSS                 652\n",
              "Web Attack – Sql Injection        21\n",
              "Name: Label, dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6A9-rS7OjyFl"
      },
      "source": [
        "We use **undersampling** to correct class imbalances: we remove most of the \"BENIGN\" records.\n",
        "\n",
        "Form a balanced dataset web_attacks_balanced.csv in proportion: 30% attack (2180 records), 70% benign data (2180 / 30 * 70 ~ = 5087 records).\n",
        "\n",
        "Algorithm to form a balanced df_balanced dataset:\n",
        "\n",
        "* All the records with the attacks are copied to the new dataset.\n",
        "* There are two conditions for copying \"BENIGN\" records to the new dataset:\n",
        " \n",
        "     1. The next record is copyied with the benign_inc_probability.\n",
        "     2. The total number of \"BENIGN\" records must not exceed the limit of 5087 records."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVZutNOZj_N9"
      },
      "source": [
        "Сalculate the probability of copying a \"BENIGN\" record. The enlargement multiplier is used to get exactly 70% benign data (5087 records)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "XaW7sEjHkB3K",
        "outputId": "27e0c165-6226-48a5-cab6-9d94ad80566e"
      },
      "source": [
        "enlargement = 1.1\n",
        "benign_included_max = attack_total / 30 * 70\n",
        "benign_inc_probability = (benign_included_max / benign_total) * enlargement\n",
        "print(benign_included_max, benign_inc_probability)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5086.666666666667 0.03326872232726466\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3ZXh6fTkFK_"
      },
      "source": [
        "Copy records from df to df_balanced, save dataset **web_attacks_balanced.csv**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MdiMPmdkICk"
      },
      "source": [
        "import random\n",
        "indexes = []\n",
        "benign_included_count = 0\n",
        "for index, row in df.iterrows():\n",
        "    if (row['Label'] != \"BENIGN\"):\n",
        "        indexes.append(index)\n",
        "    else:\n",
        "        # Copying with benign_inc_probability\n",
        "        if random.random() > benign_inc_probability: continue\n",
        "        # Have we achieved 70% (5087 records)?\n",
        "        if benign_included_count > benign_included_max: continue\n",
        "        benign_included_count += 1\n",
        "        indexes.append(index)\n",
        "df_balanced = df.loc[indexes]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "cqypBDIdkKN4",
        "outputId": "81585c22-c45e-4c60-b35a-c6272a1c263e"
      },
      "source": [
        "df_balanced['Label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "BENIGN                        5087\n",
              "Web Attack – Brute Force      1507\n",
              "Web Attack – XSS               652\n",
              "Web Attack – Sql Injection      21\n",
              "Name: Label, dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDPCkmutkNYD"
      },
      "source": [
        "If necessary, we can keep a single class of attack for experiments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_q52wjdkPZ8"
      },
      "source": [
        "# df_balanced = df_balanced[df_balanced['Label'] != \"Web Attack – Brute Force\"]\n",
        "# df_balanced = df_balanced[df_balanced['Label'] != \"Web Attack – Sql Injection\"]\n",
        "# df_balanced['Label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiYFzlGkkSXe"
      },
      "source": [
        "df_balanced.to_csv(\"web_attacks_balanced.csv\", index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-IEyQxBDt5sG"
      },
      "source": [
        "### Preparing data for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GY6ML0sYmm3"
      },
      "source": [
        "df = pd.read_csv('web_attacks_balanced.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3soJH0gOYmm3"
      },
      "source": [
        "The Label column is encoded as follows: \"BENIGN\" = 0, attack = 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bd4Sk24sYmm3"
      },
      "source": [
        "df['Label'] = df['Label'].apply(lambda x: 0 if x == 'BENIGN' else 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zZz-_spYmm3"
      },
      "source": [
        "If necessary, we will work with our own dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g16jjgSlYmm3"
      },
      "source": [
        "#!wget https://raw.githubusercontent.com/infosecdemos/ml-2020/master/ml-ids/datasets/mix_labelled.zip -O mix_labelled.zip\n",
        "#!unzip -u mix_labelled.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGgqWdxHYmm3"
      },
      "source": [
        "#df = pd.read_csv('mix_labelled.csv')\n",
        "#excluded = ['Flow Key', 'Flow Bytes/s', 'Flow MB/s', 'Fwd IAT Min', \n",
        "#            'Flow IAT Min', 'Packet Length Mean', 'Average Fwd Segment Size', 'Subflow Fwd Bytes',\n",
        "#            'Fwd Packets/s', 'Fwd IAT Total', 'Fwd IAT Max', 'Fwd IAT Mean',\n",
        "#            'Average Bwd Segment Size', 'Bwd Packet Length Mean', 'Packet Length List', 'Total Fwd Packets',\n",
        "#            'Packet Length Std', 'Packet Length Variance', 'Total Backword Packets', 'Fwd Packet Length Std',\n",
        "#            'Total Length of Bwd Packets', 'Bwd Header Length']\n",
        "#df = df.drop(columns=excluded, errors='ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1k2G5UAEt9cw"
      },
      "source": [
        "7 features (Flow ID, Source IP, Source Port, Destination IP, Destination Port, Protocol, Timestamp) are excluded from the dataset. The hypothesis is that the \"shape\" of the data being transmitted is more important than these attributes. In addition, ports and addresses can be substituted by an attacker, so it is better that the ML algorithm does not take these features into account in training [Kostas2018]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yza95R2t_1N"
      },
      "source": [
        "excluded = ['Flow ID', 'Source IP', 'Source Port', 'Destination IP', 'Destination Port', 'Protocol', 'Timestamp']\n",
        "df = df.drop(columns=excluded, errors='ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCxptOz2uDIV"
      },
      "source": [
        "Below at the stage of importance estimation the \"Init_Win_bytes_backward\" feature has the maximum value. After viewing the source dataset, it seems that an inaccuracy was made in forming the dataset. \n",
        "\n",
        "It turns out that it is possible to make a fairly accurate classification by one feature.\n",
        "\n",
        "Description of features: http://www.netflowmeter.ca/netflowmeter.html\n",
        "\n",
        "     Init_Win_bytes_backward - The total number of bytes sent in initial window in the backward direction\n",
        "     Init_Win_bytes_forward - The total number of bytes sent in initial window in the forward direction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lYhtGKqJuUQA",
        "outputId": "aade2290-801b-44a9-d1b8-880b3f40d121"
      },
      "source": [
        "if 'Init_Win_bytes_backward' in df.columns:\n",
        "    df['Init_Win_bytes_backward'].hist(figsize=(6,4), bins=10);\n",
        "    plt.title(\"Init_Win_bytes_backward\")\n",
        "    plt.xlabel(\"Value bins\")\n",
        "    plt.ylabel(\"Density\")\n",
        "    plt.savefig('Init_Win_bytes_backward.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "XPbBcbj_uYMV",
        "outputId": "88d26808-c28b-43d1-91d5-9bc2dc6a9d04"
      },
      "source": [
        "if 'Init_Win_bytes_forward' in df.columns:\n",
        "    df['Init_Win_bytes_forward'].hist(figsize=(6,4), bins=10);\n",
        "    plt.title(\"Init_Win_bytes_forward\")\n",
        "    plt.xlabel(\"Value bins\")\n",
        "    plt.ylabel(\"Density\")\n",
        "    plt.savefig('Init_Win_bytes_forward.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uw6liMMducM5"
      },
      "source": [
        "excluded2 = ['Init_Win_bytes_backward', 'Init_Win_bytes_forward']\n",
        "df = df.drop(columns=excluded2, errors='ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "a-FDOBmOufo6",
        "outputId": "8ba70aa2-818b-48d5-a74b-9a5ddf0b76f7"
      },
      "source": [
        "y = df['Label'].values\n",
        "X = df.drop(columns=['Label'])\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7267, 74) (7267,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3P69z9kfupES"
      },
      "source": [
        "## Feature importance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "4BbvfnjTuzeX",
        "outputId": "55719ff0-84a0-4568-ebda-df2f479bc71c"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "unique, counts = np.unique(y_train, return_counts=True)\n",
        "dict(zip(unique, counts))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: 3556, 1: 1530}"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wE4AwrX1u6Hj"
      },
      "source": [
        "### Visualization of the decision tree, importance evaluation using a single tree (DecisionTreeClassifier)\n",
        " \n",
        "In the beginning we use one tree - for the convenience of visualization of the classifier. High cross-validation scores even with 5 leaves look suspiciously good, we should look at the data carefully. Parameters for change - test_size in the cell above (train_test_split), max_leaf_nodes in the cell below.\n",
        " \n",
        "By changing the **random_state** parameter, we will get different trees and different features with the highest importance. But the forest will already average individual trees below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "JtVUvneuyAeI",
        "outputId": "582feeb2-a10f-4615-d3a7-74b734cddaf2"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "decision_tree = DecisionTreeClassifier(max_leaf_nodes=5, random_state=0)\n",
        "decision_tree = decision_tree.fit(X_train, y_train)\n",
        "cross_val_score(decision_tree, X_train, y_train, cv=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.94891945, 0.95284872, 0.956778  , 0.96070727, 0.93516699,\n",
              "       0.96070727, 0.96653543, 0.96062992, 0.96062992, 0.96456693])"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "cITpkfwyCqqU",
        "outputId": "d8120dd9-fc45-4746-b25d-cc488863c6e8"
      },
      "source": [
        "from sklearn.tree import export_text\n",
        "r = export_text(decision_tree, feature_names=X_train.columns.to_list())\n",
        "print(r)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "|--- Packet Length Mean <= 0.86\n",
            "|   |--- Fwd IAT Std <= 2358690.50\n",
            "|   |   |--- class: 0\n",
            "|   |--- Fwd IAT Std >  2358690.50\n",
            "|   |   |--- Fwd IAT Min <= 11045.50\n",
            "|   |   |   |--- class: 1\n",
            "|   |   |--- Fwd IAT Min >  11045.50\n",
            "|   |   |   |--- class: 0\n",
            "|--- Packet Length Mean >  0.86\n",
            "|   |--- Subflow Fwd Bytes <= 36189.50\n",
            "|   |   |--- class: 0\n",
            "|   |--- Subflow Fwd Bytes >  36189.50\n",
            "|   |   |--- class: 1\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "6xpZlcv_-J_W",
        "outputId": "7b256ccd-64c0-4ad4-a34b-14e7275cfeab"
      },
      "source": [
        "from graphviz import Source\n",
        "from sklearn import tree\n",
        "Source(tree.export_graphviz(decision_tree, out_file=None, feature_names=X.columns))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/svg+xml": [
              "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
              "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
              " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
              "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n",
              " -->\r\n",
              "<!-- Title: Tree Pages: 1 -->\r\n",
              "<svg width=\"582pt\" height=\"373pt\"\r\n",
              " viewBox=\"0.00 0.00 582.00 373.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
              "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 369)\">\r\n",
              "<title>Tree</title>\r\n",
              "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-369 578,-369 578,4 -4,4\"/>\r\n",
              "<!-- 0 -->\r\n",
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              "<polygon fill=\"none\" stroke=\"black\" points=\"400.5,-365 215.5,-365 215.5,-297 400.5,-297 400.5,-365\"/>\r\n",
              "<text text-anchor=\"middle\" x=\"308\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Packet Length Mean &lt;= 0.857</text>\r\n",
              "<text text-anchor=\"middle\" x=\"308\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.421</text>\r\n",
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              "<text text-anchor=\"middle\" x=\"206\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fwd IAT Std &lt;= 2358690.5</text>\r\n",
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              "<text text-anchor=\"middle\" x=\"218\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fwd IAT Min &lt;= 11045.5</text>\r\n",
              "<text text-anchor=\"middle\" x=\"218\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.028</text>\r\n",
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          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b99AtZuj-NR8"
      },
      "source": [
        "Analyze the confusion matrix. Which classes are confidently classified by the model?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "-7IrLSAx-PuK",
        "outputId": "e54c7989-2a1b-4054-e384-c42dd3c74d3f"
      },
      "source": [
        "unique, counts = np.unique(y_test, return_counts=True)\n",
        "dict(zip(unique, counts))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: 1531, 1: 650}"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "a9iK4zQt-SlP",
        "outputId": "256f6c4d-1123-4a97-dae1-b5a34ca0c29f"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_pred = decision_tree.predict(X_test)\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1529,    2],\n",
              "       [  75,  575]], dtype=int64)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suPDWnoYaEPD"
      },
      "source": [
        "### Importance evaluation using SelectFromModel (still one decision tree)\n",
        "\n",
        "Source: https://github.com/oreilly-mlsec/book-resources/blob/master/chapter2/select-from-model-nslkdd.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "hm7PM7f_aRei",
        "outputId": "c6023813-4233-4b05-d889-98a617b21b55"
      },
      "source": [
        "from sklearn.feature_selection import SelectFromModel\n",
        "sfm = SelectFromModel(estimator=decision_tree).fit(X_train, y_train)\n",
        "sfm.estimator_.feature_importances_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "       0.        , 0.18953957, 0.        , 0.0197273 , 0.        ,\n",
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ])"
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          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "Y0Sx8yJ_aW33",
        "outputId": "3a4f5abf-92b5-429c-e3c5-a28dbe0be19e"
      },
      "source": [
        "sfm.threshold_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.013513513513513514"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "4iNlo5jIaaaL",
        "outputId": "d3683bfc-289b-414f-acec-eb6a60c1b09e"
      },
      "source": [
        "X_train_new = sfm.transform(X_train)\n",
        "print(\"Original num features: {}, selected num features: {}\"\n",
        "      .format(X_train.shape[1], X_train_new.shape[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original num features: 74, selected num features: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "aBQxtLAwaegL",
        "outputId": "2c75fc3c-35e2-4532-fe8f-69fe909869df"
      },
      "source": [
        "indices = np.argsort(decision_tree.feature_importances_)[::-1]\n",
        "for idx, i in enumerate(indices[:10]):\n",
        "    print(\"{}.\\t{} - {}\".format(idx, X_train.columns[i], decision_tree.feature_importances_[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.\tPacket Length Mean - 0.7301243666452162\n",
            "1.\tFwd IAT Std - 0.18953956737517003\n",
            "2.\tSubflow Fwd Bytes - 0.060608769122583714\n",
            "3.\tFwd IAT Min - 0.019727296857030143\n",
            "4.\tIdle Min - 0.0\n",
            "5.\tBwd IAT Std - 0.0\n",
            "6.\tFwd IAT Mean - 0.0\n",
            "7.\tFwd IAT Max - 0.0\n",
            "8.\tBwd IAT Total - 0.0\n",
            "9.\tBwd IAT Mean - 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "duGKUsAzal52"
      },
      "source": [
        "### Evaluation of importance using RandomForestClassifier.feature_importances_ (move from one tree to a random forest, classification quality increases)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "xo4E_wFeaqNd",
        "outputId": "1f00d02b-5dc1-47eb-8c5f-47a76b077109"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=250, random_state=42, oob_score=True)\n",
        "rf.fit(X_train, y_train)\n",
        "# Score = mean accuracy on the given test data and labels\n",
        "print('R^2 Training Score: {:.2f} \\nR^2 Validation Score: {:.2f} \\nOut-of-bag Score: {:.2f}'\n",
        "      .format(rf.score(X_train, y_train), rf.score(X_test, y_test), rf.oob_score_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R^2 Training Score: 0.99 \n",
            "R^2 Validation Score: 0.99 \n",
            "Out-of-bag Score: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "byV6krktawE_",
        "outputId": "b43f9da0-593c-4ec1-f993-d61d78a65706"
      },
      "source": [
        "features = X.columns\n",
        "importances = rf.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "webattack_features = []\n",
        "\n",
        "for index, i in enumerate(indices[:20]):\n",
        "    webattack_features.append(features[i])\n",
        "    print('{}.\\t#{}\\t{:.3f}\\t{}'.format(index + 1, i, importances[i], features[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.\t#51\t0.081\tAverage Packet Size\n",
            "2.\t#13\t0.076\tFlow Bytes/s\n",
            "3.\t#38\t0.064\tMax Packet Length\n",
            "4.\t#39\t0.057\tPacket Length Mean\n",
            "5.\t#7\t0.055\tFwd Packet Length Mean\n",
            "6.\t#61\t0.055\tSubflow Fwd Bytes\n",
            "7.\t#23\t0.054\tFwd IAT Min\n",
            "8.\t#52\t0.052\tAvg Fwd Segment Size\n",
            "9.\t#3\t0.039\tTotal Length of Fwd Packets\n",
            "10.\t#15\t0.036\tFlow IAT Mean\n",
            "11.\t#5\t0.032\tFwd Packet Length Max\n",
            "12.\t#21\t0.029\tFwd IAT Std\n",
            "13.\t#33\t0.027\tFwd Header Length\n",
            "14.\t#0\t0.027\tFlow Duration\n",
            "15.\t#14\t0.023\tFlow Packets/s\n",
            "16.\t#22\t0.023\tFwd IAT Max\n",
            "17.\t#35\t0.021\tFwd Packets/s\n",
            "18.\t#16\t0.019\tFlow IAT Std\n",
            "19.\t#19\t0.018\tFwd IAT Total\n",
            "20.\t#20\t0.017\tFwd IAT Mean\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RXQUuqHa11b"
      },
      "source": [
        "For comparison, the results of the study [Sharafaldin2018] (compare relatively, without taking into account the multiplier):\n",
        " \n",
        "* Init Win F.Bytes 0.0200\n",
        "* Subflow F.Bytes 0.0145\n",
        "* Init Win B.Bytes 0.0129\n",
        "* Total Len F.Packets 0.0096\n",
        "\n",
        "And incorrect results [Kostas2018] (error was found in assessing the importance of features, line: *impor_bars = pd.DataFrame({'Features':refclasscol[0:20],'importance':importances[0:20]})*, the importances[0:20] sample does not take into account that the values are not sorted in descending order):\n",
        " \n",
        "* Flow Bytes/s 0.313402\n",
        "* Total Length of Fwd Packets 0.304917\n",
        "* Flow Duration 0.000485\n",
        "* Fwd Packet Length Max 0.00013"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "TPxzWeP4bHa5",
        "outputId": "5815f66e-3b0e-4c06-a7d6-6c0b1a49b59d"
      },
      "source": [
        "indices = np.argsort(importances)[-20:]\n",
        "plt.rcParams['figure.figsize'] = (10, 6)\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), importances[indices], color='#cccccc', align='center')\n",
        "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.grid()\n",
        "plt.savefig('feature_importances.png', dpi=300, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "QaYCrMC4cenU",
        "outputId": "da62ba7e-d7af-41e8-d9a9-0fab415eba7e"
      },
      "source": [
        "y_pred = rf.predict(X_test)\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1522,    9],\n",
              "       [  18,  632]], dtype=int64)"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktl5y47rcsiy"
      },
      "source": [
        "Next, for experiments, we keep the first max_features of features with maximum importance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "Xriaa8XicuoB",
        "outputId": "8a171904-2d66-4d5f-b0e3-3f549f383cdc"
      },
      "source": [
        "max_features = 20\n",
        "webattack_features = webattack_features[:max_features]\n",
        "webattack_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Average Packet Size',\n",
              " 'Flow Bytes/s',\n",
              " 'Max Packet Length',\n",
              " 'Packet Length Mean',\n",
              " 'Fwd Packet Length Mean',\n",
              " 'Subflow Fwd Bytes',\n",
              " 'Fwd IAT Min',\n",
              " 'Avg Fwd Segment Size',\n",
              " 'Total Length of Fwd Packets',\n",
              " 'Flow IAT Mean',\n",
              " 'Fwd Packet Length Max',\n",
              " 'Fwd IAT Std',\n",
              " 'Fwd Header Length',\n",
              " 'Flow Duration',\n",
              " 'Flow Packets/s',\n",
              " 'Fwd IAT Max',\n",
              " 'Fwd Packets/s',\n",
              " 'Flow IAT Std',\n",
              " 'Fwd IAT Total',\n",
              " 'Fwd IAT Mean']"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLikdx9Legm0"
      },
      "source": [
        "## Analysis of selected features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "l4AlTAUme5aJ",
        "outputId": "30c0dea5-f39d-4bf5-dc91-fb1619023a2c"
      },
      "source": [
        "df[webattack_features].hist(figsize=(20,12), bins=10);\n",
        "plt.savefig('features_hist.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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kq+yXStoQOIZ0NW+u0m1+Pye1ytd6KlwE3JO7Wzbquv084Eek+5bvIbWCN3rs+l6kCuYlWv1kpdqJ3lC/D7NOW9sc9HnSLWJ/Ih1zl5BzRhOuJTU6/6KfaUiVo5+SBpm+idQY3p/HScff9ZKeIB2Pt5NyCxHxI1IOm5Vzxu2kXlm1XovvBr5KaojfkTRIZbNl6zc/lXAO6ZaX5ZJ+3GD5clKuuk3SSlKPzh/l2OsdQmqkWlLIX5/KFxbeRn7CHKmHw7dJ/x+Y2cAOIfUAX0I69o6PiDmF5dcC65EqYbXp+tzWyLNPcSSdk30m8tNqASLi18DfSbcnLyx87mpSA9T9kmr5eqActEOeXkm6LfkbkZ76dACrK42QzvdOJOWH+0kPcvnUIGUws/Yaznz0OKnB52ukp33vW7hI9R3SudlC4Geki/D9yr2lTiblnAdIY1X+urBKozxW/PxVwH/lOJaS7nY5eJAyWBsp1dfNEkknAc+PiOmDrmxmI56kj5IGhN5z0JW7iNJT0xYDh0bENZ2Ox8wMQNLVpPHSvj0M274c+HpEXD7oymZmZgXusTTC5VtHdlayO6n75I86HZeZVZOkcZJeJ2kdpUdRH02P5AxJ+0gak7tYf4p03/9zbi8zM+uEfKvqbgzSM2At9AFuSDczsyEbNfgq1uNGk255GU96pOzJpHtozcwaWR/4FrAd6ZasWcA3OhpR67yWdA//+qRbhw+MiL90NiQzM5A0kzRG3ifqngDVMhHR6HZaMzOzQflWODMzMzMzMzMza4pvhTMzMzMzMzMzs6ZU+la4rbbaKiZPnlxq3SeeeIKNN954eANqA5ejWlyO1ebNm/dwRGzdopC6Xjflp07v3zFUY/+9HIPz05q6KT+1istRLS7Has5Pz1U2R1Xld1SVOKA6sVQlDqhOLFWJA8rH0tL8FBGVfb3qVa+Ksq655prS61aZy1EtLsdqwI1RgbxQlVc35adO798xVGP/vRyD81P35qdWcTmqxeVYzfmp+RxVld9RVeKIqE4sVYkjojqxVCWOiPKxtDI/+VY4MzMzMzMzMzNrihuWzMzMzMzMzMysKW5YMjMzMzMzMzOzplR68O6huO2+Fcw49ict297CEw9o2bbMbGRzfjKzqmp1fgLnKDNrDecns+7hHktmZmZmZmZmZtYUNyyZmZmZmZmZmVlT3LBkZmZmZmZmZmZNKdWwJGmMpEsk/V7SfEmvlbSFpDmS7s7/bp7XlaTTJC2QdKuk3QrbmZ7Xv1vS9OEqlJmZmZmZmTXm+p2ZtVLZHkunAldGxEuBXYD5wLHAVRGxA3BVngbYD9ghv44AvgkgaQvgeGAPYHfg+FqyMjMzMzMzs7Zx/c7MWmbQhiVJmwJvBM4BiIinI2I5MA2YmVebCRyY308Dzo9kLjBG0jhgH2BORCyLiEeBOcC+LS2NmZmZmZmZ9cv1OzNrtTI9ll4IPAT8r6SbJX1b0sbA2IhYCpD/3SavPwFYVPj84jyvv/lmZmZmZmbWHq7fmVlLjSq5zm7AxyPiekmnsrpbZCNqMC8GmL/mh6UjSF0sGTt2LH19fSVChLEbwdE7rSq1bhll99tqK1eu7Ni+W8nlqJZeKYeZmZmZrbW21u+guTpeq+t30Fwdr0rn0VWJpSpxQHViqUoc0JlYyjQsLQYWR8T1efoSUuJ5QNK4iFiau0I+WFh/UuHzE4Elef7Uuvl99TuLiLOAswCmTJkSU6dOrV+lodMvuJSTbytTnHIWHlpuv63W19dH2TJXmctRLb1SDjMzMzNba22t30FzdbxW1++guTpelc6jqxJLVeKA6sRSlTigM7EMeitcRNwPLJL0kjxrL+BOYDZQG/l/OnBpfj8bOCw/PeA1wIrclfKnwN6SNs+Duu2d55mZmZmZmVkbuH5nZq1Wtgn448AFktYH7gHeT2qUuljS4cC9wLvzupcD+wMLgCfzukTEMklfAG7I630+Ipa1pBRmZmZmZmZWlut3ZtYypRqWIuIWYEqDRXs1WDeAj/WznXOBc4cSoJmZmZmZmbWO63dm1kplngpnZmZmZmZmZmb2HG5YMrOuJmmMpEsk/V7SfEmvlbSFpDmS7s7/bp7XlaTTJC2QdKuk3QrbmZ7Xv1vS9P73aGZmZmZmZjVuWDKzbncqcGVEvBTYBZhPerLJVRGxA3AVqx+hux+wQ34dAXwTQNIWwPHAHsDuwPG1xigzs2a54dvMzMxGAjcsmVnXkrQp8EbgHICIeDoilgPTgJl5tZnAgfn9NOD8SOYCY/LjdPcB5kTEsoh4FJgD7NvGophZb3LDt5mZmfW8sk+FMzOrohcCDwH/K2kXYB7wCWBsfgwuEbFU0jZ5/QnAosLnF+d5/c1fg6QjSBU+xo4dS19fX6kgx24ER++0qnypBlF2vzUrV64c8mdazTF0fv+Oob0KDd8zIDV8A09LmgZMzavNBPqAYyg0fANzc2+ncXndObUnLUmqNXxf1K6ymJmZmQ3EDUtm1s1GAbsBH4+I6yWdyuqr/42owbwYYP6aMyLOAs4CmDJlSkydOrVUkKdfcCkn39a6dLvw0HL7renr66NsrMPFMXR+/46h7UZkwzcMvfG7FXqlwdLlqJZeKYeZ2XBzw5KZdbPFwOKIuD5PX0JqWHpA0rhcaRsHPFhYf1Lh8xOBJXn+1Lr5fcMYt5n1vhHZ8A1Db/xuhV5psHQ5qqVXymFmNtw8xpKZda2IuB9YJOkledZewJ3AbKA2wO104NL8fjZwWB4k9zXAitxz4KfA3pI2z2OX7J3nmZk1q1HD927khm+AITR8N5pvZmZmVgluWDKzbvdx4AJJtwK7Al8GTgT+UdLdwD/maYDLgXuABcDZwL8A5LFLvgDckF+fr41nYmbWDDd8m5mZ2UjhW+HMrKtFxC3AlAaL9mqwbgAf62c75wLntjY6Mxvhag3f65Matd9Puqh3saTDgXuBd+d1Lwf2JzV8P5nXJSKWSao1fIMbvs3MzKxi3LBkZmZmNgzc8G1mZmYjgW+FMzMzMzMzMzOzprhhyczMzMzMzMzMmuKGJTMzMzMzMzMza4oblszMzMzMzMzMrCluWDIzMzMzMzMzs6a4YcnMzMzMzMzMzJrihiUzMzMzMzMzM2uKG5bMzMzMzMzMzKwpblgyMzMzMzMzM7OmuGHJzMzMzMzMzMya4oYlMzMzMzMzMzNrSumGJUnrSrpZ0mV5ejtJ10u6W9L3JK2f52+Qpxfk5ZML2zguz79L0j6tLoyZmZmZmZkNzvU7M2uVofRY+gQwvzB9EnBKROwAPAocnucfDjwaEdsDp+T1kLQjcDDwcmBf4BuS1l278M3MzMzMzKwJrt+ZWUuUaliSNBE4APh2nhbwZuCSvMpM4MD8flqeJi/fK68/DZgVEX+NiD8BC4DdW1EIMzMzMzMzK8f1OzNrpbI9lr4G/Cfw9zy9JbA8Ilbl6cXAhPx+ArAIIC9fkdd/dn6Dz5iZmZmZmVl7uH5nZi0zarAVJL0VeDAi5kmaWpvdYNUYZNlAnynu7wjgCICxY8fS19c3WIgAjN0Ijt5p1eArllR2v622cuXKju27lVyOaumVcpiZmZnZ2ml3/S7vc8h1vFbX76C5Ol6VzqOrEktV4oDqxFKVOKAzsQzasAS8Dni7pP2BDYFNSS3cYySNyq3WE4Elef3FwCRgsaRRwGbAssL8muJnnhURZwFnAUyZMiWmTp1aqiCnX3ApJ99WpjjlLDy03H5bra+vj7JlrjKXo1p6pRxmZmZmttbaWr+D5up4ra7fQXN1vCqdR1cllqrEAdWJpSpxQGdiGfRWuIg4LiImRsRk0uBsV0fEocA1wLvyatOBS/P72XmavPzqiIg8/+D8VIHtgB2A37asJGZmZmZmZjYg1+/MrNWG8lS4escAn5S0gHSP7Tl5/jnAlnn+J4FjASLiDuBi4E7gSuBjEfHMWuzfzAzw43LNrLqcn8ysi7h+Z2ZNGVLfwojoA/ry+3toMOp/RDwFvLufz38J+NJQgzQzG0Ttcbmb5una43JnSTqT9Jjcb1J4XK6kg/N676l7XO544OeSXuyTIzNrAecnM6ss1+/MrBXWpseSmVnH+XG5ZlZVzk9mZmY2ErR2NDQzs/arPS53dJ4u/bhcScXH5c4tbLPh43K79amVVXhKhWPo/P4dQ0e0LT+ZmZmZdYoblsysa7X7cbnd+tTKKjylwjF0fv+Oob3anZ+q0vANzT3Oe231SoOly1EtvVIOM7Ph5oYlM+tmbX9crplZSW3NT1Vp+IbmHue9tnqlwdLlqJZeKYeZ2XDzGEtm1rX8uFwzqyrnJzMzMxsp3GPJzHrRMcAsSV8EbmbNx+V+Jz8udxmpskdE3CGp9rjcVfhxuWY2fJyfzMzMrKe4YcnMeoIfl2tmVeX8ZGZmZr3Mt8KZmZmZmZmZmVlT3LBkZmZmZmZmZmZNccOSmZmZmZmZmZk1xQ1LZmZmZmZmZmbWFDcsmZmZmZmZmZlZU9ywZGZmZmZmZmZmTXHDkpmZmZmZmZmZNcUNS2ZmZmZmZmZm1hQ3LJmZmZmZmZmZWVPcsGRmZmZmZmZmZk1xw5KZmZmZmZmZmTXFDUtmZmZmZmZmZtYUNyyZmZmZmZmZmVlT3LBkZmZmZmZmZmZNccOSmZmZmZmZmZk1ZdCGJUmTJF0jab6kOyR9Is/fQtIcSXfnfzfP8yXpNEkLJN0qabfCtqbn9e+WNH34imVmZmZmZmb1XL8zs1Yr02NpFXB0RLwMeA3wMUk7AscCV0XEDsBVeRpgP2CH/DoC+CakRAUcD+wB7A4cX0tWZmZmZmZm1hau35lZSw3asBQRSyPipvz+cWA+MAGYBszMq80EDszvpwHnRzIXGCNpHLAPMCcilkXEo8AcYN+WlsbMzMzMzMz65fqdmbXakMZYkjQZeCVwPTA2IpZCSk7ANnm1CcCiwscW53n9zTczMzMzM7M2c/3OzFphVNkVJW0C/AD414h4TFK/qzaYFwPMr9/PEaQulowdO5a+vr5S8Y3dCI7eaVWpdcsou99WW7lyZcf23UouR7X0SjnqSZoEnA88H/g7cFZEnJq7Zn8PmAwsBA6KiEeVEtepwP7Ak8CM2hW7PC7AZ/KmvxgRMzEza5Lzk5lVXbvqd3lfQ67jtbp+B83V8ap0Hl2VWKoSB1QnlqrEAZ2JpVTDkqT1SEnngoj4YZ79gKRxEbE0d4V8MM9fDEwqfHwisCTPn1o3v69+XxFxFnAWwJQpU2Lq1Kn1qzR0+gWXcvJtpdvJBrXw0HL7bbW+vj7KlrnKXI5q6ZVyNFAbI+AmSaOBeZLmADNIYwScKOlY0hgBx7DmGAF7kMYI2KMwRsAU0gnRPEmzc7duM7NmOD+ZWWW1s34HzdXxWl2/g+bqeFU6j65KLFWJA6oTS1XigM7EUuapcALOAeZHxP8UFs0GaiP/TwcuLcw/LD894DXAityV8qfA3pI2z4O67Z3nmZk1xWMEmFlVOT+ZWVW5fmdmrVamCfh1wD8Dt0m6Jc/7FHAicLGkw4F7gXfnZZeTunEvIHXlfj9ARCyT9AXghrze5yNiWUtKYWYj3kBjBEhqyRgB3XqrbhW65jqGzu/fMXTOSMpP0JnhBHrld+VyVEuvlKMB1+/MrKUGbViKiF/R+P5ZgL0arB/Ax/rZ1rnAuUMJ0MxsMO0aI6Bbb9WtQtdcx9D5/TuGzhhp+Qk6M5xAr/yuXI5q6ZVy1HP9zsxabUhPhTMzq5qBxgjIy8uOEdBovplZ05yfzMzMbCRww5KZdS2PEWBmVeX8ZGZmZiNFa/s+m5m1l8cIMLOqcn4yMzOzEcENS2bWtTxGgJlVlfOTmZmZjRS+Fc7MzMzMzMzMzJrihiUzMzMzMzMzM2uKG5bMzMzMzMzMzKwpblgyMzMzMzMzM7OmuGHJzMzMzMzMzMya4oYlMzMzMzMzMzNrihuWzMzMzMzMzMysKW5YMjMzMzMzMzOzprhhyczMzMzMzMzMmuKGJTMzMzMzMzMza4oblszMzMzMzMzMrCluWDIzMzMzMzMzs6a4YcnMzMzMzMzMzJoyqtMBVNXkY3/S8m0uPPGAlm/TzMzMzMzMzKxT3GPJzMzMzMzMzMya4oYlMzMzMzMzMzPLe/19AAAgAElEQVRrihuWzMzMzMzMzMysKR5jyczMzMzMzMyszYZjbOfz9t245dscTNt7LEnaV9JdkhZIOrbd+zcz64/zk5lVlfOTmVWV85OZtbXHkqR1gTOAfwQWAzdImh0Rd7YzDjOzes5PZlZVzk9mVlXOTyPTbfetYEaLe9r4Cerdrd23wu0OLIiIewAkzQKmASMi8ZTp5nb0TqtafpAOhQ9oG8FGdH4ys0pzfjKzqhrR+Wk4bmNyfcy6UbsbliYAiwrTi4E92hyDDaBVyXG4G8iccG0YdE1+GupxWuZ49DFlVmldk5/MbMTpqvzUTF2n3Rf+B4qx050QVsfR+m02Ww+tzt+kGnF0SrsbltRgXqyxgnQEcESeXCnprpLb3gp4eC1iq4SjXI5SdNJwbfk5euL7oDXl2LYVgVRYz+anMsdjG46pKhxLnY6h0/vv5RicnyqUn9r4f3RRFX7breByVIvPnwY3aH6CpnNUJX5HVaqfVSWWqsQB1YmlKnEAvOmk0rG0LD+1u2FpMTCpMD0RWFJcISLOAs4a6oYl3RgRU9YuvM5zOarF5RhRejY/dXr/jqEa+3cMXa1n81OruBzV4nKMKIPmJ2guR1Xl71+VOKA6sVQlDqhOLFWJAzoTS7ufCncDsIOk7SStDxwMzG5zDGZmjTg/mVlVOT+ZWVU5P5lZe3ssRcQqSUcCPwXWBc6NiDvaGYOZWSPOT2ZWVc5PZlZVzk9mBu2/FY6IuBy4fBg2PeTu3xXlclSLyzGC9HB+6vT+wTFUYf/gGLpWD+enVnE5qsXlGEFGQH6qShxQnViqEgdUJ5aqxAEdiEURzxlbzczMzMzMzMzMbFDtHmPJzMzMzMzMzMx6RNc3LEnaV9JdkhZIOrbT8QxG0kJJt0m6RdKNed4WkuZIujv/u3meL0mn5bLdKmm3DsZ9rqQHJd1emDfkuCVNz+vfLWl6RcpxgqT78ndyi6T9C8uOy+W4S9I+hfkd/d1JmiTpGknzJd0h6RN5ftd9J72snb+TTuSWTueFKhzPnT4WB9h/2/4OkjaU9FtJv8sxfC7P307S9bk831Ma1BVJG+TpBXn55MFis7U32Pc70PdSJSXKMUPSQ4Xf/gc7EedAGuWuuuUtydHDrUQ5pkpaUfguPtvuGMvoL4/WrdMV30m3qkp+KhHHJyXdmX8DV0lq2aPahxpLYb13SQpJw/IEsDJxSDoo/13ukHThcMRRJhZJL8jH8s35O9q/0XZaEEclcniJOA7N+79V0nWSdhmOOJ4VEV37Ig0Q90fghcD6wO+AHTsd1yAxLwS2qpv3VeDY/P5Y4KT8fn/gCkDAa4DrOxj3G4HdgNubjRvYArgn/7t5fr95BcpxAvDvDdbdMf+mNgC2y7+1davwuwPGAbvl96OBP+R4u+476dVXu38nncgtnc4LVTieO30sDrD/tv0dclk2ye/XA67PZbsYODjPPxP4aH7/L8CZ+f3BwPcGiq2dx22vvsp8v/19L1V6lSzHDODrnY51kHI8J3fVLa/M+d9almMqcFmn4yxRjoZ5tBu/k258VSU/lYzjTcDz8vuPDleeLPt/cv69/gKYC0zp0N9kB+Bm8jkLsE0HfydnsfpcY0dg4TDFUokcXiKOfyh8L/sNd97q9h5LuwMLIuKeiHgamAVM63BMzZgGzMzvZwIHFuafH8lcYIykcZ0IMCJ+ASyrmz3UuPcB5kTEsoh4FJgD7Dv80a/WTzn6Mw2YFRF/jYg/AQtIv7mO/+4iYmlE3JTfPw7MBybQhd9JD+v474Rhzi2dzgtVOJ47fSwOsP+2/R1yWVbmyfXyK4A3A5f08zeo/W0uAfaSpAFis7VX5vvt73upkirk1bVWIndV5vxvIEPMwZVVMo92xXfSpaqSnwaNIyKuiYgn8+RcYGKLYygdS/YF0oWspzoYx4eAM/K5CxHxYAdjCWDT/H4zYMlwBFKVHD5YHBFxXe17YXh/r0D33wo3AVhUmF7MwCfUVRDAzyTNk3REnjc2IpZC+s8N2CbPr3r5hhp3lctzZO4meK7yLSt0STlyd+BXknoJ9NJ30u3a/betSm6pwm+wI8dzp4/Fuv1DG/8OktaVdAvwIKlR7I/A8ohY1WB7z+4rL18BbLm2MdiAyvxt+/teqqTsb+Sf8m//EkmT2hNaS/XSsfBapdtkr5D08k4HM5gGebSml76TqqlKfhrqd3w4qVfKcBg0FkmvBCZFxGXDFEOpOIAXAy+W9GtJcyUN1wXqMrGcALxP0mLSUwo/PkyxDKaK+WI4f69A9zcsNWqprvpj7l4XEbuRuqN9TNIbB1i3G8sH/cdd1fJ8E3gRsCuwFDg5z698OSRtAvwA+NeIeGygVRvMq1RZelC7/7ZVzy3t+g125Hju9LHYYP9t/TtExDMRsSvpatjuwMsG2J7zUfuV+dt2w9+/TIz/B0yOiJ2Bn7O6l0M36YbvooybgG0jYhfgdODHHY5nQIPk8V75TqqoKvmp9D4kvQ+YAvy/FsdQKhZJ6wCnAEcP0/5LxZGNIt0ONxU4BPi2pDEdiuUQ4LyImEi6He07+W/VbpXKF5LeRGpYOmY499PtDUuLgeKVqIkMU5e3VomIJfnfB4EfkU7AH6h1j8v/1roQVr18Q427kuWJiAdypejvwNmsvvWi0uWQtB7pBOiCiPhhnt0T30mPaOvftkK5paO/wU4cz50+Fhvtv1N5LSKWA32kMQXGSBrVYHvP7isv34zUldv5aPiU+dv2971UyaDliIhHIuKvefJs4FVtiq2VeuJYiIjHarfJRsTlwHqStupwWA31k8eLeuI7qaiq5KdS37GktwCfBt5eyDWtNlgso4FXAH2SFpL+z509DAN4l/1uLo2Iv+Xb2O8iNTS1WplYDieN70hE/AbYEOhEzqlMvpC0M/BtYFpEPDKc++r2hqUbgB2UnjyzPmkwt9kdjqlfkjaWNLr2HtgbuJ0Uc+0JQNOBS/P72cBheWT51wArardWVMRQ4/4psLekzfNtGXvneR1Vd8/rO0jfCaRyHKz0JIrtSEnyt1Tgd5fvKz8HmB8R/1NY1BPfSY9o2++kYrmlo7/Bdh/PnT4W+9t/O/8OkrauXZ2UtBHwFtIYJdcA7+rnb1D727wLuDoiYoDYbO2V+X77+16qZNBy1P323076LXabqp//lSLp+TlHIWl3Ur1jWCs2zRggjxf1xHdSUVXJT2XyyyuBb5EalYZrLKFBY4mIFRGxVURMjojJpPFz3h4RN7YzjuzHpEHNyQ3HLyY9gKTVysRyL7BXjuVlpIalh4YhlsFUIl9IegHwQ+CfI+IPw77DGMaRwdvxInVz+wNpPIdPdzqeQWJ9IWkE+98Bd9TiJd0jfBVwd/53izxfwBm5bLcxDKP9DyH2i0i3U/yN1Ap7eDNxAx8gDci6AHh/RcrxnRznraREMK6w/qdzOe4C9qvK7w54PalL5a3ALfm1fzd+J738atfvpFO5pdN5oQrHc6ePxQH237a/A7Az6Ykwt5IasD5b+F3+Npfn+8AGef6GeXpBXv7CwWLza+1fjb5f4POkysiA30uVXiXK8RVSHvwdqXHzpZ2OuUEZGuWujwAfycsrc/63luU4svBdzAX+odMx91OO/vJo130n3fqqSn4qEcfPgQcKv5PZnfqb1K3bN1y/yRJ/EwH/A9yZj42DO/g72RH4dc45twB7D1MclcjhJeL4NvBo4fd643B9NxGB8k7NzMzMzMzMzMyGpNtvhbMGJE2WFFo9toWZWaWN9LwlaarSU0zMrAO6NQdJOk/SFzsdh5kNr5yftu90HNC7eadKf+Nu5IalLiZpoaS/SFpZeI1v0777JH2wbt52kv4u6RuFeS+oiy8kPVGYfkM/2w5Ju9TN/3GeP3XYCmZmw6qH89bLJf1M0qOSlkuaJ2n/vMyNRmYVUYEc9FTe58OSflg3JlTbNJOXJL1X0oXDFZNZr2h3nunn/KZrzj060aDT6G9ma8cNS93vbRGxSeHVySdUHEa6j/NgSRsARMS9xfjyersU5v2yn239IW8PAElbkp540IkB2MystXoxb/0fMAcYC2wDHAXUP67azKqhkznoyJxXXgyMIT0yvFvsD1ze6SDMukSVznUqodt6hNrQuGFpBJA0XtJsScskLZD0oTx/w9yavlWe/oykVZI2zdNflPS1IezqMOAzpAHE3raWYV8AvEfSunn6ENIj1J8ulGsdScdK+qOkRyRdLGmLwvLvS7pf0gpJv5D08sKy8ySdIeknkh6XdL2kF61lzGbWIt2Ut3Is2wFnR8TT+fXriPiV0lP6rgDGF69aStoo56FHJd0JvLqZfZvZ8BjuHBQRy0iPt39F/twBkm6W9JikRZJOqIvn9ZKuyz0iF0ma0SDm0ZKukXSakg0k/bekeyU9IOnMnHv6y0u7S7oxx/CApOKTJtcB/hG4Mv8NvpvPvZZLukHS2Ob+0mYjh6SZko7O7yfknjr/kqe3z/mm9iTF/5C0VNISSR9owb43k3RO3uZ9OVetm5e9SNLV+Zh+WNIFyk97zctfKemmXGf6Hmkw9eK23yrplpwPrlN6xH1t2UJJx0i6FXhCQ2xckvQBSfPz+dJPJW1bWBaSPiLp7rz8jMLfb11JJ+fy/EnSkXn9UZK+BLwB+HrOf18v7PItjbZng3PD0shwEWmk+PGkR3R+WdJeEfEU6dGNe+b13gj8GXhdYfraMjtQujVkIjALuJhCb6MmLSE9XWDvPH0YcH7dOkcBB5LiH0/qdXBGYfkVpMdVbwPcRGqsKjoE+BywOekpE19ay5jNrHW6KW89Qsoh35V0YLGCFRFPAPsBS+quWh4PvCi/9mH1Y5TNrBqGNQflhql/Ij1REeAJUg4aAxwAfFTSgXndF5DOaU4HtgZ2JT3hp7i92pMofx0RR0V6Os9JpJ5RuwLbAxNIT23sLy+dCpwaEZuSctPFhV3sDtwTEQ+T8tVmwCTSEzA/AvxlsDJ3G0nnSnpQ0u0l1t1W0lWSblW6xWZiO2K0rnMtMDW/3xO4hzVzyS8jIiTtC/w7qTF3B+AtLdj3TGAVKRe8klTHqt0KJtITNccDLyMd2ycASFof+DHpabNbkJ7O90+1jUraDTgX+DApH3wLmK3cCzw7hJTXxkTEqrIB5xz4KeCdpNz3S1JuLnor6eLcLsBBpHMqgA+R8tyuwG6kOiMAEfHpvK0jc/47ssT2KmeIOeoF+cLDzTlP7d/qeNyw1P1+nFuHl0v6cf1CSZNIj1E9JiKeiohbSI8e/Oe8yrXAnrn1eGfgtDy9Iemg6u9WtXrTgSsi4lHgQmA/SdusVclSQ9Jhkl5CSkS/qVv+YdKjJhdHxF9JCfBdtZbwiDg3Ih4vLNtF0maFz/8wIn6bE9wFpMRjZsOvp/JWrsC9CVgInAwsVeolucMAHzsI+FJELIuIRbkMZtYencxBp0laTnoc9lLgkwAR0RcRt0XE3yPiVlLlqVbhPBT4eURcFBF/i4hHckw143NM34+Iz+QyiFSx+recZx4HvgwcPEBsfwO2l7RVRKyMiLmFZQew+ja4v5EqkNtHxDMRMS8ievHW3/OAfUuu+9/A+RGxM+nx518ZrqCsazTKM9cCb1DqAfhG4KusbpTek9WN0gcB/xsRt+eG4BNK7O+0wv6WA5fVFuQLXvsB/xoRT0TEg6TbcA8GiIgFETEnIv4aEQ8B/8Pq/PMaYD3gazn/XEJqXK/5EPCtiLg+54OZwF/z556NLSIWRcRQG6A/DHwlIubn+tqXgV2LvZaAEyNieUTcC1zD6vrcQaSG8sX5PO/Ekvvsb3tVdB7lc9RngIsj4pWk7/0bg6w/ZG5Y6n4HRsSY/DqwwfLxQO2EoubPpKtWsLrlfDfgNtIYIXuSksGCfGVqQJI2At5N7hGUG4DuBd7bVIlW+yHwZuDjpFbyetsCPyok0PnAM8DY3P3xRKXb5B4jVfgAtip8/v7C+yeBTTCzdui5vJVPXI6MiBeRctMTPLeXZdF4YFFh+s/N7NfMmtLJHHRU3u+EiDg0V+KQtEe+mvyQpBWkXkC1c5ZJwB8H2OYBwEbAmYV5WwPPA+YVzpOuzPP7cziph9PvlW5ve2thWXF8pe8APwVmKd2m81VJ6w2w3a4UEb8AlhXnKd0ydKXSAxp+KemledGOpB5jkCqj09oYqlXTc/JMRPwRWElqrHgDqfFnSb6IXmxYauYc4ajC/saQet7UbEtqHFpayAffIt3VgaRtJM1SukXuMeC7rM4/44H78kW0RvFsCxxd16g1KX+upliWodgWOLWw3WWk3lUTCuv0V5+r/xuWjaFr6odDzFEBbJrfb0a6O6il3LDU+5YAW0gaXZj3AuC+/P464CXAO4BrI+LOvPwASt5Okj+7KfANpTGN7icd8Gt1O1xEPEnq+v1RGjcsLQL2KybRiNgwIu4jVQ6nkbqObgZMzp/xfbJm1de1eQsg90A6gzx2Cuk/83pLSSdeNS9Y2/2aWcu0IwfVuxCYDUyKiM1IjUS1c5ZFpFvT+nM2qdHocqXxkwAeJt2e9vLCOdJmsfqBBM/JSxFxd0QcQqpsngRcImljSc8HxpGGFSD3WvhcROwI/AOpArvWubNLnAV8PCJeRbpVqXbV/3esvj3oHcDofHuiWb1rSbfXrp/rLNeSjp/NWX2La6vPERaRehFtVcgHm0ZEbfzZr5Byws6RboV9H6vzz1JgQt1YQ8V4FpF6YBfrY8+LiOIta43Og8rG/eG6bW8UEdeV+OxS0nAHNZPqljcbU9X1l6NOAN6n9KTAy0kdN1rKDUs9LldwrgO+ojTY4s6kK1K1q/RPAvOAj7H6ZOg6UtfDsidH00n31u5EaoHfldStc1dJO61lET4F7BkRCxssOxP4Uq07pKStJdWuEI0mJdBHSFfsvryWcZhZm3Rb3pK0uaTPKQ28uY7S2CkfAGq3kTwAbFl3K+7FwHH5sxMZhv/gzaw5bcpB9UaTekk9JWl31uw9eQFpQNmDlAae3VJS/e0ZRwJ3AZdJ2igi/k5qcDqldouv0mDBtfFCnpOXJL1P0tb5s8vz7GdIvZWurPVYkPQmSTspDfz7GOnWuGeaLHfXkLQJqSHt+5JuIfX4GJcX/zvpdsibST1P7iONZ2NW71rS8fqLPN1HOgf4VUTUjqOLgRmSdpT0PNK4jE2LiKXAz4CTJW2az1VeJKl2u9toUk+q5ZImAP9R+PhvSL/lo3L+eSdpzLWas4GP5F6Xyo3RB9Q1zJexfs63tde6pLreccoPYFIagPzdJbd3MfCJnPfGAMfULX8AeOEQY6y0QXLUIcB5ETGRlNO/k2/JbBk3LI0Mh5B67CwhPVnt+IiYU1h+Lal75G8L06NZnfD6lZPPXqT7bu8vvOaRrp6t1YC0EbEkIn7Vz+JTSVf3fibpcVIlbo+87HxSN837SIOAz224BTOrqm7KW0/nWH9OqmTdTmrYngEQEb8njZdyT+7OPZ704IA/A38inew16pVpZp0zbDmoH/8CfD6fz3yWwsDZeayP/YGjSbc93EIaWJbCOgEcQbrCf6nSeE/HkB4sMDff3vJzUk+r/vLSvsAdklaSzrEOjjRYefE2OIDnA5eQ8t38XPbvNlnubrIOsDwidi28XgbPnq++M49f8uk8b0Ung7XKqs8VvyJdBH82d0TEFcDXgKtJx/DVLdjvYcD6pHrRo6RjuNbo8DnSrb0rgJ+QhiOpxfI0afDsGflz76lbfiNpnKWv5+UL8rpDdQepl2Xt9f6I+BGp9+SsnMNuJ40VVcbZpPOrW0kPSbic1EBWa7w7lTQ276OSemWcy35zFOniyMXw7PAPG7LmEDFrTWveLmlmZmZmZgZKA5TfD7xoJDaUSJoMXBYRr8jT1wGnRMT3861BO0fE73JP1WUR8XelR5k/ExGf7VjgZrYGSfsBZ0bEtoOu3EWGkKOuAL4XEedJehlpTLgJ0cLGIPdYMjMzMzOzRrYA/muENipdRLoN6CWSFks6nPSEvsMl/Y7Uw6I2BMNU4C5JfwDGAl/qQMhmlknaSNL++fa9CaTbCX/U6bhaaYg56mjgQ3n+RcCMVjYqgXssmZmZmZmZmVmPyGNTXQu8lHRr3U+AT0TEYx0NrIe5YcnMzMzMzMzMzJriW+HMzMzMzMzMzKwpozodwEC22mqrmDx5cql1n3jiCTbeeOPhDaiL4gDH0h/H0lwc8+bNezgitm5TSJXXjfmpXVze3le1Mjs/rWkk56deKk8vlQVGbnmcn56rbI7q5t9Mt8berXFD98beybhbmZ8q3bA0efJkbrzxxlLr9vX1MXXq1OENqIviAMfSH8fSXByS/tyeaLpDN+andnF5e1/Vyuz8tKaRnJ96qTy9VBYYueVxfnqusjmqm38z3Rp7t8YN3Rt7J+NuZX7yrXBmZmZmZmZmZtYUNyyZmZmZmZmZmVlT3LBkZmZmZmZmZmZNqfQYS0Nx230rmHHsT1q2vYUnHtCybZnZyOb8ZGZV1er8BM5RZtYazk9m3cM9lszMzMzMzMzMrCluWDIzMzMzMzMzs6a4YcnMzMzMzMzMzJrihiUzMzMzMzMzM2uKG5bMzMzMzMzMzKwpblgyMzMzMzMzM7OmuGHJzMzMzMzMzMya4oYlMzMzMzMzMzNrihuWzMzMzMzMzMysKW5YMjMzMzMzMzOzprhhyczMzGwYSBoj6RJJv5c0X9JrJW0haY6ku/O/m+d1Jek0SQsk3Sppt8J2puf175Y0vXMlMjMzM3suNyyZWVdzxc3MKuxU4MqIeCmwCzAfOBa4KiJ2AK7K0wD7ATvk1xHANwEkbQEcD+wB7A4cX8tpZmZmZlVQqmHJFTczqzBX3MysciRtCrwROAcgIp6OiOXANGBmXm0mcGB+Pw04P5K5wBhJ44B9gDkRsSwiHgXmAPu2sShmZmZmAxpVcr1axe1dktYHngd8ilRxO1HSsaSK2zGsWXHbg1Rx26NQcZsCBDBP0ux8kmRmNmSFitsMSBU34GlJ04CpebWZQB8pPz1bcQPm5kbzcXndORGxLG+3VnG7qF1lMbOe80LgIeB/Je0CzAM+AYyNiKUAEbFU0jZ5/QnAosLnF+d5/c1fg6QjSA3mjB07lr6+vlJBjt0Ijt5pVflSlVB238Nh5cqVHd1/K/VSWcDlqRpJY4BvA68g1c0+ANwFfA+YDCwEDoqIRyWJVB/cH3gSmBERN+XtTAc+kzf7xYiYiZmNOIM2LLniZmYVNiIrblU/ke32k+2hGmnlhZFZ5iaMAnYDPh4R10s6ldW9JxtRg3kxwPw1Z0ScBZwFMGXKlJg6dWqpIE+/4FJOvq3sdcZyFh5abt/Doa+vj7Jlr7peKgu4PBXkjgNm1jJlziTaWnEzMxuCEVlx62SlrYweONkekpFWXhiZZW7CYmBxRFyfpy8h5acHJI3L507jgAcL608qfH4isCTPn1o3v28Y4zazHueOA2bWamVqOm2tuHV7j4AqXcV1LI05lurG0QRX3MyskiLifkmLJL0kIu4C9gLuzK/pwIn530vzR2YDR0qaReoRsCLnsJ8CXy6M+7Y3cFw7y2JmPccdB8yspco0LLW14tbtPQKqdBXXsTTmWKobx1C54mZmFfdx4IJ8m8k9wPtJD065WNLhwL3Au/O6l5PGL1lAGsPk/QARsUzSF4Ab8nqfr/UOMDNrUls7DkBznQe6eQy4br1o261xQ/fG3q1x1xu0JcYVNzOrOFfczKySIuIW0tgj9fZqsG4AH+tnO+cC57Y2OjMbwdre47uZzgPdPAZct1607da4oXtj79a465U9Ul1xM7NKcsXNzMzMrDx3HDCzVivVsOSKm5mZmZmZWc9wxwEza5nW9i00MzMzMzOzSnPHATNrpXU6HYCZmZmZmZmZmXUnNyyZmZmZmZmZmVlT3LBkZmZmZmZmZmZNccOSmZmZmZmZmZk1xQ1LZmZmZmZmZmbWFDcsmZmZmZmZmZlZU9ywZGZmZmZmZmZmTXHDkpmZmZmZmZmZNcUNS2ZmZmZmZmZm1hQ3LJmZmZmZmZmZWVPcsGRmZmZmZmZmZk1xw5KZmZmZmZmZmTXFDUtmZmZmZmZmZtYUNyyZmZmZmZmZmVlT3LBkZmZmZmZmZmZNccOSmZmZmZmZmZk1xQ1LZmZmZmZmZmbWFDcsmZmZmQ0TSetKulnSZXl6O0nXS7pb0vckrZ/nb5CnF+TlkwvbOC7Pv0vSPp0piZmZmVljpRuWfGJkZlXl/GRmFfYJYH5h+iTglIjYAXgUODzPPxx4NCK2B07J6yFpR+Bg4OXAvsA3JK3bptjNzMzMBjWUHks+MTKzqnJ+MrPKkTQROAD4dp4W8GbgkrzKTODA/H5aniYv3yuvPw2YFRF/jYg/AQuA3dtTAjPrZb4wZ2atMqrMSoUToy8BnyycGL03rzITOAH4JukE6IQ8/xLg6/UnRsCfJNVOjH7TkpKY2Yjk/GRmFfY14D+B0Xl6S2B5RKzK04uBCfn9BGARQESskrQirz8BmFvYZvEzz5J0BHAEwNixY+nr6ysV4NiN4OidVg2+4hCU3fdwWLlyZUf330q9VBZweSqqdmFu0zxduzA3S9KZpAty36RwYU7SwXm999RdmBsP/FzSiyPimXYXxMw6q1TDEm08MTIzG6IRV3Gr+olsj5xslzbSygsjs8xDJemtwIMRMU/S1NrsBqvGIMsG+szqGRFnAWcBTJkyJaZOnVq/SkOnX3ApJ99W9nSwnIWHltv3cOjr66Ns2auul8oCLk/V+MKcmbXSoGcS7T4x6vaKW5VOth1LY46lunEM1UituHWy0lZGt59sD9VIKy+MzDI34XXA2yXtD2xI6hHwNWCMpFG58XsisCSvvxiYBCyWNArYDFhWmF9T/IyZWbPa2nGgmTpeN/eo7NZz626NG7o39m6Nu16Zmk5bT4y6veJWpZNtx9KYY6luHE1wxc3MKikijgOOA5pKdTcAACAASURBVMgN3/8eEYdK+j7wLmAWMB24NH9kdp7+TV5+dUSEpNnAhZL+h3SryQ7Ab9tZFjPrLe2+MAfN1fG6uUdlt55bd2vc0L2xd2vc9QYdvDsijouIiRExmXQP7dURcShwDenEBxqfGEHhxCjPPzgP/rYdPjEys7Xk/GRmXegY0m0nC0hX/M/J888BtszzPwkcCxARdwAXw/9n786jZanLe/+/P4IDKgKKnsukGEUTohGVOMSoJ5IgYBLI+mkuBgMaEm6cYiK5EU3uFU3M0tzrEI1DiCKoKCIxynUMQY5DFFQUQUQEkcgREJVZ44B5fn/Ud0uz2fvs3n167vdrrVq7u6q66/l293762099q4qvAB8Bnu35SyRtpaUdc5fRFbmfQM+OubbOSjvmcMecpJVsTQn4BcDJSf4G+CK37hi9vXWMrqH7sUdVXZBkqWN0M3aMJI2O+UnS1KiqTcCmdvtSVriqW1X9EHjKKo9/Gd15UCRpqzmiUtKwrauwZMdI0rQyP0mSJG0Vd8xJGshwD1qVJEmSJM0Ed8xJGoY1z7EkSZIkSZIkrcTCkiRJkiRJkgZiYUmSJEmSJEkDsbAkSZIkSZKkgVhYkiRJkiRJ0kAsLEmSJEmSJGkgFpYkSZIkSZI0EAtLkiRJkiRJGoiFJUmSJEmSJA3EwpIkSZIkSZIGYmFJkiRJkiRJA7GwJEmSJEmSpIFYWJIkSZIkSdJALCxJkiRJkiRpIBaWJEmSJEmSNBALS5IkSZIkSRqIhSVJkiRJkiQNxMKSJEnSkCXZI8mZSS5MckGS57X5d09yepKL29+d2vwkeW2SS5Kcl+RhPc91RFv/4iRHTKpNkiRJK1mzsGTHSNK0Mj9JmmI3A0dX1S8AjwKenWRv4BjgjKraCzij3Qc4ENirTUcBb4QunwEvBh4JPAJ48VJOkyRJmgb9jFiyYyRpWpmfJE2lqrqyqr7Qbt8IXAjsBhwMnNhWOxE4pN0+GHhbdc4CdkyyC/BE4PSquqaqrgVOBw4YY1MkzRl3zEkatjULS3aMJE0r85OkWZBkT+ChwNnAhqq6ErocBtyrrbYbcHnPwza3eavNl6RBuWNO0lBtu56Vt9QxSmLHSNLEjCM/JTmKrkPFhg0b2LRpU1+xbdgOjn7wzf03Zg39bndSbrrppqmPcZgWrb2wmG0eVJK7Av8M/GlV3ZBk1VVXmFdbmL98O1ORn2CyOWqePpvz1BawPdOk9ZGW+kk3JundMbexrXYisAl4AT075oCzkiztmNtI2zEHkGRpx9y7xtYYSVOh78LSonWMBv2imKYvGWNZmbFMbxyDGld+qqrjgOMA9t1339q4cWNf8b3upPfzyvPXVcffossO62+7k7Jp0yb6fW3mwaK1FxazzYNIcnu63HRSVb23zf52kl1a0XsX4Oo2fzOwR8/DdweuaPM3Lpu/afm2piU/wWRz1Dx9NuepLWB7ptW4Bg4M8htvlgvfs9q3ntW4YXZjn9W4l+urJ7GIHaNBO0XT9CVjLCszlumNYxDjzE+S1K90Fe63ABdW1at6Fp0GHAG8vP19f8/85yQ5me6wkutbDvso8Lc9h5fsD7xwHG2QNN/GtWMOBvuNN8uF71ntW89q3DC7sc9q3Mv1c1W4tTpGcNuO0eHtJG+PonWMgI8C+yfZqXWO9m/zJGkg5idJU+wxwO8DT0hybpsOoiso/UaSi4HfaPcBPgRcClwC/BPwLIB2iMlfA59r00uXDjuRpEFtacdcW97vjrmV5ktaMP2UgJc6RucnObfNexFdR+iUJEcC3wSe0pZ9CDiIrmP0A+AZ0HWMkix1jMCOkaStZ36SNJWq6lOsvDcfYL8V1i/g2as81/HA8cOLTtIic0SlpGFbs7Bkx0jStDI/SZIkrZs75iQN1XAPWpUkSZIkTS13zEkatjXPsSRJkiRJkiStxMKSJEmSJEmSBmJhSZIkSZIkSQOxsCRJkiRJkqSBWFiSJEmSJEnSQCwsSZIkSZIkaSAWliRJkiRJkjQQC0uSJEmSJEkaiIUlSZIkSZIkDcTCkiRJkiRJkgZiYUmSJEmSJEkDsbAkSZIkSZKkgVhYkiRJkiRJ0kAsLEmSJEmSJGkgFpYkSZIkSZI0EAtLkiRJkiRJGoiFJUmSJEmSJA3EwpIkSZIkSZIGMvbCUpIDklyU5JIkx4x7+5K0GvOTpGllfpI0rcxPksZaWEqyDfB64EBgb+CpSfYeZwyStBLzk6RpZX6SNK3MT5IAth3z9h4BXFJVlwIkORk4GPjKmOOQpOXMT5Km1ULnpz2P+WDf6x794Jt5+jrWH6bLXv6kiWxXmrCFzk+SOuMuLO0GXN5zfzPwyN4VkhwFHNXu3pTkoj6fe2fgu1sd4VIcrxj4oUONYysZy8qM5bb6ieM+4whkghYhP43LtHyux2XR2gvT12bz05TkJ5hsjvqTCX42R9Duafs/21qL2p6Fz08wcI6a5fw0q5/3WY0bZjf2ScY9tPw07sJSVphXt7pTdRxw3LqfOPl8Ve07aGDDMi1xgLGsxlimN44Jm/v8NC62d/4tYpsnzPzUp3lqzzy1BWzPHFszP8FgOWqWX+NZjX1W44bZjX1W415u3Cfv3gzs0XN/d+CKMccgSSsxP0maVuYnSdPK/CRp7IWlzwF7JblvkjsAhwKnjTkGSVqJ+UnStDI/SZpW5idJ4z0UrqpuTvIc4KPANsDxVXXBkJ5+3cO/R2Ra4gBjWY2x3Na0xDExC5KfxsX2zr9FbPPEmJ/WZZ7aM09tAdszl8xPq5rV2Gc1bpjd2Gc17ltJ1W0OgZUkSZIkSZLWNO5D4SRJkiRJkjQnLCxJkiRJkiRpIDNVWEpyQJKLklyS5JgVlt8xybvb8rOT7DnBWJ6f5CtJzktyRpL7TCqWnvWenKSSjOxyhv3EkuR322tzQZJ3TiqWJPdOcmaSL7b36aARxXF8kquTfHmV5Uny2hbneUkeNoo4+ozlsBbDeUk+neQho4plEfT7vzkvkuzR/qcubP/fz5t0TOOQZJuWRz4w6VhGLcmOSU5N8tX2Pj960jFpMNOYn5JcluT8JOcm+Xybd/ckpye5uP3dqc1f9bszyRFt/YuTHNEz/+Ht+S9pj82WtjFA/Lf5jp1k/FvTv1ilLccm+VZ7f87t7TcleWHbzkVJntgzf8XPWboTPZ/dYn53upM+b7Evv9o2+mzPit9Ps/r+zLvVPjcTiGNin5shxX+r/skw/+9G+R5lhb7GLLzmSf6sfU6+nORdSe40K6/5UFTVTEx0J4P7OvBzwB2ALwF7L1vnWcCb2u1DgXdPMJZfA+7cbj9zkrG09bYHPgGcBew7wddlL+CLwE7t/r0mGMtxwDPb7b2By0YUy+OAhwFfXmX5QcCHgQCPAs4eRRx9xvIrPe/NgaOMZd6nfv8352kCdgEe1m5vD3xt3tvc2vp84J3AByYdyxjaeiLwh+32HYAdJx2T00Dv41TmJ+AyYOdl8/4OOKbdPgZ4Rbu94ncncHfg0vZ3p3Z76Xvts8Cj22M+DBy4pW0MEP9tvmMnGf/W9C9WacuxwJ+vsO7e7TN0R+C+7bO1zZY+Z8ApwKHt9pu4pT+2Yl9+tW2soz0rfj/N6vszz9OWPjcTiGVin5shxX+r/smw/u9G/R6xQl9j2l9zYDfgG8B2Pa/102flNR/GNEsjlh4BXFJVl1bVj4GTgYOXrXMw3QcR4FRgv2FWfdcTS1WdWVU/aHfPAnYfQRx9xdL8Nd0/5A9HFEe/sfwR8Pqquhagqq6eYCwF3K3d3gG4YhSBVNUngGu2sMrBwNuqcxawY5JdJhFLVX166b1htJ/bRdDv/+bcqKorq+oL7faNwIV0X7RzK8nuwJOAN086llFLcje6H5tvAaiqH1fVdZONSgOapfzU27c7ETikZ/5K351PBE6vqmva99npwAFt2d2q6jPV9d7ftuy5VtrGuqzyHTvJ+AfuX/TRd1nexpOr6kdV9Q3gErrP2Iqfs9Y3fwJdX32lmFfqy6+2jb5s4ftpJt+fOTc1+WnCn5utsrx/MuT/u5G9R1voa0z9aw5sC2yXZFvgzsCVzMBrPiyzVFjaDbi85/5mbvuD5WfrVNXNwPXAPSYUS68j6aqho7BmLEkeCuxRVaM+TKOf1+UBwAOS/HuSs5IcMMFYjgWelmQz8CHguSOKZS3r/TyNyyg/t4tgWt/XsWhDeh8KnD3ZSEbuNcBfAP816UDG4OeA7wBvbUPr35zkLpMOSgOZ1vxUwL8mOSfJUW3ehqq6ErofecC92vzV2rCl+ZtXmL+lbQzDJOMfxfv8nHa4yfG55ZDB9bblHsB1ra++PK7V+vJDa8uy76d5e3/mwVS+LhP43Gyt5f2TYf7fjfI9Wq2vMdWveVV9C/i/wDfpCkrXA+cwG6/5UMxSYWmlkUc1wDrjiqVbMXkasC/wf0YQx5qxJLkd8Grg6BFtv+9Ymm3pDofbCDwVeHOSHScUy1OBE6pqd7phlG9vr9e4jetz27ckv0ZXWHrBJOOYcVP3vo5LkrsC/wz8aVXdMOl4RiXJbwJXV9U5k45lTLalOzTmjVX1UOD7dMPRNXumNT89pqoeRnco9rOTPG4L667WhvXOn5RxxD/sNr8RuB+wD90Pp1eusZ1B2jLS928d30+z+P7Mi6l7Xabsc7OmVfonw/y/G+V7tN6+xlTE3grtB9MdvrYrcBe677LVtjUVcQ/TLBWWNgN79NzfndseuvSzddoQtB3ofxjvsGMhya8Dfwn8dlX9aARx9BPL9sCDgE1JLqM79vS0jOYE3v2+R++vqp+04X0X0RWaJhHLkXTHvVJVnwHuBOw8gljW0tfnaVyS/BLdsNmDq+p7k4pjDkzV+zouSW5P1/k6qareO+l4RuwxwG+33Hoy8IQk75hsSCO1GdhcVUuj0E6l6/xp9kxlfqqqK9rfq4F/oRv6/+2lw4Pa36VD6Fdrw5bm777CfLawjWGYZPxDfZ+r6ttV9dOq+i/gn7jlULT1tuW7dIerbLtCXKv15be6Lat8P83N+zNHpup1meDnZmvcpn9CN4JpWP93o3yPVutrTPtr/uvAN6rqO1X1E+C9dOeunYXXfChmqbD0OWCvdmb1O9Cd5Oq0ZeucBhzRbj8Z+Fg7dnLssbTDz/6Rrqg0qvMIrRlLVV1fVTtX1Z5VtSfdeXN+u6o+P+5YmvfRndicJDvTHRp36YRi+SawX4vlF+gKS98ZQSxrOQ04PJ1HAdcvDfUctyT3pkuEv19VX5tEDHOkn8/gXGnHhr8FuLCqXjXpeEatql5YVbu33Hoo3XfO0yYc1shU1VXA5Uke2GbtB3xlgiFpcFOXn5LcJcn2S7eB/YEvc+u+3RHA+9vt1b47Pwrsn2Sntgd5f+CjbdmNSR7VctXhy55rpW0MwyTjH2r/Irc+/8/v0L0/S9s5NN1Vju5Lt8Pws6zyOWt98zPp+uorxbxSX361bfQb+2rfT3Pz/syRqclPE/7cDGyV/slhDO//bmTv0Rb6GlP9mtP9rnxUkju3512Ke+pf86GpKTiDeL8T3eFKX6M7I/pftnkvpSuUQFcYeA/dSa4+C/zcBGP5N+DbwLltOm1SsSxbdxMjuipcn69LgFfR/aOdTztL/oRi2Rv4d7qz6p8L7D+iON5FN2T8J3TV5iOBPwb+uOc1eX2L8/wRvz9rxfJm4Nqez+3nRxXLIkwrfQbneQJ+lW5Y7nk9n6GDJh3XmNq+kcW4Ktw+wOfbe/w+2hVWnGZvmrb8RHdejS+16YKe7+17AGcAF7e/d2/zV/3uBP6Ari94CfCMnvn70hVDvg78A5AtbWOANqz0HTux+Lemf7FKW97enuc8uh80u/Ss/5dtOxfRc4Wl1T5n7f3+bGvje4A7tvmr9uVX20af7Vnx+2lW3595n1b73Ewgjol9bobYho3cclW4of3fjfI9YoW+xiy85sBLgK+253473ZXdZuI1H8a0lPAkSZIkSZKkdZmlQ+E0IUkqyf0nHYckmY8kzZppz1tJXpTkzZOOQ9JkTHuOWk2Sjemu7q0pYGFpziS5LMl/JrmpZ9p1hNvblOQPl827b5L/SvKGnnn3XhZTJfl+z/3HrvLcleQhy+a/r83fOKp2Sdp65iNJs2YR81ZV/W1V/eHyx0uaPnOWo34xyb8muTbJdUnOSXJQW2bRaMZYWJpPv1VVd+2Zxn3G+MPpztNzaJI7AlTVN3tjaus9pGfeJ1d5rq+15wMgyT3ormw3iZNsS1o/85GkWWPekjTN5iVH/T/gdGADcC/gT4AbxhC/RsDC0oJIcmKSo9vt3VoV+Vnt/v2TXJMk7f7/THJlkiuS/MEAmzsc+Cu6Ez7+1laGfhLw35Ns0+4/le4SxD9eWiHJI5J8plW6r0zyD+nOlk+SX0ny3SRLl3N8SFvv57cyLkkDmvN8dLskxyT5epLvJTklyd17lr8nyVVJrk/yiSS/2LPshCSvT/LBJDcmOTvJ/bYyZklDMOd569gk72i392xtOyLJN1sf6i+3MgZJIzZrOSrd1cHvC/xTVf24Tf9eVZ9Kd2XQDwO79ox42jXJdq2vdG2SrwC/PMi2NRoWlhbHx+muCgDweODS9hfgccAnq6qSHAD8OfAbdJc3/PX1bKQNc9wdOBk4hZ69ZAO6gu4Kcvu3+4cDb1u2zk+BPwN2Bh5Nd3nHZwFU1aeBfwROTLId3Rn6/6qqvrqVcc2MJMcnuTrJl/tY995JzkzyxSTnLQ1HlYZsnvPRnwCH0LVnV7o9eq/vWf5hurbcC/gC3Y++Xk+lu6rITnRXCnnZVsY81daZn16d5Nw2fS3JdeOIUWrmOW+t5FeBB9L1qf53kl/Yyjgkjdas5ajv0fVz3pHkkCQblhZU1feBA4Erlo3KejFwvzY9EThiwG1rBCwszaf3tVE51yV5X5v3ceCxSW5Hl1z+DnhMW/b4thzgd4G3VtWX2z/1sevc9hHAh6vqWuCdwIFJ7rUVbYGuA3R4kgcCO1bVZ3oXVtU5VXVWVd1cVZfRFZIe37PKscAOdJdyvIJb/8hbBCcAB/S57l8Bp1TVQ4FDgTessb60loXKR8D/oLsE7Oaq+lGL+clJtgWoquOr6saeZQ9JskPP499bVZ+tqpvpik77bGW80+4E+sxPVfVnVbVPVe0DvA547ygD00JbtLy1kpdU1X9W1ZeALwEPWesBksZm5nNUdZem/zXgMuCVwJVtJPdeW3jY7wIvq6prqupy4LXr3a5Gx8LSfDqkqnZs0yEAVfV14Ca6HymPBT4AXNE6Gb3JZlfg8p7n+o9+N9pGBD2Ftge+dVy+Cfze1jWH9wJPAJ5LN+Jo+XYfkOQD7fCSG4C/pRu9RIvjJ3Q/Xh4EvLIlsoVRVZ8Arumdl+R+ST6S7iR5n8wthwYWcLd2ewe6Qpy0NRYqHwH3Af5lqcMHXEg3qnJDkm2SvDzdYXI30HWmoCdfAVf13P4BcFfm2DrzU6+nAu8aS5BaRIuWt1ayULlImjFzkaPaTrjnVNX96PpP32fLIyoHjl2jZ2FpsXwceDJwh6r6Vrt/ON0hF+e2da4E9uh5zL3X8fy/Q1eUeEMr8lwF7MZWDuOuqh/QHT7yTFbuEL0R+CqwV1XdDXgRkKWFSXajGzr5VuCVaSeZW3DHAc+tqofTDYddGpl0LPC0dFdh+BBdJ1QahXnNR5cDB/Z0+Hasqju1Nv4ecDDdsPMdgD3bY7LC8yyy1fITAEnuQ3deho9NIDYttnnNW5Lmw0zmKIA2Aun1dAMBoNvZvdzWxK4Rs7C0WD4OPAf4RLu/ia5w8Kmq+mmbdwrw9CR7J7kzXUGmX0cAxwMPpquW70M3BHOfJA/eythfBDy+Heq23PZ0VxC4qe3ZfubSgnaSuhOAtwBH0iWkv97KWGZakrsCvwK8J8m5dIcO7tIWPxU4oap2Bw4C3t6G1ErDNq/56E3Ay1rxgyT3THJwW7Y98CO68wrcmW50pXqskZ+WHAqc2vM5kcZlXvOWpPkwMzkqyU5JXpLuxOK3S3cy7z8AzmqrfBu4x7LTBZwCvLA9dnfcAT5V/MG4WD5O98NmKdl8iu7HzdJ9qurDwGvo9gRfQp97hNuooP2A11TVVT3TOcBH2MqTq1XVFVX1qVUW/zndSIAbgX8C3t2z7E/oLmH5v9ohcM8AntFOPLeobgdct3SukjYtnZTzSLqkvTS89U7c+jAdaVjmNR/9PXAa8K9JbqTrID2yLXsb3bDtb9GdTPesFZ9hsW0pPy05FA+D02TMa96SNB9mKUf9mG7k9r/RDRD4Mt3Ot6e3OL9K911/aTu9wK50Fzf5D+AbwL/iCMypkgU73Yy0kJLsCXygqh7U7n8aeHVVvaeN6vqlqvpSkg8D766qE9oVYM4Adlu081JJGp9+81Nb9kDgo8B9zUuSJEnTwRFL0pxL8i7gM8ADk2xOciRwGHBkki8BF9Cd9wXgaOCP2vx3AU/3x5ukUVlnfoLucN2TzUuSJEnTwxFLkiRJkiRJGogjliRJkiRJkjQQC0uSJEmSJEkayLaTDmBLdt5559pzzz37Wvf73/8+d7nLXUYb0AzFAcayGmMZLI5zzjnnu1V1zzGFNPVmMT/B9MQyLXGAsaxmlmIxP92a+Wk+4gBjWc0sxWJ+uq1+c9Q0vc/jYpsXxzS0e6j5qaqmdnr4wx9e/TrzzDP7XneUpiWOKmNZjbHcVj9xAJ+vKcgL0zLNYn6qmp5YpiWOKmNZzSzFYn4yPw3TtMRRZSyrmaVYzE+D56hpep/HxTYvjmlo9zDzk4fCSZIkSZIkaSAWliRJkiRJkjQQC0uSJEmSJEkayFSfvHs9zv/W9Tz9mA8O7fkue/mThvZckhab+UnStBp2fgJzlKThMD9Js8MRS5IkSZIkSRqIhSVJkiRJkiQNxMKSJEmSJEmSBmJhSZIkSZIkSQOxsCRJkiRJkqSBWFiSJEmSJEnSQCwsSZIkSZIkaSAWliRJkiRJkjQQC0uSJEmSJEkaiIUlSZIkSZIkDaSvwlKSHZOcmuSrSS5M8ugkd09yepKL29+d2rpJ8toklyQ5L8nDep7niLb+xUmOGFWjJEmSJEmSNHr9jlj6e+AjVfXzwEOAC4FjgDOqai/gjHYf4EBgrzYdBbwRIMndgRcDjwQeAbx4qRglSZIkSZKk2bNmYSnJ3YDHAW8BqKofV9V1wMHAiW21E4FD2u2DgbdV5yxgxyS7AE8ETq+qa6rqWuB04IChtkbSwnFEpaRpZX6SJEmLoJ8RSz8HfAd4a5IvJnlzkrsAG6rqSoD2915t/d2Ay3sev7nNW22+JG0NR1RKmlbmJ0lTK8k27ffdB9r9+yY5uxWx353kDm3+Hdv9S9ryPXue44Vt/kVJnjiZlkiatG37XOdhwHOr6uwkf88tnaCVZIV5tYX5t35wchRdh4oNGzawadOmPkKEDdvB0Q++ua91+9Hvdpe76aabBn7ssBnLyoxleuNYr54RlU+HbkQl8OMkBwMb22onApuAF9AzohI4q40m2KWte3pVXdOed2lE5bvG1RZJ88X8JGkGPI+u4H23dv8VwKur6uQkbwKOpCtyHwlcW1X3T3JoW++/J9kbOBT4RWBX4N+SPKCqfjruhkiarH4KS5uBzVV1drt/Kl1h6dtJdqmqK1vH5+qe9ffoefzuwBVt/sZl8zct31hVHQccB7DvvvvWxo0bl6+yoted9H5eeX4/zenPZYf1t93lNm3aRL8xj5qxrMxYpjeOAfSOqHwIcA5dJ+lWIyqTDGVE5awXvmF6iojTEgcYy2qMZastZH6C2d85Ny1xgLGsxli2XpLdgScBLwOenyTAE4Dfa6ucCBxLV1g6uN2G7rfgP7T1DwZOrqofAd9IcgndyMrPjKkZkqbEmpWYqroqyeVJHlhVFwH7AV9p0xHAy9vf97eHnAY8J8nJdMO2r28dp48Cf9szfHt/4IXDbY6kBTPWEZWzXviG6SkiTkscYCyrMZattpD5CWZ/59y0xAHGshpjGYrXAH8BbN/u3wO4rqqWKs29ReyfFbir6uYk17f1dwPO6nnOVU91Mkjxe5oK3+Myq4XKrbGIbYb5a3e/PYnnAie142wvBZ5Bd36mU5IcCXwTeEpb90PAQcAlwA/aulTVNUn+GvhcW++lS8O6JWlAYx1RKUnrYH6SNJWS/CZwdVWdk2Tj0uwVVq01lvVV+IbBit/TVPgelxkuVA5sEdsM89fufk7eTVWdW1X7VtUvVdUhVXVtVX2vqvarqr3a32vaulVVz66q+1XVg6vq8z3Pc3xV3b9Nbx1VoyQthqq6Crg8yQPbrKURlafRjaSE246oPLxdfelRtBGVwEeB/ZPs1EZV7t/mSdJAzE+SpthjgN9OchlwMt0hcK+hu5r3UiVnqbgNPYXvtnwH4BpWL4hLWjDDLQFL0vg5olLStDI/SZo6VfVC2ilJ2oilP6+qw5K8B3gyXbFpeeH7CLpzJz0Z+FhVVZLTgHcmeRXdybv3Aj47zrZImg4WliTNtKo6F9h3hUX7rbBuAc9e5XmOB44fbnSSFpn5SdKMeQFwcpK/Ab4IvKXNfwvw9nZy7mvorgRHVV2Q5BS60Zg3A8/2inDSYrKwJEmSJEkLqKo20c7bVlWX0l3Vbfk6P+SW0ZXLl72M7spykhZYX+dYkiRJkiRJkpazsCRJkiRJkqSBWFiSJEmSJEnSQCwsSZIkSZIkaSAWliRJkiRJkjQQC0uSJEmSJEkaiIUlSZIkSZIkDcTCkiRJkiRJkgZiYUmSJEmSJEkDsbAkSZIkSZKkgVhYkiRJkiRJ0kAsLEmSJEmSJGkgFpYkSZIkSZI0EAtLkiRJkiRJGoiFJUmSJEmSJA3EwpIkSZIkSZIGYmFJkiRJkiRJA+m7sJRkmyRfTPKBdv++Sc5OcnGSdye5Q5t/x3b/krZ8z57neGGbf1GSJw67MZIkSZIkSRqf9YxYeh5wZ6MEqAAAIABJREFUYc/9VwCvrqq9gGuBI9v8I4Frq+r+wKvbeiTZGzgU+EXgAOANSbbZuvAlycK3pOllfpIkSfOur8JSkt2BJwFvbvcDPAE4ta1yInBIu31wu09bvl9b/2Dg5Kr6UVV9A7gEeMQwGiFp4Vn4ljStzE+SJGmubdvneq8B/gLYvt2/B3BdVd3c7m8Gdmu3dwMuB6iqm5Nc39bfDTir5zl7H/MzSY4CjgLYsGEDmzZt6ivADdvB0Q++ee0V+9Tvdpe76aabBn7ssBnLyoxleuMYRE/h+2XA83sK37/XVjkROBZ4I12B+9g2/1TgH5YXvoFvJFkqfH9mTM2QNIfMT5IkaRGsWVhK8pvA1VV1TpKNS7NXWLXWWLalx9wyo+o44DiAfffdtzZu3Lh8lRW97qT388rz+62Tre2yw/rb7nKbNm2i35hHzVhWZizTG8eALHyvw7QUEaclDjCW1RjLUCxcfoLZ3zk3LXGAsazGWCRpuvRTiXkM8NtJDgLuBNyNrqO0Y5JtW+dod+CKtv5mYA9gc5JtgR2Aa3rmL+l9jCStm4Xv9ZuWIuK0xAHGshpj2TqLmp9g9nfOTUscYCyrMRZJmi5rnmOpql5YVbtX1Z50x/h/rKoOA84EntxWOwJ4f7t9WrtPW/6xqqo2/9B2csr7AnsBnx1aSyQtoqXC92XAyXSHmPys8N3WWanwjYVvSSNmfpIkSQthPVeFW+4FdOcLuIRuqPZb2vy3APdo858PHANQVRcApwBfAT4CPLuqfroV25e04Cx8S5pW5idJkrQo1jX2uao2AZva7UtZ4apuVfVD4CmrPP5ldCewlKRRegFwcpK/Ab7IrQvfb2+F72vofuxRVRckWSp834yFb0mjY36SJElzZbgH1UvShFj4ljStzE+SJGmebc2hcJIkSZIkSVpgFpYkSZIkSZI0EAtLkiRJkiRJGojnWJIkSZIkzb09j/ngUJ/vspc/aajPJ80qRyxJkiRJkiRpIBaWJEmSJEmSNBALS5IkSZK0IJLskeTMJBcmuSDJ89r8uyc5PcnF7e9ObX6SvDbJJUnOS/Kwnuc6oq1/cZIjJtUmSZNlYUmSJEmSFsfNwNFV9QvAo4BnJ9kbOAY4o6r2As5o9wEOBPZq01HAG6ErRAEvBh4JPAJ48VIxStJisbAkSZIkSQuiqq6sqi+02zcCFwK7AQcDJ7bVTgQOabcPBt5WnbOAHZPsAjwROL2qrqmqa4HTgQPG2BRJU8KrwkmSJEnSAkqyJ/BQ4GxgQ1VdCV3xKcm92mq7AZf3PGxzm7fa/JW2cxTdaCc2bNjApk2b1oxtw3Zw9INv7r8xE9BPO9bjpptuGvpzTrtFbDPMX7stLEmSJEnSgklyV+CfgT+tqhuSrLrqCvNqC/NvO7PqOOA4gH333bc2bty4ZnyvO+n9vPL86f65etlhG4f6fJs2baKf12aeLGKbYf7a7aFwkiRJkrRAktyerqh0UlW9t83+djvEjfb36jZ/M7BHz8N3B67YwnxJC8bCkiRJkiQtiHRDk94CXFhVr+pZdBqwdGW3I4D398w/vF0d7lHA9e2QuY8C+yfZqZ20e/82T9KCme6xhZIkSZKkYXoM8PvA+UnObfNeBLwcOCXJkcA3gae0ZR8CDgIuAX4APAOgqq5J8tfA59p6L62qa8bTBEnTxMKSJEmSJC2IqvoUK58fCWC/FdYv4NmrPNfxwPHDi07SLPJQOEmSJEmSJA3EwpIkSZIkSZIGYmFJkiRJkiRJA1mzsJRkjyRnJrkwyQVJntfm3z3J6Ukubn93avOT5LVJLklyXpKH9TzXEW39i5Mcsdo2JUmSJEmSNP36GbF0M3B0Vf0C8Cjg2Un2Bo4BzqiqvYAz2n2AA4G92nQU8EboClHAi4FHAo8AXrxUjJKkQVj4ljStzE+SJGlRrFlYqqorq+oL7faNwIXAbsDBwIlttROBQ9rtg4G3VecsYMckuwBPBE6vqmuq6lrgdOCAobZG0qKx8C1pWpmfJEnSQljXOZaS7Ak8FDgb2FBVV0JXfALu1VbbDbi852Gb27zV5kvSQCx8S5pW5idJkrQotu13xSR3Bf4Z+NOquiHJqquuMK+2MH/5do6i21PHhg0b2LRpU1/xbdgOjn7wzX2t249+t7vcTTfdNPBjh81YVmYs0xvH1thS4TvJUArfs56fYHre62mJA4xlNcYyPIuUn2D2+1DTEgcYy2qMRZKmS1+FpSS3pysqnVRV722zv51kl9Yp2gW4us3fDOzR8/DdgSva/I3L5m9avq2qOg44DmDfffetjRs3Ll9lRa876f288vy+62Rruuyw/ra73KZNm+g35lEzlpUZy/TGMahxFb5nPT/B9LzX0xIHGMtqjGU4Fi0/wez3oaYlDjCW1RiLJE2Xfq4KF+AtwIVV9aqeRacBSyeQPAJ4f8/8w9tJKB8FXN/2zH0U2D/JTu3cAPu3eZI0sC0VvtvyfgvfK82XpIGZnyRJ0iLo5xxLjwF+H3hCknPbdBDwcuA3klwM/Ea7D/Ah4FLgEuCfgGcBVNU1wF8Dn2vTS9s8SRqIhW9J08r8JEmSFsWaY5+r6lOsPAwbYL8V1i/g2as81/HA8esJUJK2YKnwfX6Sc9u8F9EVuk9JciTwTeApbdmHgIPoCt8/AJ4BXeE7yVLhGyx8S9p65idJkrQQhntQvSSNkYVvSdPK/CRJkhZFP4fCSZIkSZIkSbdhYUmSJEmSJEkDsbAkSZIkSZKkgVhYkiRJkiRJ0kAsLEmSJEmSJGkgFpYkSZIkSZI0EAtLkiRJkiRJGoiFJUmSJEmSJA3EwpIkSZIkSZIGYmFJkiRJkiRJA7GwJEmSJEmSpIFYWJIkSZIkSdJALCxJkiRJkiRpIBaWJEmSJEmSNBALS5IkSZIkSRqIhSVJkiRJkiQNxMKSJEmSJEmSBmJhSZIkSZIkSQOxsCRJkiRJkqSBjL2wlOSAJBcluSTJMePeviStxvwkaVqZnyRNK/OTpLEWlpJsA7weOBDYG3hqkr3HGYMkrcT8JGlamZ8kTSvzkySAbce8vUcAl1TVpQBJTgYOBr4y5ji0ij2P+eDQn/Oylz9p6M8pjYD5SdK0Mj9JmlbmJw2Fv0Nn27gLS7sBl/fc3ww8sneFJEcBR7W7NyW5qM/n3hn47lZHuBTHKwZ+6FDj2EpTEUt7LacilsZYbqufOO4zjkAmaBHyE8zWZ25cjGVlsxSL+WlK8hPMRR9qWuIAY1nNLMWy8PkJBs5R0/Q+r2gr+2Qrmfo2j8DI2jyC92eYpuG9Hlp+GndhKSvMq1vdqToOOG7dT5x8vqr2HTSwYZmWOMBYVmMs0xvHhM19foLpiWVa4gBjWY2xTBXz0wLGAcayGmOZKmvmJxgsRy3ia2ubF8e8tXvcJ+/eDOzRc3934IoxxyBJKzE/SZpW5idJ08r8JGnshaXPAXsluW+SOwCHAqeNOQZJWon5SdK0Mj9JmlbmJ0njPRSuqm5O8hzgo8A2wPFVdcGQnn7dw79HZFriAGNZjbHc1rTEMTELkp9gemKZljjAWFZjLFPC/DR20xIHGMtqjGVKLFB+GhfbvDjmqt2pus0hsJIkSZIkSdKaxn0onCRJkiRJkuaEhSVJkiRJkiQNZKYKS0kOSHJRkkuSHLPC8jsmeXdbfnaSPScYy/OTfCXJeUnOSHKfScXSs96Tk1SSkV3WsJ9Ykvxue20uSPLOScWS5N5JzkzyxfY+HTSiOI5PcnWSL6+yPEle2+I8L8nDRhFHn7Ec1mI4L8mnkzxkVLHMo2nJUeanwWMxP91muflpTpif1h9Lz3rmp1svNz/ddrn5aYj6/d+cJ2t9xuZRkj1aLrmw5bXnTTqmUUtypySfTfKl1uaXTDqmoamqmZjoTgb3deDngDsAXwL2XrbOs4A3tduHAu+eYCy/Bty53X7mJGNp620PfAI4C9h3gq/LXsAXgZ3a/XtNMJbjgGe223sDl40olscBDwO+vMryg4APAwEeBZw9ijj6jOVXet6bA0cZy7xN05KjzE9b9bqYn2673Pw0B5P5abBY2nrmJ/OT+WmMU7//m/M2rfUZm8cJ2AV4WLu9PfC1eX+vW766a7t9e+Bs4FGTjmsY0yyNWHoEcElVXVpVPwZOBg5ets7BwInt9qnAfkkyiViq6syq+kG7exaw+wji6CuW5q+BvwN+OKI4+o3lj4DXV9W1AFV19QRjKeBu7fYOwBWjCKSqPgFcs4VVDgbeVp2zgB2T7DKJWKrq00vvDaP93M6jaclR5qfBYzE/3Zb5aT6YnwaIpTE/mZ/MT+PV7//mXOnj8z53qurKqvpCu30jcCGw22SjGq2Wr25qd2/fprm4mtosFZZ2Ay7vub+Z237wfrZOVd0MXA/cY0Kx9DqSbo/KKKwZS5KHAntU1QdGFEPfsQAPAB6Q5N+TnJXkgAnGcizwtCSbgQ8Bzx1RLGtZ7+dpXEb5uZ1H05KjzE8DxoL5aSXmp/lgfhogFvPTqrEci/lpS8xPW2da31eNUDv8+qF0I3jmWpJtkpwLXA2cXlVz0eZtJx3AOqy012x5da+fdcYVS7di8jRgX+DxI4hjzViS3A54NfD0EW2/71iabemGc2+k25vzySQPqqrrJhDLU4ETquqVSR4NvL3F8l9DjmUt4/rc9i3Jr9F1jH51knHMmGnJUeanAWJpzE+3ZX6aD+andcZiftpiLOanVZifhmLq3leNVpK7Av8M/GlV3TDpeEatqn4K7JNkR+BfWv6c+XNrzdKIpc3AHj33d+e2Q29/tk6SbemG545iSGE/sZDk14G/BH67qn40gjj6iWV74EHApiSX0R2DftqITkDZ73v0/qr6SVV9A7iIrqM0iViOBE4BqKrPAHcCdh5BLGvp6/M0Lkl+CXgzcHBVfW9SccygaclR5qfBYllax/x0a+an+WB+Wn8s5qfVYzE/rcD8NDRT9b5qtJLcnq6odFJVvXfS8YxT2zGwCRjVCNSxmqXC0ueAvZLcN8kd6E4sedqydU4Djmi3nwx8rKpGUeFeM5Y2fPof6TpFozoOfs1Yqur6qtq5qvasqj3pjvv+7ar6/Lhjad5Hd2JOkuxMN7T70gnF8k1gvxbLL9B1jL4zgljWchpweLu6yaOA66vqygnEQZJ7A+8Ffr+qvjaJGGbYtOQo89MAsTTmp9syP80H89M6YzE/mZ/Ww/w0VP18BjUHkgR4C3BhVb1q0vGMQ5J7tpFKJNkO+HXgq5ONakhqCs4g3u9Ed/WHr9FdKeAv27yX0n3RQ/fF9h7gEuCzwM9NMJZ/A74NnNum0yYVy7J1NzGiq5r0+boEeBXwFeB84NAJxrI38O90V5s4F9h/RHG8C7gS+AndXpgjgT8G/rjnNXl9i/P8Eb8/a8XyZuDans/t50cVyzxO05KjzE8Dvy7mJ/PT3E7mp/XHsmxd85P5yfw0pmmlz+C8Tyt9xiYd0xja/Kt0hzme1/O/c9Ck4xpxm3+J7gqf5wFfBv73pGMa1pTWQEmSJEmSJGldZulQOI1Zkkpy/0nHMUpJrkriCRalAYw7RyQ5IcnfjGt7k7AIbZRGwXw0fIvQRmlRmCM1ahaW5kSSy5L8Z5KbeqZdR7i9TUn+cNm8+yb5ryRv6Jl372UxVZLv99x/7LLnuKBn2U+T/LDn/ovWiOnnk9w83JZK82FCOWLp//e7Sd6bZJdRbW+NWDamuyz2ltYZewcoydOTfGorHr9ny6lfWDZ/5yQ/TnfCYWnqmI/MR5JWZ47cco5c4TG/l+Sdo4pJ/bGwNF9+q6ru2jON+woKh9MdX35okjsCVNU3e2Nq6z2kZ94ne5+gqn6xZ91PAs/pWfdvx9oaaf6MO0c8p/0vPwDYke7S3Rq+uyR5UM/93wO+MalgpD6Zj+aT+UgaDnNk/w4CPjTpIBadhaU5l+TEJEe327u1vUnPavfvn+SaJGn3/2eSK5NckeQPBtjc4cBf0Z107reG1YZeSbZJ8pIk30zy7STHJ9m+Lf4EsE1PZf+hbRTTptbO77TXY/stbUNaJOPIEVV1Dd2lZB/UnudJSb6Y5IYklyc5dllMv5rk00mua8ufvkLc2yc5M8lr07ljkv/bkxvelGS7JHcBPgzsOuhev5ZHTm+vxUVJfrdn2QlJXp/kg0luTHJ2kvv1LN+/Peb6JG9I8vEkf5juSkpvAh7dYrquZ5M7rfZ8q3g7t1zNC7pc/LZlbTgmydfbc34lye/0LHtjklN77r8iyRlL77s0Luajvl6jechHuyb553T9sm8k+ZOeZY9I8pn2el+Z5B/SXRlsaXkl+eMkFye5trXXXKWFsKg5suWFz7cYvp3kVT3PfTvgN4CPJLlTknck+V6L53NJNqzvVdagLCzNv48DG9vtx9Ndmvbx7f7jgE9WVSU5APhzun/Mvegufdi3dIe07Q6cDJxC15EYhf8B/C7wWLo470V3lRTo2vPTnsr+F9v8lwL/DXgw8EDgL0cU21RKV3y7OsmX+1j33i3xfzHJeUkOGkeMmqiR54h0l8b+/+iuggHwfbocsSPwJOCZSQ5p696brlPxOuCewD50Vwnpfb57AGcA/15Vf1LdVSheQbeXbR/g/sBudFfa+D5wIHDFIHv9WifndOCddPnmqcAbkvxiz2pPBV4C7ER3Ra2X9bT7VOCFwD2Ai4BfAaiqC+muKPSZFtOOaz3fFryDbqToNu0H4vbA2cvW+Tpd3tyhPfc7cssw96OBX0p3KMxj6a52dESN6eoe5ij1MB9tOfaZz0ftR+D/o7ui3G7AfsCfJnliW+WnwJ8BOwOPbsuftWwbvwn8MvAQuj7hExkR85OmzKLmyL8H/r6q7gbcj+635pJHAJdW1Xfpito7AHvQ5bk/Bv6z37bPmnXmp1cnObdNX8utdyAMhYWl+fK+Vp29Lsn72ryPA49tX+SPA/4OeExb9vi2HLov5rdW1ZfbP/Wx69z2EcCHq+paug7PgUnutRVtWc1hwP+pqv+oqhvoikSHrba3qqq+WlUfq6ofV9VVwGu4JQEvihOAA/pc96+AU6rqocChwBvWWF+zZdw54rXti+tLdJfQfT5AVW2qqvOr6r+q6jy6S+wu/V8eBvxbVb2rqn5SVd+rqt5Oyq4tpvdU1V8BtP//PwL+rKquqaobgb+l+wxvrd8ELquqt1bVzVX1Bbo9eU/uWee9VfXZqroZOImuowTd0OwLquq9bdlrgav62OZqz7eazXQ/En+dLhe/bfkKVfWeqrqivebvBi6m64xRVT8AnkZXpH8H8NyqWtf5DbbSCZijFpH5aP3mIR/9MnDPqnpp65tdCvwT7fWpqnOq6qzWvsuAf+S2/baXV9V1VfVN4Mw+YtoaJ2B+0mSYI2/xE+D+SXauqpuq6qyeZU/ilsPgfkJXULp/Vf205ZMb+mj7rDqBPvNTVf1ZVe1TVfvQFQLfO+xgLCzNl0Oqasc2HQJQVV8HbqL70n0s8AHgiiQP5NYJaFfg8p7n+o9+N5pkO+ApdB0OquozwDfpjqsftl2XxfYfwHbA3VeJbdck70nyrSQ3AG+m2wu2MKrqE8A1vfOS3C/JR5Kck+STSX5+aXXgbu32DsC4z9Ol0Rp3jviTtq3dquqwqvoOQJJHtr2630lyPd0epaX/yz3oRtes5kl0//Nv6pl3T+DOwDlLnTDgI23+1roP8Miezt11dB2p/9azTu+Psx8AS+eTu9Vr1vbS9VOwWe35tuRtwNPpRhe8Y/nCJIe3vVRLbXgQPbmwqj5Lt+cz3HpP4MiZoxaW+Wj95iEf3YfuMJfeNrwI2ACQ5AFJPpDuqr030P3gXN5vGySmgZifNEHmyFscSTfC6avpDm/7zZ5lvedXejvwUeDkdIcA/l2S2/fR9pm0zvzU66l0BcKhsrC0GD5OtzfrDlX1rXb/cLphzUtV5SvpksOSe6/j+X+H7ov0Da0jcBXdkMZRHA53BV2nZMm96YY4XkP3hb7c/6Ebwvmg6oZP/iHdD6dFdxzdqISH0w2VXdqrdizwtHRXY/gQ8NzJhKcxG3WOWO6dwGnAHlW1A12HY+n/8nK6Yc6r+Se6DsiH0h0WAvBdujzwiz2dsB3qlgsGbM0hXZcDH+953h2rG5r9zD4eeyXdIcLAz/bS7d6zfJiHmv0zXQfu0qq6VQcyyX3oXrfnAPeo7jCXL9OTC5M8G7gjXY79iyHGNShz1OIyH61u5vMRXRu+sawN21fV0mFjbwS+CuzV+m0vYvr6beYnTdLC5ciquriqnkp3CPArgFOT3CXJfwN2Ab7Q1vtJVb2kqvamO9T3Nxnd6Vmm1Wr5CfhZn/C+wMeGvWELS4vh43Q/KD7R7m+i+7L7VFX9tM07BXh6kr2T3Bl48Tqe/wjgeLpzGO3TpscA+yR58NaHfyvvAv483XHs2wN/A7yz7Xm7mu7k3b3Jc3u6yv4Nbf7zhxzPzElyV7pk+54k59INM18618pTgROqane6PQBvb8NtNd9GnSOW2x64pqp+mOQR3Hp040nAryf53STbJrlHkuWHOTyH7lCLDyTZrqr+i67z8uq0Q3DTndRy6bwb3wbukWSHNeLaJt2JH5emO9DtDXxAkt9Pcvs2/XK6c4es5YPAg5MckmRb4NncemTBt4Hd03Ni2kG1oe5PoCueL3cXuo7a0t7HZ9BOytnuP4Aulz4N+H3gL1Z4zcfGHLXwzEedec1Hn6Xrk70g3cl6t0nyoCS/3JZvD9wA3NT2tPdTNBsb85OmwMLlyCRPS3LP9tilcwP9lO7/7CPtdyBJfi3Jg5NsQ5dHftLWWwhr5KclhwKn9nxWhsZktxg+TpcUlhLQp+iGIC7dp6o+THf+oY/RnZyxrypmkqUTL76mqq7qmc6hq1AfseVnWLc30h0T+mm6oZfXcMsxwNfSHWu8NLRyH+B/A78KXA/8C91etEV3O+C6peNs27TUKT2SdhhMO6TxTizYoYMLamQ5YhXPAl6a5Ea6/9GfHXpV3TkzDqI7ofQ1dHvfHtL74NaBOIpuT9n7k9wJeEGL66x0h0/8G93J+qmqr9IVpS9tuWG1qzAdQ7cXbWn6WHXH/u9P90V8Bd0hGK+gG92zRdWdSPIpdHnpe8DewOeBH7VVPgZcAFyV5LtrPV8f2/t8Gya/fP5XgFcCn6HrsD0Y+HeA9gPzHcArqupLVXUx3QiBtydZs40jYo5abOajzrzmo5/SXTl4H+AbdKMX3kx36Bh0e9h/D7iR7sfnu7c2liEzP2nSFjFHHgBckOQmuhN5H1pVP+TWh8FBVyw/la6odCHda3Wb0wPMsS3lpyWHMoLD4ABS47noi6QJSrIn8IGqWrp06KeBV1fVe9pw+F+qqi8l+TDw7qo6oe0BPQPYrUwU0lZre643A4dV1ZmTjmeamKOk8TIf9c/8JE2ftmPsKuB+VXX9pOOZlH7zU1v2QLpzUN13FHnJEUvSnEvyLrqRCg9MsjnJkXQn+zwyyZfo9lAe3FY/GvijNv9dwNPtEEmDS/LEJDu20T9L5wo5a42HLRRzlDQe5qP1Mz9JU+vuwP9a8KLSevITdIfrnjyqvOSIJUmSRiTJsXTnPrgD8BW6q66cPdGgJC0k85EkaVQsLEmSJEmSJGkgHgonSZIkSZKkgWw76QC2ZOedd64999yzr3W///3vc5e73GW0Ac1QHDA9sUxLHDA9sUxLHNB/LOecc853q+qeYwhpJsxifuqX8Y7erMU87fGan25tlvLTpLdvDNOx/XmOwfx0W/3mqGn4TAzTPLVnntoCi9ueoeanqpra6eEPf3j168wzz+x73VGaljiqpieWaYmjanpimZY4qvqPBfh8TUFemJZpFvNTv4x39GYt5mmP1/w0u/lp0ts3hunY/jzHYH4aPEdNw2dimOapPfPUlqrFbc8w85OHwkmSJEmSJGkgFpYkSZIkSZI0EAtLkiRJkiRJGshUn7x7Pc7/1vU8/ZgPDu35Lnv5k4b2XJIWm/lJ0rQadn4Cc5Sk4TA/SbPDEUuSJEmSJEkaiIUlSZIkSZIkDcTCkiRJkiRJkgbSV2EpyY5JTk3y1SQXJnl0krsnOT3Jxe3vTm3dJHltkkuSnJfkYT3Pc0Rb/+IkR4yqUZIkSZIkSRq9fkcs/T3wkar6eeAhwIXAMcAZVbUXcEa7D3AgsFebjgLeCJDk7sCLgUcCjwBevFSMkiRJkiRJ0uxZs7CU5G7A44C3AFTVj6vqOuBg4MS22onAIe32wcDbqnMWsGOSXYAnAqdX1TVVdS1wOnDAUFsjSZIkSZKksdm2j3V+DvgO8NYkDwHOAZ4HbKiqKwGq6sok92rr7wZc3vP4zW3eavNvJclRdCOd2LBhA5s2beqrIRu2g6MffHNf6/aj3+0ud9NNNw382GGbllimJQ6YnlimJQ6YrlgkSZIkSbOln8LStsDDgOdW1dlJ/p5bDntbSVaYV1uYf+sZVccBxwHsu+++tXHjxj5ChNed9H5eeX4/zenPZYf1t93lNm3aRL8xj9q0xDItccD0xDItccB0xSJJkiRJmi39nGNpM7C5qs5u90+lKzR9ux3iRvt7dc/6e/Q8fnfgii3MlyRJkiRJ0gxas7BUVVcBlyd5YJu1H/D/s3fv4ZJU5aH/v68giAgCohMcCIOKF4yXkAmQaDwTCRdRM+RElAR1MBiSiLef5AgYjxJvwSRqUBMNCjoQZEA0YWJQnKgbj1HuIIgEGXF0hrsOt9Eoou/vj7Wa6dmze+/ePX2p3vv7eZ5+dld1ddVbvbveXrVqrVXfBlYCrTu7LQMuqM9XAq+sd4c7ALi3dpm7CDg4Inaug3YfXOdJkiRJkiRpDHV7V7jXAWdHxLXAs4H3AKcAB0XETcBBdRrgQuBmYDXwMeA1AJm5HngncHl9vKPOkyRJkiQNSUTsFBHnR8R/R8QNEfFbEbFLRKyKiJvq353rshERH4yI1RFxbUTs27aeZXX5myJN2i9RAAAgAElEQVRiWectSprLuhqUKDOvARZP8dKBUyybwHEd1nMGcMZsApQkSZIk9dWpwBcy8yURsQ3wSOAtwJcy85SIOJEyru4JwAuAvetjf+AjwP4RsQvwdsp5YgJXRsTKegdwSfNIty2WJKmRvOImqanMT5KaKCJ2BJ4HnA6QmQ9k5j3AUmB5XWw5cHh9vhQ4M4tLgJ3qGLuHAKsyc32tTFoFHDrEXZHUEP27jZokjYZX3CQ1lflJUhM9AbgL+EREPAu4EngDsKCOjUtm3hYRj6vLLwTWtr1/XZ3Xaf5mIuJY4FiABQsWMDExMWOQC7aD45/xYPd71YVutjsoGzZsGOn2+2ku7Qu4P/1gxZKksdV2xe1oKFfcgAciYimwpC62HJignLg9dMUNuKS2JtitLruqNe5bRLSuuJ0zrH2RNLeYnyQ12NaUu3y/LjMvjYhTKZXcncQU83Ka+ZvPzDwNOA1g8eLFuWTJkhmD/NDZF/C+6/p7urrmqJm3OygTExN0s9/jYC7tC7g//WBXOEnjrP2K29UR8fGI2J5JV9yAvl1xk6QumZ8kNdU6YF1mXlqnz6dUNN1RK7Spf+9sW36PtvfvDtw6zXxJ84wtliSNs6FeceulGTf0vyn3oJu2jltz4HGLF8Yv5nGLtyHmZX6C2eWoJny3jGH02zeG4crM2yNibUQ8JTNvpNyQ6dv1sYxyt+9lwAX1LSuB10bECkpX3XtrV7mLgPe0xooDDgZOGua+SGoGK5YkjbOprridSL3iVgs93V5xWzJp/sTkjfXSjBv635R70M24x6058LjFC+MX87jF2xDzMj/B7HJUE75bxjD67RvDSLwOOLuO/3Yz8CpKb5bzIuIY4AfAEXXZC4HDgNXAT+qyZOb6iHgncHld7h2tbruS5he7wkkaW5l5O7A2Ip5SZ7WuuK2kXGmDza+4vbLefekA6hU34CLg4IjYuV51O7jOk6SemJ8kNVlmXpOZizPzmZl5eGbenZk/yswDM3Pv+nd9XTYz87jMfGJmPiMzr2hbzxmZ+aT6+MTo9kjSKNliSdK484qbpKYyP0mSpDnPiiVJYy0zr6HchnuyA6dYNoHjOqznDOCM/kYnaT4zP0mSpPnArnCSJEmSJEnqiRVLkiRJkiRJ6okVS5IkSZIkSeqJFUuSJEmSJEnqiRVLkiRJkiRJ6okVS5IkSZIkSeqJFUuSJEmSJEnqiRVLkiRJkiRJ6okVS5IkSZIkSeqJFUuSJEmSJEnqiRVLkiRJkiRJ6okVS5IkSZIkSeqJFUuSJEmSJEnqSdcVSxGxVURcHRGfq9N7RcSlEXFTRJwbEdvU+dvW6dX19UVt6zipzr8xIg7p985IkiRJkiRpeGbTYukNwA1t0+8FPpCZewN3A8fU+ccAd2fmk4AP1OWIiH2AI4GnA4cC/xQRW21Z+JIkSZIkSRqVriqWImJ34IXAx+t0AM8Hzq+LLAcOr8+X1mnq6wfW5ZcCKzLzZ5n5PWA1sF8/dkKSJEmSJEnDt3WXy/0D8GZghzr9GOCezHywTq8DFtbnC4G1AJn5YETcW5dfCFzSts729zwkIo4FjgVYsGABExMTXQW4YDs4/hkPzrxgl7rd7mQbNmzo+b391pRYmhIHNCeWpsQBzYpFkiRJkjReZqxYiogXAXdm5pURsaQ1e4pFc4bXpnvPxhmZpwGnASxevDiXLFkyeZEpfejsC3jfdd3Wk81szVHdbXeyiYkJuo150JoSS1PigObE0pQ4oFmxSJIkaTjqsCRXALdk5osiYi9gBbALcBXwisx8ICK2Bc4EfgP4EfCyzFxT13ESZSiUXwCvz8yLhr8nkkatm65wzwF+PyLWUBLN8yktmHaKiFZNzu7ArfX5OmAPgPr6o4H17fOneI8kSZIkaXgcQ1dSX8xYsZSZJ2Xm7pm5iJI4vpyZRwFfAV5SF1sGXFCfr6zT1Ne/nJlZ5x9Z7xq3F7A3cFnf9kTSvOVdKyU1lflJUhM5hq6kfprNXeEmOwF4U0SspoyhdHqdfzrwmDr/TcCJAJl5PXAe8G3gC8BxmfmLLdi+JLV4xU1SU5mfJDVRawzdX9bprsfQBdrH0F3bts4px9CVNPfNalCizJwAJurzm5miRjozfwoc0eH97wbePdsgJamTtitu76ZUdreuuP1xXWQ5cDLwEcqVtZPr/POBD0++4gZ8r1aM7wd8Y0i7IWkOMj9JaqJhj6FbtznrGzT1++ZM0PsNmvphLt0wZy7tC7g//dC/0a4laTSGdtdKSZqleXdXXZjdiVsTCvPGMPrtG8PQtcbQPQx4BLAjbWPo1hw11Ri663odQ7eXGzT1++ZM0PsNmvphLt0wZy7tC7g//WDFkqSxNewrbk05cRt0oXfcCtbjFi+MX8zjFm8TzNe76sLsTtyaUJg3htFv3xiGKzNPAk4CqPnpLzPzqIj4NGWM3BVMPYbuN2gbQzciVgKfioj3A4/HMXSlecuKJUnjbKhX3Jpy4jboq23jVrAet3hh/GIet3gbYugtAiRpC50ArIiIdwFXs+kYumfVrrjrKeO+kZnXR0RrDN0HcQxdad7aksG7JWmkvGulpKYyP0kaB5k5kZkvqs9vzsz9MvNJmXlEHduNzPxpnX5Sff3mtve/OzOfmJlPyczPj2o/JI2WLZYkzUVecZPUVOYnSZI0p1ixJGlO8K6VkprK/CRJkuYyu8JJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJzNWLEXEHhHxlYi4ISKuj4g31Pm7RMSqiLip/t25zo+I+GBErI6IayNi37Z1LavL3xQRywa3W5IkSZIkSRq0blosPQgcn5lPAw4AjouIfYATgS9l5t7Al+o0wAuAvevjWOAjUCqigLcD+wP7AW9vVUZJkiRJkiRp/MxYsZSZt2XmVfX5/cANwEJgKbC8LrYcOLw+XwqcmcUlwE4RsRtwCLAqM9dn5t3AKuDQvu6NJEmSJKkje6RI6retZ7NwRCwCfh24FFiQmbdBqXyKiMfVxRYCa9vetq7O6zR/8jaOpbR0YsGCBUxMTHQV24Lt4PhnPNj9zsyg2+1OtmHDhp7f229NiaUpcUBzYmlKHNCsWGYrIvYAzgR+BfglcFpmnlpbSJ4LLALWAC/NzLsjIoBTgcOAnwBHtyrOa2HorXXV78rM5UhSj8xPkhqs1SPlqojYAbgyIlYBR1N6pJwSESdSeqScwKY9Uvan9EjZv61HymIg63pW1kYEkuaRriuWIuJRwGeAN2bmfaX8M/WiU8zLaeZvOiPzNOA0gMWLF+eSJUu6iu9DZ1/A+66bVT3ZtNYc1d12J5uYmKDbmAetKbE0JQ5oTixNiQOaFUsPLBhJairzk6RGqo0DWg0E7o+I9h4pS+piy4EJSn56qEcKcElEtHqkLKH2SAGoOe5Q4Jyh7YykRuiqJiYiHk6pVDo7Mz9bZ98REbvV1kq7AXfW+euAPdrevjtwa52/ZNL8id5DlzTfWTCS1FTmJ0njYBg9Uup2Zt0rpd89UqD3Xin9MM69BCabS/sC7k8/zFixVJtmnw7ckJnvb3tpJbAMOKX+vaBt/msjYgXlitu9NTFdBLynbcDug4GT+rMbkuY7u+r2z7j9uI5bvDB+MY9bvE0zn/ITzC5HNeG7ZQyj374xjMaweqRAb71S+t0jBXrvldIPY95LYBNzaV/A/emHbo7U5wCvAK6LiGvqvLdQKpTOi4hjgB8AR9TXLqSMD7CaMkbAqwAyc31EvBO4vC73jtbVN0naEnbV7a9x+3Edt3hh/GIet3ibZL7lJ5hdjmrCd8sYRr99Yxg+e6RI6qcZSxKZ+TWmLtQAHDjF8gkc12FdZwBnzCZASZqOBSNJTWV+ktRE9kiR1G8PG3UAktSrLgpGsHnB6JX1trkHUAtGwEXAwRGxcy0cHVznSVJPzE+SGqzVI+X5EXFNfRxGqVA6KCJuAg6q01B6pNxM6ZHyMeA1UHqkAK0eKZdjjxRp3upv22dJGi676kpqKvOTpEayR4qkfrNiSdLYsmAkqanMT5Ikab6wK5wkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSerL1qAOQ1L1FJ/5H39f5yUO37/s6JUmSJEnzgy2WJEmSJEmS1JOht1iKiEOBU4GtgI9n5inDjkHDdd0t93J0n1varDnlhX1dnwTmJ0nNZX6S1FTmJ0lDrViKiK2AfwQOAtYBl0fEysz89jDjGJVBdGOygkXqj/menyQ1l/lJUlOZn/rPc0aNo2G3WNoPWJ2ZNwNExApgKdC4xNPrAX38Mx7se+uc6UwXZy+xmHQ0j835/DQdj32p0cYmP8HsclQ3ZRXzk9RoY5Wf+q3f+U7zz1wZQ3fYFUsLgbVt0+uA/dsXiIhjgWPr5IaIuLHLde8K/HCLI9xCr29IHNBbLPHegYTS989kC+Jsyv+nKXHwu+/tOpY9Bx3LiM35/DSdScdU4+OdZNzihfGLuenxmp/GND91U1YZUNmkXRO+36OOYdTbn8sxzPv8BD3nqCadQ2yxUZ4nDmC/m3C89tOc2p9RnN8Nu2IpppiXm0xkngacNusVR1yRmYt7DaxfmhIHNCeWpsQBzYmlKXFAs2IZsTmfn7plvIM3bjGPW7xz0JzNT6PevjE0Y/vGMNZmzE/QW46aa/+PubQ/c2lfwP3ph2HfFW4dsEfb9O7ArUOOQZKmYn6S1FTmJ0lNZX6SNPSKpcuBvSNir4jYBjgSWDnkGCRpKuYnSU1lfpLUVOYnScPtCpeZD0bEa4GLKLejPCMzr+/T6mfd/HtAmhIHNCeWpsQBzYmlKXFAs2IZmXmSn7plvIM3bjGPW7xzyhzPT6PePhhDE7YPxjCW5nh+6re5tD9zaV/A/dlikblZF1hJkiRJkiRpRsPuCidJkiRJkqQ5woolSZIkSZIk9WSsKpYi4tCIuDEiVkfEiVO8vm1EnFtfvzQiFo0wljdFxLcj4tqI+FJE7DmKONqWe0lEZEQM7LaD3cQSES+tn8v1EfGpUcQREb8aEV+JiKvr/+ewAcVxRkTcGRHf6vB6RMQHa5zXRsS+g4ijy1iOqjFcGxFfj4hnDSqW+aTb43NIsayJiOsi4pqIuKLO2yUiVkXETfXvznV+x+9mRCyry98UEcv6HONm39N+xhgRv1E/g9X1vVPdInlL4z05Im6pn/M17fklIk6q274xIg5pmz/l9yTKQKiX1v04N8qgqFsS7x41991Qc/Ab6vzGfsYanGHmp37ln1luc6D5pMft9y0/dBnDwI/5Hrc/tM8hIh4REZdFxDdrDH9d5+8VU+TXmOZcolNs6r9h5qdu9SuPdTqWosPvZ6dt9BD/SMpYvWyjx30ZePlrUPkhRlg+6+v/JzPH4kEZDO67wBOAbYBvAvtMWuY1wEfr8yOBc0cYy+8Cj6zP/2IQsXQTR11uB+CrwCXA4hF+JnsDVwM71+nHjSiO04C/qM/3AdYM6DN5HrAv8K0Orx8GfB4I4ADg0kHE0WUsv932f3nBIGOZL49uj88hxrMG2HXSvL8FTqzPTwTeW59P+d0EdgFurn93rs937mOMm31P+xkjcBnwW/U9nwdeMIB4Twb+copl96nfgW2Bvep3Y6vpvifAecCR9flHW3lrC+LdDdi3Pt8B+E6Nq7GfsY/BPIadn/qRf3rY5kDzSY/b71t+6DKGgR/zPW5/aJ9D3ZdH1ecPBy6t+zZlfqXDuUSn2IZ53M6Xx5Z+7wcY1xoGWI6iw+9np230EP9Iyliz3cYW7MvJDLj8Naj8wAjLZ/38/4xTi6X9gNWZeXNmPgCsAJZOWmYpsLw+Px84sFUbN+xYMvMrmfmTOnkJsPso4qjeSfnS/HQAMcwmlj8F/jEz7wbIzDtHFEcCO9bnjwZuHUAcZOZXgfXTLLIUODOLS4CdImK3UcSSmV9v/V8Y3Pd1vun2+Byl9py5HDi8bf5U381DgFWZub5+X1YBh/YrmA7f077EWF/bMTO/keUX88y2dfUz3k6WAisy82eZ+T1gNeU7MuX3pP52PZ/yWzZ533uN97bMvKo+vx+4AVhIgz9jDUwT8tNsv3ezMsh8sgXb72RW+aHLdQ78mN+C7Q/tc6j7sqFOPrw+ks75tdO5RKfY1H9NyE/dGsbvZ6dtzMoIy1h9z/UjLH8NJD+MuHzWt//POFUsLQTWtk2vY/Mfp4eWycwHgXuBx4wolnbHUGr8hh5HRPw6sEdmfm4A259VLMCTgSdHxH9FxCUR0bcT0lnGcTLw8ohYB1wIvG4AcXRjtt+jYRnU93W+adr/N4EvRsSVEXFsnbcgM2+D8qMGPK7O7xT7KPapXzEurM8nzx+E19Zmw2fExibrs433McA99bes7/HW5tu/Trl6P46fsbbMsI/lfuSffmhCzutHfpi1AR3zvW4fhvg5RMRWEXENcCflROu7dM6vnc4lmvabPpc19bMeZDlqut/PTtvoh1HG39T8Ol35a+D5YQTls779f8apYmmqlkfZwzLDiqUsGPFyYDHwd8OOIyIeBnwAOH4A255VLNXWlO5wS4A/Aj4eETuNII4/Aj6ZmbtTmvmdVT+rYRvW97VrEfG7lIqlE0YZxxzRtP/vczJzX0pXx+Mi4nnTLNsp9ibt02xjHFbsHwGeCDwbuA14X53fmHgj4lHAZ4A3ZuZ90y06y9ia9P3Q9Ib9v+pH/hmkYX2n+5UfZmWAx3yv2x/q55CZv8jMZ1NaY+8HPG2a9ZnfRq+pn/Ugy1FN2+dhxN/U/DpdXOOaq4fy/xmniqV1wB5t07uzeRemh5aJiK0p3Zy6bSbX71iIiN8D/gr4/cz82Qji2AH4NWAiItZQ+keujMEM4N3t/+eCzPx5bSZ4I6WiadhxHEPpO0tmfgN4BLBrn+PoRlffo2GJiGcCHweWZuaPRhXHHNKo/29m3lr/3gn8K6VwfUerWWv92+qe2in2UexTv2Jcx6ZdPAcSe2beUU9ifgl8jI1NoWcb7w8pzY637me8EfFwSqHl7Mz8bJ09Vp+x+mKox3Kf8k8/jDTn9TE/dG3Ax3xP2x/F51C3ew8wQSkPd8qvnc4lGvWbPsc18rMecDlqut/PTtvoh1HG39T8Ol35a2D5YYTls779f8apYulyYO8oo7RvQxkwa+WkZVYCy+rzlwBfrv0Ihx5L7YL2z5RKpUGMJTRjHJl5b2bumpmLMnMRZeyc38/MK4YdS/VvlEHNiYhdKV3jbh5BHD8ADqxxPI1SsXRXn+PoxkrglVEcANzbaoo4bBHxq8BngVdk5ndGEcMc1M13cSgiYvuI2KH1HDgY+Bab5sxlwAX1eafv5kXAwRGxc21ifHCdN0h9ibG+dn9EHBARAbyybV19M6n/+R9QPudWvEdGuaPIXpRK9cvo8D2pv11fofyWTd73XmML4HTghsx8f9tLY/UZqy+Glp/6mH/6YaQ5r1/5YRbbG+gx3+v2h/k5RMRjo7aOj4jtgN+jjF/SKb92OpfoFJv6rzHlp5ZBl6Nm+P3stI1+GGX8fc31Qyp/DSQ/jLh81r//T454hP3ZPCjdlr5D6Rv9V3XeOyiVJVAqCD5NGTDrMuAJI4zlP4E7gGvqY+Uo4pi07AQDuitcl59JAO8Hvg1cRx1tfwRx7AP8F2X0/muAgwcUxzmUppg/p9T6HgP8OfDnbZ/HP9Y4rxvw/2amWD4O3N32fb1iULHMp8dU38URxfGE+n3/JnB923HxGOBLwE317y51fsfvJvAnNceuBl7V5zin+p72LUZKt+Rv1fd8GIgBxHtWjedayo/ybm3L/1Xd9o203S2t0/ek/t8uq/vxaWDbLYz3uZRmzNe2HeuHNfkz9jG4x7DyUz/zzyy3O9B80uP2+5Yfuoxh4Md8j9sf2ucAPJNyR+Jra256W9v3crP8yjTnEp1i89H/x5Z87wcUz8DLUXT4/ey0jR72YSRlrF620eO+DLz8Naj8wAjLZ/38/7RWKG2xiEhg78xcPepYBmU+7KPUbxExAfxLZn581LEMynzYR2mumem4jYg/AD5IuW3z71C6v7w6M/9zaEFuoYj4JLAuM9866lik+SIiHgH8D+UGSutmWn5UIuIS4MOZ+S99WNcSSqubBcBLMvMLW7rOGbY39M84IlYA38rMdw1je+NmnLrCzVkRsSYiHojSPax9/jURkVFGh+/n9hbV9W6ojzURcWI/tzHLeD4ZEdMeoDXeJw0rprrNiYh49TC3KY1azQf/U3PDHRHxiSiDCY4ilqMj4mszLDP04zQiTo6IngthEbGk5rTPTpr/rDp/YouDlOaJiHhuRHw9Iu6NiPVR7jz7m31a/d8Dr83MR2Xm1X1aZ0c1t/y8rXy2ISLePMDtHR0Rv2jb1s0R8RezeP/Qy2ZSryYdV79sK+tsiIijZnjvoRHRt4vKUe6O/fJ+ra/LbZ4SEYO8+PVu4G9rvtysUikibo+In0z6Pwzi7u2t7V0SET+t27krIj4dEf28o95sYunr96eprFhqju9R7lYGQEQ8A9huwNvcKTMfVbf7tog4dMDbkzQeXlxzw77AbwJe6e6/u4DfnlSoWkZpki2pCxGxI/A54EPALpRbIf810K8bpuxJ6fYyTOfWE7PW428HvL1vtLZFGTPkb6OMEyrNKe3HFWW81Re3zTt71PHNAd3ky4Mn5bdB3yzo1fX//TTgV4BB59N5zYql5jiLMpBWyzLgzPYFIuKFEXF1RNwXEWsj4uS2115WrzTtWKdfUGuGHzvThrPcGe16yh3kiIhT6/rvi4grI+J32razVUS8JSK+GxH319f3mLzOegVxbZTb1xMRT42IVfVq4o0R8dI6/1jgKODNtUb537v9wNq29ScRcUNE3B0RF0XEnm2vZUT8eUTcVF//x4iItn15X0T8MCK+FxGvrctvHRHvpjR7/3CN68Ntm/y9qdYnzTWZeQvweTbmhlfVY+3+mm/+rH35iFgapaXlfTVHbFZZHRG7RcS1EfGXdfrREXF6RNwWEbdExLvqsfk04KPAb9Vj8J7Zxh9lkMKvR8Q9EfHNKM20W69NRMQ7o7RuuD8ivhhtrUYj4pUR8f2I+FFE/N8oLbl+r+7TW4CX1bi+2bbJPTutbwoPUG5ocGTd3lbAS4FNCredcmd9bbrfhFbL1GUR8YOa5/5qtp+h1HBPBsjMc7LcDeh/MvOLmXktbN66sO242LptHU+MiMuitHi6ICJ2iTIA6wZgK+CbEfHdyRuuy/xDRNxaH/8QEdvW1y6OiD+sz59bt3lYnf69iLhmNjsZEb8bEde1Tf9nRFzWNv21iDi8Pv/1iLiq5qFzKWOCdCUzr6IMbP20uq7/iIjXTYrl2og4PCK+Wmd9s+bCl9XXX1R/B+6p+feZbe89oeb5+2s+O3A2n4M0SBGxXS3X3xYR6yLi7yLi4VEuAP0r8IRoa2kTEc+JiEtr7rg1Ij4wKbf0Gsfv1PXeU4/l57S9dklEvL3+vS8iLowySHPr9VfX3/y7IuLNUc4Fn1vzw5uAZTX+9oGln9hpfVPEdlyU8t2PIuKzEbGgzl8HPB74Ys2ds9nfcyPiuPr8STVf/kmd/rWIuL1t2b+K0pp+HdB1i6/M/CGlzNUqz/5BlHLhfVHKem+ZFNOS+pncWz/PP54i7kfX3Pt3dXq7+juwtn7uH6q/E9N9f1pluNsj4m9m87k1Ui+Df/no7wNYQ7lLxY2UH/OtgLWUmt8EFtXllgDPoFQIPpMyOPjhbes5G/gkZRCuW4EXddjeorrerSkDcz0H+AlwYH395XUdWwPHA7cDj6iv/R/KAF5Pqe99FvCY+loCTwIOqfHvV+dvX6dfVde5L+VWjk+vr38SeNcMn1ECT5pi/uGUwcmeVtf9VuDrk973OWAn4FcprQQOra/9OWUg8d0p4yf8Z+tzqa9PUGq6J8cx5fp8+JgLj1Y+qs/3oFQ6v7NOvxB4Yj32/1fNG/vW1/YD7gUOqjlqIfDU+toE8Oqae74DHNu2vX+j3EFze+BxlMEQ/6y+djTwtRni3ew4rfMXAj+iDH74sBrXj4DHtr3vu5QT0+3q9Cn1tX2ADZTBFLehdIf5edvncjJlXJbJcUy5viliW0IZbPK3gUvrvMMod/N4NTBR582UO5fQ4TeBjXn+YzWeZ1FacTxt1N8xHz769QB2rMf1cuAFwM6TXt/kWG07Ltp/52+hnGxsT7nVc/vym5Q92DQ/voNyt93HAY8Fvs7GXPkO4EP1+Vtqbnhv22undtifzXJLnd8aS2TXmgtup5TzdqjH9/9Qym3bAN8H/j/g4ZQWSD+nQxmLSTmW0kL1HuDJdfqlrRxVp59VP+9tOnw++1JuVb0/pSy7rH5m21LKjWuBx7f9L5446u+Qj/n5aD+W2+b9LfD/6nG2gHLXsNYg3YcCqyctv189ZrailI1Ws/GmOI+ox8fuHbZ/CfDyKeYvqsfY71F+2w+jnGvs3Pa+G+v2tq955+T62rOB+4AD6jH3QeBB4Ln19VOAj08Rx5TrmyK2w2rueWbdv9OAVW2v397aVof3T/k68Brg0/X5n1Dy5fK2186tzw+n5OunAo+i5OuuPmNKnv4a8LE6fSDw9PoZ7wusZ+P54ZMoZcA/pEz0WaQAACAASURBVOTbxwLPqq+toJxrPo5yU4C3tm3vo8D5lHPER1PKdG+f5vtzNXBEfb4DsP+oj4stfdhiqVlarZYOAv6bcvA8JDMnMvO6zPxllqtx51BO7lqOA55PKSj9e2Z+bobt/ZByIH0cODEzv1S38y+Z+aPMfDAz38fGAgGUk563ZuaNWXwzN23GeAQl0RyWma3a8BcBazLzE3WdV1GSwUvYcn8G/E1m3pCZDwLvAZ4dba2WKCd392TmDyi3j3x2nf9SSuFuXWbeTUm43ei0vkaKiDMi4s6I+FYXy/5qRHyl1qBf27rCqnnn36K0EPoacDHluCIz/yMzv1uP/YuBL1Ja9kG5I8cZmbmq5qhbMvO/29a5DyU3vT0zTwOoV7peALwxM3+cmXcCH6C24tlCLwcuzMwLazyrgCsoBaOWT2TmdzLzf4Dz2Hgsv4SSQ7+WmQ8Ab6MUXmbSaX1TysyvA7tExFMouf/MSYtMmzu7+E0A+OssrThad7N5Vhf7IY2FzLyPjXfT+RhwV0SsbF1F79JZmfmtzPwx8H+Bl0ZpQTiTo4B3ZOadmXkXpQveK+prF7PxWHwe8Ddt0/+rvt7JS2tLhdbj8Zn5U0r+eh7lzj7XUvLzcygnkTfVstgBlAqlf8jMn2fm+ZST4+kcULezgVKxfxbl7kBQbju9d0TsXadfQTnJe6DDuv4U+OfMvDRLC7LllArtA4BfUMqT+0TEwzNzTWZu1hJMGqGjKGWUH2bmHcC72HhMbyYzL8vMy+t3/buU86nJv8GztQz4bGb+Z/1tv5ByEfzgtmU+VstiP6ZUZLSf13wmMy/JzJ9RKkC6OdfvtL7JjgJOy8xra056M3BgRPzKLPbv8225bUWddzElt1H/ntI23Z4vX1pj/e/M3EDJuTP551qevYpSYXUCQGZ+KTOvr5/xVZQyW+t/9wpKGfAztex1Vy1DtexRY/pE1kG8a0u1PwHeUM8R7637MV159ufAkyPiMZl5f2Ze2sX+NJoVS81yFvDHlCtIk08wiIj960n/XRFxL6XFzUNdLTLzHsotEH8NeF8X29s1M3fOzKdl5gfbtnN8lO4u99aD8dFt29mDcmB28kbgvMy8rm3ensD+7QUlSnKaTSLqZE/g1Lb1rqe0pljYtsztbc9/QqnlhtJkc23ba+3Pp9NpfU31SUpNeTfeSvn//TolGf7ToIJSox2emTtl5p6Z+ZpaUdLqYntJlG5Z91AqabrNDUdRKsvPb5u3J+Uk6La2Y/ifKVeCttSewBGT8s5zgd3alukqN2TmTyhXEGfSS244C3gt8LuUptKT96Fj7pzpN2ELYpLGRr2wdHRm7k4p/zwe+IdZrKL9t//7lJw0XTfWlsfX5dvf+/j6/BuUE4YFlJO0M4E9onSP3Q/4Kp2dV/Nv63FrnX8xpZXi8+rzCcqJUPuJ1+OBWzKzvSK8PcapXFK38yhKbnk6Gy8m/IxywvXyiHgYZUzOs6ZZ157A8ZNy1h6UVkqrKWXEk4E7I2JFRDx+mnVJQxMRQfn+Tz6mF079DoiIfSLi87Vr1n2Ui1Dd5I7p7Ek53tqPocVszC3QfdnlPkpL8pl0W07YJOfV8877mOYzmsIL2nJbq9Ll28DDImIfSjntX4H7ayOBVr5rbX9yvp7Jn9Vt7Z6ZyzJzPUDthnZxW/npaLovzx5e/57eNu/xlN+O69v+b//G9OXZZZTWX9+J0vXxkC72p9GsWGqQzPw+ZRDvw4DPTrHIp4CVlNsqPprS5O6h8X0i4tmU2tJzKM0fZy3KeEonUGqFd87MnShJqbWdtZTmkp0cARweEW9sm7cWuHhSQelRmdm680g3LQE6WcvGpNF6bFdbAszkNko3uJbJY0VtSVyNkZlfpVS4PSQinhgRX4gyRtb/i4inthandC2AUqF4KxJlPBFKa5m/BxbU3HAh3eeGkymtJD/V1hpgLeVq9q5tx++Omfn0+vqW5oazJuWG7TOzm5aJm+SGiNiO0s2kpZ+54SxKU+8LawVWu5ly57S/CdJ8k6WV5Cep42gAPwYe2bbIVBe02n/7f5VyFfmHXWzuVspJYPt7b61x/AS4EngD5dbUD1C6mLwJ+G6W8T5ma3LFUqtVVHvF0m3AwnqS3B5XV2orjc8AL26bvZxSoX0g8JMs43J2shZ496Sc9cjMPKeu/1OZ+Vw2DvXw3m5jkwapVsbezubHdKv3yFS/+x+jtIR5YmbuSOnmuqW/wWsp3dUml10+0MV7J5dddqSU5Vu2tOyySc6LiEdTzhlu6fiOLtTP/quUPPPTmh8vpvRK2ZpS8QRl/ybn616dB5zLxvLTJ+m+PPthSj7/91o+bMX2IOW70Pq/PTozW2XHzT77elHkZZTKpw8Cn42IbbZgn0bOiqXmOQZ4fm2OONkOwPrM/GlE7Edp3QRARDwC+BdKX/5XUQoWr+lh+ztQDoy7gK0j4m1srGiA0szznRGxdxTPjE3vanQrpfDx+rbtf45y5e4VUQbBe3hE/GaUwXmhjAvyhC5i2yYiHtH22IpyInVSRDy9fg6PjogjutzX84A3RMTCiNiJ2jyyTbdxjaPTgNdl5m8Af8nGlkknU66UrKNUGrxu6rdrHtqG0o3hLuDBiHgBmzbNPh14VUQcGBEPq8fVU9te/zml4nl74KyIeFhm3kbpTve+iNixvu+JEdFqjnwHsHsXP7RbT8oND6fkwxdHxCFRBgN/RJTBGHefYV1QWlW9OCJ+u277r9m0sHgHsKhewd8imfk9yonhVANrz5Q7O/4mSPNBlMHtj28d11FuJvJHlLE1AK4Bnhelm/ejgZOmWM3La8uDR1JODM/PzF90sflzgLdGxGNrS6S3UfJOy8WU1oitSp+JSdOz9XXKsAT7AZdl5vXUVo1sbAH1DUoZ7vVRbkTyv+vyXanluT+g7c5OtSLpl5SW8JNbK00uJ30M+PPamjIiYvsoNxnYISKeEhHPrxcpfkoZF6qbz1kalnOAt0cZWPlxlN/l1jF9B/C4iGhvzbMDcG9mbqjnIX86y+09fFLZZWtKRe4RtSy1VZQBobvtbnYe8Ie1nLANJZ/9su31O4C9JlU8z8Y5wJ9GGVD7EZSK4S9n5u0zvK8bF1POOSbny6+2tcA8D3h1RDy5/h/e1suG6v4/CvhRLT/9NqV82nIm8KIoA3xvVXP8M9teT8r/+hbK0BHbZubPgTMovWh2rflvj4g4qL5ns+9PlJvEPKb+3txb19v+/xo7Viw1TJY+rld0ePk1wDsi4n7KwXRe22t/A6zLzI/UpssvB94VG/vFd+siyl2gvkNpYvhTNm12+P663S9Smj+eThk4sn0ffkCpXDohIl6dmfdTTkCPpFQ83U5JRtvWt5xO6XN/T0T82zSxXU8piLQer8rMf63rWhGlGeq3KGO2dONjdT+upQygdiGlQNYq6JwKvCTK3d96agHWRDWp/Tbw6Sh3pvlnNnYP+iPgk7VLwWHUCoDRRKomqcfx6ynH/92USoyVba9fRqnU/gDlB/JiNr3yR71q/78pV2fOqN+tV1Iqrb5d13s+G7+PX6Yc97dHxHRX+D/CprnhE5m5FlhKqWy/i5LH/g9d/O7VE7bXUQZpvA24nzIgbesW5p+uf38UEVfNtL4utve1tu4u7fNnyp3T/SZI88H9lIqVSyPix5QKpW9RbjxClrHVzqX8zl9Jqayd7CzK1erbKQPSvr7Lbb+LMu7RtZSbmlxV57VcTDnx/GqH6VmpFxyvAq7PjWMcfQP4fpbx6dpz7NGUfPoypm4B3651580NlDvC3cXmF5XOpNwo4F8mzT8ZWF7Lby+t5dc/pVzRv5symPHRddltKWOO/JDyWT+Okp+lpngbpSxyPaVS+r/YeHv6b1LKPN+v3/ddKIPkv7oeO/9IyTWzcQabll0+mpk3UwaN/mvKsfJ9SsvHbsouV1PKOf9KqfS4jVIea5VdVlBacK6PiG56dkxe/+co55srKWWSX2GaMahmaXJ+/Cql8uehfFnP+U6jDLD+35Rz1lmrFVV/Dvx9LT+9mY3lOrKMl9UqP95NyfNPn7SOX1Jy2z3AZ2pF3hspn8sVlM/9C5SBwGHq78+LgBtrDH8DvDTLeMFjKzbthi3NX7UFxkczc88ZFx4zEbEI+Fxm/lqUprE3ZuZuUyx3PeWuCGvr9M3AAa1CqzQf1crYe4C9awsjSZo3IuKVlLt5PnfUsUjqTkTsTBkK4/G1hbg0ULZE0LxVm5ceVpuLLwTezuaD5845WQbz+17ULoO1uWbrTlGt1mbU7jaPoFy9lOaViHhxRDwyIranjCt1HeX2xJI0b9Qugq+htBSQ1GAR8fv1/OZRlF4ml1qppGGxYknzWVCamt5N6Qp3Az32122yiDiH0lz+KRGxLiKOoQyQd0xEfJPS5HdpXfx4Sv/pb1L6Uh+dNmvU/LSU0qT5VmBv4EiPBUnzSZS7FN1FGR/kUyMOR9LMjqB0NV1HuVvbUaMNR/OJXeEkSZIkSZLUE1ssSZIkSZIkqSdWLEmSJEmSJKknW486gOnsuuuuuWjRoq6W/fGPf8z2228/2ID6yHgHb9xibnq8V1555Q8z87GjjqMp5nJ+6ob7ND7m4n5N3ifz06bmS34y9tEw9tkxP22u2xzld200jH00xj0/NbpiadGiRVxxxRVdLTsxMcGSJUsGG1AfGe/gjVvMTY83Ir4/6hiaZC7np264T+NjLu7X5H0yP21qvuQnYx8NY58d89Pmus1RftdGw9hHY9zzk13hJEmSJEmS1BMrliRJkiRJktQTK5YkSZIkSZLUk0aPsTQb191yL0ef+B99W9+aU17Yt3VJmt/MT5Kaqt/5CcxRkvrD/CSND1ssSZIkSZIkqSdWLEmSJEmSJKknVixJGmsRsVNEnB8R/x0RN0TEb0XELhGxKiJuqn93rstGRHwwIlZHxLURsW/bepbV5W+KiGWj2yNJkiRJGh9WLEkad6cCX8jMpwLPAm4ATgS+lJl7A1+q0wAvAPauj2OBjwBExC7A24H9gf2At7cqoyRJkiRJnVmxJGlsRcSOwPOA0wEy84HMvAdYCiyviy0HDq/PlwJnZnEJsFNE7AYcAqzKzPWZeTewCjh0iLsiSZIkSWPJiiVJ4+wJwF3AJyLi6oj4eERsDyzIzNsA6t/H1eUXAmvb3r+uzus0X5IkSZI0ja1HHYAkbYGtgX2B12XmpRFxKhu7vU0lppiX08zf9M0Rx1K60LFgwQImJia6CnLBdnD8Mx7satludLvdQdqwYUMj4uinubhPMDf3ay7ukyRJ0riyYknSOFsHrMvMS+v0+ZSKpTsiYrfMvK12dbuzbfk92t6/O3Brnb9k0vyJyRvLzNOA0wAWL16cS5YsmbzIlD509gW877r+pds1R3W33UGamJig2/0fF3Nxn2Bu7tdc3CdJkqRxZVc4SWMrM28H1kbEU+qsA4FvAyuB1p3dlgEX1OcrgVfWu8MdANxbu8pdBBwcETvXQbsPrvMkSZIkSdPoqmLJ23lLarDXAWdHxLXAs4H3AKcAB0XETcBBdRrgQuBmYDXwMeA1AJm5HngncHl9vKPOkyRJkiRNo9u+Ga3beb8kIrYBHgm8hXI771Mi4kRK95MT2PR23vtTbue9f9vtvBdTxi65MiJW1jswSVJPMvMaSl6Z7MAplk3guA7rOQM4o7/RSZIkSdLcNmOLJW/nLUmSJEmSpKl00xXO23lLkiTNkkMJSGoq85OkfuqmK5y38x6AcbtV8rjFC+MX87jFK0makUMJSGoq85OkvummYsnbeQ/AuN0qedzihfGLedzilSR11jaUwNFQhhIAHoiIpWwsDy2nlIVOoG0oAeCS2ppgt7rsqtYNBSKiNZTAOcPaF0lzi/lJUr/NWBOTmbdHxNqIeEpm3sjG23l/m3Ib71PY/Hber42IFZQa7Xtr5dNFwHtaTSopt/M+qb+7I0mS1AjtQwk8C7gSeAOThhKIiL4MJdCUFt8w+FbfLePc0tfYR2OcY++zoeYn6C1HmZ9Gw9hHY5xjh+7vCte6nfc2lFt1v4oyPtN5EXEM8APgiLrshcBhlNt5/6QuS2auj4jW7bzB23lLkqS5a6hDCTSlxTcMvtV3yzi39DX20Rjn2PtsqPkJestR5qfRMPbRGOfYocuKJW/nLUmSNCtDHUpAkmbB/CSpr7q5K5wkSZJmITNvB9ZGxFPqrNZQAispQwjA5kMJvLLefekA6lACwEXAwRGxcx1O4OA6T5J6Yn6S1G/9bVsoSZKkFocSkNRU5idJfWPFkiRJ0gA4lICkpjI/Seonu8JJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJGnsRsVVEXB0Rn6vTe0XEpRFxU0ScGxHb1Pnb1unV9fVFbes4qc6/MSIOGc2eSJIkSdJ4sWJJ0lzwBuCGtun3Ah/IzL2Bu4Fj6vxjgLsz80nAB+pyRMQ+wJHA04FDgX+KiK2GFLskSZIkjS0rliSNtYjYHXgh8PE6HcDzgfPrIsuBw+vzpXWa+vqBdfmlwIrM/Flmfg9YDew3nD2QJEmSpPFlxZKkcfcPwJuBX9bpxwD3ZOaDdXodsLA+XwisBaiv31uXf2j+FO+RJEmSJHWw9agDkKReRcSLgDsz88qIWNKaPcWiOcNr072nfXvHAscCLFiwgImJia7iXLAdHP+MB2desEvdbneQNmzY0Ig4+mku7hPMzf2ai/skSZI0rrquWKrjjVwB3JKZL4qIvYAVwC7AVcArMvOBiNgWOBP4DeBHwMsyc01dx0mUMU5+Abw+My/q585ImneeA/x+RBwGPALYkdKCaaeI2Lq2StoduLUuvw7YA1gXEVsDjwbWt81vaX/PQzLzNOA0gMWLF+eSJUu6CvJDZ1/A+67rXz3+mqO62+4gTUxM0O3+j4u5uE8wN/drLu6TJEnSuJpNVzgHx5XUKJl5UmbunpmLKPnly5l5FPAV4CV1sWXABfX5yjpNff3LmZl1/pH1rnF7AXsDlw1pNyRJkiRpbHVVseTguJLGzAnAmyJiNWUMpdPr/NOBx9T5bwJOBMjM64HzgG8DXwCOy8xfDD1qSXNORGwVEVdHxOfq9F4RcWlE3BQR50bENnX+tnV6dX19Uds6Tqrzb4yIQ0azJ5LmGvOTpH7ptm9Ga3DcHep014PjRkT74LiXtK1zysFx58sYJuM2PsS4xQvjF/O4xds0mTkBTNTnNzNFxXVm/hQ4osP73w28e3ARSpqnWi2+d6zTrRbfKyLio5SW3h+hrcV3RBxZl3vZpBbfjwf+MyKebOW3pD4wP0nqixkrloY9OO58GcNk3MaHGLd4YfxiHrd4JUnTa2vx/W5KK8pWi+8/rossB06mnLgtrc+htPj+8OQW38D3aovL/YBvDGk3JM1B5idJ/dRNTcxQB8eVJEmaI+Zdi28Y3p0rx7mlr7GPxjjHPgBDy0/QW44yP42GsY/GOMcOXVQsZeZJwEkAtcXSX2bmURHxacrgtyuYenDcb9A2OG5ErAQ+FRHvpzSVdHBcSZI0J83XFt8wvDtXjnNLX2MfjXGOvZ+GnZ+gtxxlfhoNYx+NcY4duh9jaSonACsi4l3A1Ww6OO5ZtSnkekq/WzLz+ohoDY77IA6OK0mS5i5bfEtqKvOTpL7q6q5wLZk5kZkvqs9vzsz9MvNJmXlE7VtLZv60Tj+pvn5z2/vfnZlPzMynZObn+7srkiRJzZCZJ2Xm7pm5iHKR7cuZeRTwFUqLbpi6xTe0tfiu84+sd2XaC1t8S9pC5idJ/dbftoWSJEmaji2+JTWV+UlST6xYkiRJGqDMnAAm6vObKXdNmrzMT4EjOrz/3ZQ7N0lSX5mfJPXDrLrCSZIkSZIkSS1WLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEmSJEmSJKknVixJkiRJkiSpJ1YsSZIkSZIkqSdWLEkaWxGxR0R8JSJuiIjrI+INdf4uEbEqIm6qf3eu8yMiPhgRqyPi2ojYt21dy+ryN0XEslHtkyRJkiSNEyuWJI2zB4HjM/NpwAHAcRGxD3Ai8KXM3Bv4Up0GeAGwd30cC3wESkUU8HZgf2A/4O2tyihJkiRJUmczVizZIkBSU2XmbZl5VX1+P3ADsBBYCiyviy0HDq/PlwJnZnEJsFNE7AYcAqzKzPWZeTewCjh0iLsiSZIkSWNp6y6WabUIuCoidgCujIhVwNGUFgGnRMSJlBYBJ7Bpi4D9KS0C9m9rEbAYyLqelfUkTpK2SEQsAn4duBRYkJm3Qal8iojH1cUWAmvb3rauzus0f/I2jqW0dGLBggVMTEx0FduC7eD4ZzzY/c7MoNvtDtKGDRsaEUc/zcV9grm5X+OwTxGxB3Am8CvAL4HTMvPUWh46F1gErAFempl3R0QApwKHAT8Bjm5VnNeLcW+tq35XZi5HknpkfpLUbzNWLNWTs9YJ2v0R0d4iYEldbDkwQalYeqhFAHBJRLRaBCyhtggAqJVThwLn9HF/JM1DEfEo4DPAGzPzvlL+mXrRKeblNPM3nZF5GnAawOLFi3PJkiVdxfehsy/gfdd1U4/fnTVHdbfdQZqYmKDb/R8Xc3GfYG7u15jskxfmJDWV+UlSX83qTMcWAf0zDldb241bvDB+MY9bvE0REQ+nVCqdnZmfrbPviIjdam7aDbizzl8H7NH29t2BW+v8JZPmTwwybklzmxfmJDWV+UlSv3VdsWSLgP4ak6utDxm3eGH8Yh63eJugNs0+HbghM9/f9tJKYBlwSv17Qdv810bECsoVt3tr5dNFwHvaBuw+GDhpGPsgae6bTxfmYHjddcf5goyxj8Y4xz4ow8hPdTuzzlHmp9Ew9tEY59ihy4olWwRIaqjnAK8ArouIa+q8t1AqlM6LiGOAHwBH1NcupIwPsJoyRsCrADJzfUS8E7i8LveO1tU3SdoS8+3CHAyvu+44X5Ax9tEY59gHYVj5CXrLUean0TD20Rjn2KGLiiVbBEhqqsz8GlMXagAOnGL5BI7rsK4zgDP6F52k+c4Lc5KayvwkqZ8e1sUyrRYBz4+Ia+rjMEqF0kERcRNwUJ2G0iLgZkqLgI8Br4HSIgBotQi4HFsESJKkOaqLC3Ow+YW5V0ZxAPXCHHARcHBE7Fwvzh1c50lST8xPkvqtm7vC2SJAkiRpduyqK6mpzE+S+qq/nVYlSZLkhTlJjWV+ktRv3XSFkyRJkiRJkjZjxZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6YsWSJEmSJEmSemLFkiRJkiRJknpixZIkSZIkSZJ6svWoA5Akzc6iE/+j7+tcc8oL+75OSZIkSXOfLZYkSZIkSZLUEyuWJEmSJEmS1BMrliRJkiRJktQTx1jqwDFMJEmSJEmSpjf0FksRcWhE3BgRqyPixGFvX5I6MT9Jairzk6SmMj9JGmqLpYjYCvhH4CBgHXB5RKzMzG8PMw5Jmmy+56fZttI8/hkPcvQM77GVptQf8z0/SWou85MkGH5XuP2A1Zl5M0BErACWAiYeSaNmfuqzQXQpHqRuKsu6YYWaBmCs8lO/j32PKanRzE+Shl6xtBBY2za9Dti/fYGIOBY4tk5uiIgbu1z3rsAPtzjCAYr3bjLZ+HgnGbd4Yfxibnq8e446gAGb1/lptl7vPnU0Kdc3wZz7X7H5Ppmf5nB+muaYanzs0zD20RhF7PM+P0HPOarx3zXzU+MY++z0LT8Nu2IpppiXm0xkngacNusVR1yRmYt7DWzYjHfwxi3mcYt3DjI/zYL7ND7m4n7NxX2agflpCsY+GsauSWbMT9Bbjhrn/5exj4axj86wB+9eB+zRNr07cOuQY5CkqZifJDWV+UlSU5mfJA29YulyYO+I2CsitgGOBFYOOQZJmor5SVJTmZ8kNZX5SdJwu8Jl5oMR8VrgImAr4IzMvL5Pq5918+8RM97BG7eYxy3eOcX8NGvu0/iYi/s1F/epI/NTR8Y+Gsauh5ifOjL20TD2EYnMzbrASpIkSZIkSTMadlc4SZIkSZIkzRFWLEmSJEmSJKknY1+xFBGHRsSNEbE6Ik4ccSxrIuK6iLgmIq6o83aJiFURcVP9u3OdHxHxwRr3tRGxb9t6ltXlb4qIZX2O8YyIuDMivtU2r28xRsRv1M9gdX3vVLcg3dJ4T46IW+rnfE1EHNb22kl12zdGxCFt86f8ntSBBi+t+3FuHXRwS+LdIyK+EhE3RMT1EfGGOr+xn7EGp0n5abb6lc9GbdA5bxQGnRdHYRi5U5tq2P9/oOWnfv5uDjqndIq10zb6EPvAy1QRsW2dXl1fX9RD7CMrX/Xjs9fsmJ96jtX8ZH4aXX7KzLF9UAaI+y7wBGAb4JvAPiOMZw2w66R5fwucWJ+fCLy3Pj8M+DwQwAHApXX+LsDN9e/O9fnOfYzxecC+wLcGESNwGfBb9T2fB14wgHhPBv5yimX3qd+BbYG96ndjq+m+J8B5wJH1+UeBv9jCeHcD9q3PdwC+U+Nq7GfsYzCPpuWnHuLf4nzWhMegc16D9qlveXFE+zTw3Oljk8+7af//Lc43w/rdHHRO6RRrp230Ifa+5Q46lKmA1wAfrc+PBM7tIfaRla/68dn7l7XLIAAABMZJREFUmNX/2vzUe6zmJ/PTyPLTuLdY2g9YnZk3Z+YDwApg6YhjmmwpsLw+Xw4c3jb/zCwuAXaKiN2AQ4BVmbk+M+8GVgGH9iuYzPwqsH4QMdbXdszMb2T5dp/Ztq5+xtvJUmBFZv4sM78HrKZ8R6b8ntSa3ucD50+x773Ge1tmXlWf3w/cACykwZ+xBmYc8tNszfZ7PHKDzHmDj35qg8yLAwm4C4POnUPclXHRqP9/B4383RxhOarTNrY09k76WaZqj/184MDZtsoYcflqiz97zYr5qUfmJ/MTI8xP416xtBBY2za9rs4blQS+GPH/t3f/LnoUcRzH3wP+AhVRwSBqYSS9SoqAYnmgnZDCKkEFGy3s8zdopViINiIWopJ0FtFaRdCoiBq7kJBgYbT0x1jMPOfe5fbI7s3s7JD3C5bnud292+/OPvt5hmF3L3wdQngpzzsUY7wE6UMH3Jfnj9XeYp9K1fhAfr97fg2v5EsH3x1c8je13nuB32OMf9eoN19K+RjwBX22sQ5mbfk0VYk8W6secnmOErnYXKXs1E5ra6ea/aclvjeXqHVsGyXU7lNt/05efjWvP0uD/lXNtte1zKeyej9HzKdO8qn3gaW9RhPj4lX874kY4+PA08DLIYSn9ll3rPY17dPUGpeq/S3gEeBR4BLwWp6/mnpDCHcAHwGvxhj/2G/VibWt6fOh/fV+rErkWW96Pu9K5WJTFbNTO62tnWr2n1ruaw+1LtGnKrZf9q9uCGs7FuaT+XRdbvR86n1g6QLw0ODnB4GLjWohxngxv14BPiFdjnd5c0tIfr2SVx+rvcU+larxQn6/e35RMcbLMcZ/Yoz/Am+T2nlOvb+RLj28qWS9IYSbSaHyfozx4zy7qzZWEavKp6kK5dla9ZDLkxTMxWYqZ6d2WlU7Ve4/LfG9uUStY9s4kIX6VNu/k5ffxfXf8rKtYf+qSttrlPlUVrfniPnUVz71PrD0FXAkpKe830J64NaZFoWEEG4PIdy5eQ9sAd/nek7m1U4Cp/P7M8CJkBwDrubL1z4FtkIId+fL/bbyvJqK1JiX/RlCOJbvTT0x+FvF7Hp2y7Okdt7U+1xIT/d/GDhCetDZnp+TfI/q58DxPfZ9bm0BeAf4Mcb4+mBRV22sIlaTT1MVzLO16iGXJymVi0vWPFQ7OxfZib6s5vjX7j8t9L25RK1j2ziQhfpUw9qPA5/l9afU2bJ/VaXtNcp8Kqvbc8R86iyfYqOnhpeaSE9V/5n0BPhTDes4THry/LfAD5taSPdongV+ya/35PkBeDPX/R1wdPC3XiA9hOw88HzhOj8gXUr4F2n088WSNQJHSSf9r8AbQKhQ73u5nnOkk+n+wfqn8rZ/YvBfFcY+J/m4fZn340Pg1gPW+yTp0sRzwDd5embNbexUb1pLPs2ou1ietZ5qZ96K9qlYLjbap+rZ6XRNm6/i+JfMmyW+N2tnylitY9soUHv1PhVwW/75fF5+eEbtzfpXJdreafLxNp/m1Ws+mU/N8mlTkCRJkiRJkjRJ77fCSZIkSZIkqREHliRJkiRJkjSLA0uSJEmSJEmaxYElSZIkSZIkzeLAkiRJkiRJkmZxYEmSJEmSJEmzOLAkSZIkSZKkWf4DPU3lsDOP+gsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x864 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qURMpkLBf1ZH"
      },
      "source": [
        "Install Facets Overview\n",
        "\n",
        "https://pair-code.github.io/facets/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "QH4RxYR14jjz",
        "outputId": "bb5e2d44-b5b2-401e-815f-e78c85304ad5"
      },
      "source": [
        "!pip install facets-overview"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: facets-overview in c:\\programdata\\anaconda3\\lib\\site-packages (1.0.0)\n",
            "Requirement already satisfied: pandas>=0.22.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from facets-overview) (0.24.2)\n",
            "Requirement already satisfied: protobuf>=3.7.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from facets-overview) (3.13.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from facets-overview) (1.16.2)\n",
            "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.22.0->facets-overview) (2.8.0)\n",
            "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.22.0->facets-overview) (2018.9)\n",
            "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from protobuf>=3.7.0->facets-overview) (40.8.0)\n",
            "Requirement already satisfied: six>=1.9 in c:\\programdata\\anaconda3\\lib\\site-packages (from protobuf>=3.7.0->facets-overview) (1.12.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_A9f2J2tf9eA"
      },
      "source": [
        "Create the feature stats for the datasets and stringify it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0juU1aJQ44sk"
      },
      "source": [
        "import base64\n",
        "from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator\n",
        "\n",
        "gfsg = GenericFeatureStatisticsGenerator()\n",
        "proto = gfsg.ProtoFromDataFrames([{'name': 'train + test', 'table': df[webattack_features]}])\n",
        "protostr = base64.b64encode(proto.SerializeToString()).decode(\"utf-8\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Wqs7qELgBkM"
      },
      "source": [
        "Display the facets overview visualization for this data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mYvQDTKH5YRr",
        "outputId": "332a8ae7-e3a6-4c18-983e-be180619ca2e"
      },
      "source": [
        "from IPython.core.display import display, HTML\n",
        "\n",
        "HTML_TEMPLATE = \"\"\"\n",
        "        <script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
        "        <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\" >\n",
        "        <facets-overview id=\"elem\"></facets-overview>\n",
        "        <script>\n",
        "          document.querySelector(\"#elem\").protoInput = \"{protostr}\";\n",
        "        </script>\"\"\"\n",
        "html = HTML_TEMPLATE.format(protostr=protostr)\n",
        "display(HTML(html))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "        <script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
              "        <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\" >\n",
              "        <facets-overview id=\"elem\"></facets-overview>\n",
              "        <script>\n",
              "          document.querySelector(\"#elem\").protoInput = 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              "        </script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "IIqD-DjrgbIr",
        "outputId": "745329d3-a08e-43e1-be61-3410b21254ca"
      },
      "source": [
        "import seaborn as sns\n",
        "corr_matrix = df[webattack_features].corr()\n",
        "plt.rcParams['figure.figsize'] = (16, 5)\n",
        "g = sns.heatmap(corr_matrix, annot=True, fmt='.1g', cmap='Greys')\n",
        "g.set_xticklabels(g.get_xticklabels(), verticalalignment='top', horizontalalignment='right', rotation=30);\n",
        "plt.savefig('corr_heatmap.png', dpi=300, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2J6Pz0xglza"
      },
      "source": [
        "Remove correlated features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "Nml7xnnqgoMz",
        "outputId": "0aa704e5-a5ce-4234-d88b-e8c30b7fc68c"
      },
      "source": [
        "to_be_removed = {'Packet Length Mean', 'Avg Fwd Segment Size', 'Subflow Fwd Bytes', \n",
        "                 'Fwd Packets/s', 'Fwd IAT Total', 'Fwd IAT Max'}\n",
        "webattack_features = [item for item in webattack_features if item not in to_be_removed]\n",
        "webattack_features = webattack_features[:10]\n",
        "webattack_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Average Packet Size',\n",
              " 'Flow Bytes/s',\n",
              " 'Max Packet Length',\n",
              " 'Fwd Packet Length Mean',\n",
              " 'Fwd IAT Min',\n",
              " 'Total Length of Fwd Packets',\n",
              " 'Flow IAT Mean',\n",
              " 'Fwd Packet Length Max',\n",
              " 'Fwd IAT Std',\n",
              " 'Fwd Header Length']"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "vHbm-bkbgDw8",
        "outputId": "446d41d9-78b9-47c8-d99b-7e56a4d0eab8"
      },
      "source": [
        "corr_matrix = df[webattack_features].corr()\n",
        "plt.rcParams['figure.figsize'] = (6, 5)\n",
        "sns.heatmap(corr_matrix, annot=True, fmt='.1g', cmap='Greys');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eh-k8KdMhJ_Q"
      },
      "source": [
        "## Hyperparameter selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jMiAnOthSIn"
      },
      "source": [
        "Reopen the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "vrwIysyAhKxI",
        "outputId": "6be7974a-d713-4c89-b147-8b8cebb7fba7"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "df = pd.read_csv('web_attacks_balanced.csv')\n",
        "df['Label'] = df['Label'].apply(lambda x: 0 if x == 'BENIGN' else 1)\n",
        "y = df['Label'].values\n",
        "X = df[webattack_features]\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7267, 10) (7267,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQbFyYbpYmnA"
      },
      "source": [
        "If necessary, we will work with our own dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckN-NRm-YmnA"
      },
      "source": [
        "#df = pd.read_csv('mix_labelled.csv')\n",
        "#y = df['Label'].values\n",
        "#X = df[webattack_features]\n",
        "#print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFYYEqnihZnZ"
      },
      "source": [
        "We get the list of RandomForestClassifier parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "wc_IVcRPhU82",
        "outputId": "b246e3ad-4240-460b-f294-3ee8109e8b4e"
      },
      "source": [
        "rfc = RandomForestClassifier(random_state=1)\n",
        "rfc.get_params().keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQ8VThpnheWD"
      },
      "source": [
        "For search of quasi-optimal value of one parameter we fix the others."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "4JoxI-axhdD7",
        "outputId": "211fa8fc-2493-48fd-d0d2-a48b6f2e06a4"
      },
      "source": [
        "parameters = {'n_estimators': [10],\n",
        "              'min_samples_leaf': [3],\n",
        "              'max_features': [3], \n",
        "              'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 17, 20, 30, 50]}\n",
        "scoring = {'f1', 'accuracy'}\n",
        "gcv = GridSearchCV(rfc, parameters, scoring=scoring, refit='f1', cv=10, return_train_score=True)\n",
        "get_ipython().run_line_magic('time', 'gcv.fit(X, y)')\n",
        "results = gcv.cv_results_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Wall time: 29 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "OOS-7FXchh31",
        "outputId": "b1a993d9-0a3e-4f59-f012-8c37b503c266"
      },
      "source": [
        "cv_results = pd.DataFrame(gcv.cv_results_)\n",
        "cv_results.head()"
      ],
      "execution_count": null,
      "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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_max_depth</th>\n",
              "      <th>param_max_features</th>\n",
              "      <th>param_min_samples_leaf</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_f1</th>\n",
              "      <th>...</th>\n",
              "      <th>split2_train_accuracy</th>\n",
              "      <th>split3_train_accuracy</th>\n",
              "      <th>split4_train_accuracy</th>\n",
              "      <th>split5_train_accuracy</th>\n",
              "      <th>split6_train_accuracy</th>\n",
              "      <th>split7_train_accuracy</th>\n",
              "      <th>split8_train_accuracy</th>\n",
              "      <th>split9_train_accuracy</th>\n",
              "      <th>mean_train_accuracy</th>\n",
              "      <th>std_train_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.066604</td>\n",
              "      <td>0.009058</td>\n",
              "      <td>0.012801</td>\n",
              "      <td>0.002857</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>{'max_depth': 1, 'max_features': 3, 'min_sampl...</td>\n",
              "      <td>0.853273</td>\n",
              "      <td>...</td>\n",
              "      <td>0.907645</td>\n",
              "      <td>0.907187</td>\n",
              "      <td>0.907645</td>\n",
              "      <td>0.906575</td>\n",
              "      <td>0.903976</td>\n",
              "      <td>0.904143</td>\n",
              "      <td>0.903532</td>\n",
              "      <td>0.906742</td>\n",
              "      <td>0.906151</td>\n",
              "      <td>0.001644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.092505</td>\n",
              "      <td>0.015404</td>\n",
              "      <td>0.013001</td>\n",
              "      <td>0.001789</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>{'max_depth': 2, 'max_features': 3, 'min_sampl...</td>\n",
              "      <td>0.896725</td>\n",
              "      <td>...</td>\n",
              "      <td>0.957187</td>\n",
              "      <td>0.956422</td>\n",
              "      <td>0.956269</td>\n",
              "      <td>0.956728</td>\n",
              "      <td>0.957492</td>\n",
              "      <td>0.952760</td>\n",
              "      <td>0.952760</td>\n",
              "      <td>0.957805</td>\n",
              "      <td>0.956195</td>\n",
              "      <td>0.001790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.102306</td>\n",
              "      <td>0.010199</td>\n",
              "      <td>0.013101</td>\n",
              "      <td>0.001513</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>{'max_depth': 3, 'max_features': 3, 'min_sampl...</td>\n",
              "      <td>0.896203</td>\n",
              "      <td>...</td>\n",
              "      <td>0.959939</td>\n",
              "      <td>0.959327</td>\n",
              "      <td>0.959021</td>\n",
              "      <td>0.958563</td>\n",
              "      <td>0.959327</td>\n",
              "      <td>0.954594</td>\n",
              "      <td>0.955359</td>\n",
              "      <td>0.959792</td>\n",
              "      <td>0.958549</td>\n",
              "      <td>0.001858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.109006</td>\n",
              "      <td>0.017194</td>\n",
              "      <td>0.011901</td>\n",
              "      <td>0.001640</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>{'max_depth': 4, 'max_features': 3, 'min_sampl...</td>\n",
              "      <td>0.907268</td>\n",
              "      <td>...</td>\n",
              "      <td>0.971101</td>\n",
              "      <td>0.970948</td>\n",
              "      <td>0.970948</td>\n",
              "      <td>0.971254</td>\n",
              "      <td>0.972018</td>\n",
              "      <td>0.969271</td>\n",
              "      <td>0.963920</td>\n",
              "      <td>0.973399</td>\n",
              "      <td>0.969956</td>\n",
              "      <td>0.002513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.122207</td>\n",
              "      <td>0.012255</td>\n",
              "      <td>0.013101</td>\n",
              "      <td>0.002022</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>{'max_depth': 5, 'max_features': 3, 'min_sampl...</td>\n",
              "      <td>0.926108</td>\n",
              "      <td>...</td>\n",
              "      <td>0.974159</td>\n",
              "      <td>0.972018</td>\n",
              "      <td>0.972477</td>\n",
              "      <td>0.972324</td>\n",
              "      <td>0.972171</td>\n",
              "      <td>0.969424</td>\n",
              "      <td>0.969424</td>\n",
              "      <td>0.974010</td>\n",
              "      <td>0.972173</td>\n",
              "      <td>0.001564</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 59 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0       0.066604      0.009058         0.012801        0.002857   \n",
              "1       0.092505      0.015404         0.013001        0.001789   \n",
              "2       0.102306      0.010199         0.013101        0.001513   \n",
              "3       0.109006      0.017194         0.011901        0.001640   \n",
              "4       0.122207      0.012255         0.013101        0.002022   \n",
              "\n",
              "  param_max_depth param_max_features param_min_samples_leaf  \\\n",
              "0               1                  3                      3   \n",
              "1               2                  3                      3   \n",
              "2               3                  3                      3   \n",
              "3               4                  3                      3   \n",
              "4               5                  3                      3   \n",
              "\n",
              "  param_n_estimators                                             params  \\\n",
              "0                 10  {'max_depth': 1, 'max_features': 3, 'min_sampl...   \n",
              "1                 10  {'max_depth': 2, 'max_features': 3, 'min_sampl...   \n",
              "2                 10  {'max_depth': 3, 'max_features': 3, 'min_sampl...   \n",
              "3                 10  {'max_depth': 4, 'max_features': 3, 'min_sampl...   \n",
              "4                 10  {'max_depth': 5, 'max_features': 3, 'min_sampl...   \n",
              "\n",
              "   split0_test_f1  ...  split2_train_accuracy  split3_train_accuracy  \\\n",
              "0        0.853273  ...               0.907645               0.907187   \n",
              "1        0.896725  ...               0.957187               0.956422   \n",
              "2        0.896203  ...               0.959939               0.959327   \n",
              "3        0.907268  ...               0.971101               0.970948   \n",
              "4        0.926108  ...               0.974159               0.972018   \n",
              "\n",
              "   split4_train_accuracy  split5_train_accuracy  split6_train_accuracy  \\\n",
              "0               0.907645               0.906575               0.903976   \n",
              "1               0.956269               0.956728               0.957492   \n",
              "2               0.959021               0.958563               0.959327   \n",
              "3               0.970948               0.971254               0.972018   \n",
              "4               0.972477               0.972324               0.972171   \n",
              "\n",
              "   split7_train_accuracy  split8_train_accuracy  split9_train_accuracy  \\\n",
              "0               0.904143               0.903532               0.906742   \n",
              "1               0.952760               0.952760               0.957805   \n",
              "2               0.954594               0.955359               0.959792   \n",
              "3               0.969271               0.963920               0.973399   \n",
              "4               0.969424               0.969424               0.974010   \n",
              "\n",
              "   mean_train_accuracy  std_train_accuracy  \n",
              "0             0.906151            0.001644  \n",
              "1             0.956195            0.001790  \n",
              "2             0.958549            0.001858  \n",
              "3             0.969956            0.002513  \n",
              "4             0.972173            0.001564  \n",
              "\n",
              "[5 rows x 59 columns]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "9Y75B_c_hn3b",
        "outputId": "f467bbbc-6fd9-4bb5-ed8e-f68862d6cabd"
      },
      "source": [
        "# https://scikit-learn.org/dev/auto_examples/model_selection/plot_multi_metric_evaluation.html\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.title(\"GridSearchCV results\", fontsize=14)\n",
        "\n",
        "plt.xlabel(\"max_depth\")\n",
        "plt.ylabel(\"f1\")\n",
        "\n",
        "ax = plt.gca()\n",
        "ax.set_xlim(1, 30)\n",
        "ax.set_ylim(0.9, 1)\n",
        "\n",
        "X_axis = np.array(results['param_max_depth'].data, dtype=float)\n",
        "\n",
        "for scorer, color in zip(sorted(scoring), ['g', 'k']):\n",
        "    for sample, style in (('train', '--'), ('test', '-')):\n",
        "        sample_score_mean = results['mean_%s_%s' % (sample, scorer)]\n",
        "        sample_score_std = results['std_%s_%s' % (sample, scorer)]\n",
        "        ax.fill_between(X_axis, sample_score_mean - sample_score_std,\n",
        "                        sample_score_mean + sample_score_std,\n",
        "                        alpha=0.1 if sample == 'test' else 0, color=color)\n",
        "        ax.plot(X_axis, sample_score_mean, style, color=color,\n",
        "                alpha=1 if sample == 'test' else 0.7,\n",
        "                label=\"%s (%s)\" % (scorer, sample))\n",
        "\n",
        "    best_index = np.nonzero(results['rank_test_%s' % scorer] == 1)[0][0]\n",
        "    best_score = results['mean_test_%s' % scorer][best_index]\n",
        "\n",
        "    # Plot a dotted vertical line at the best score for that scorer marked by x\n",
        "    ax.plot([X_axis[best_index], ] * 2, [0, best_score],\n",
        "            linestyle='-.', color=color, marker='x', markeredgewidth=3, ms=8)\n",
        "\n",
        "    # Annotate the best score for that scorer\n",
        "    ax.annotate(\"%0.2f\" % best_score,\n",
        "                (X_axis[best_index] + 0.3, best_score + 0.005))\n",
        "\n",
        "plt.legend(loc=\"best\")\n",
        "plt.grid(False)\n",
        "plt.savefig('GridSearchCV_results.png', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZtnTrlNk_Uf"
      },
      "source": [
        "### Grid search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaeAf6VRhwJq"
      },
      "source": [
        "parameters = {'n_estimators': [30, 50, 70], \n",
        "              'min_samples_leaf': [3, 5, 7],\n",
        "              'max_features': [3, 5, 7, 10], \n",
        "              'max_depth': [10, 17, 23]}\n",
        "\n",
        "# parameters = {'n_estimators': [250]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OsRxOoSYmnB"
      },
      "source": [
        "The operation may take a long time, 3-5 minutes depending on the computer performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "O3ziP5BolEss",
        "outputId": "0f09bd77-fa69-472a-c195-b4b1f356fffd"
      },
      "source": [
        "gcv = GridSearchCV(rfc, parameters, scoring=scoring, refit='f1', cv=3, return_train_score=True)\n",
        "get_ipython().run_line_magic('time', 'gcv.fit(X, y)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Wall time: 4min 14s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=1),\n",
              "             param_grid={'max_depth': [10, 17, 23],\n",
              "                         'max_features': [3, 5, 7, 10],\n",
              "                         'min_samples_leaf': [3, 5, 7],\n",
              "                         'n_estimators': [30, 50, 70]},\n",
              "             refit='f1', return_train_score=True, scoring={'f1', 'accuracy'})"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwi5Ds61mirz"
      },
      "source": [
        "Let's take a look at the results of the parameter selection."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "vi47XwmNmeHT",
        "outputId": "5680d386-af3e-459f-831a-41594fb99765"
      },
      "source": [
        "gcv.best_estimator_ "
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=17, max_features=7, min_samples_leaf=3,\n",
              "                       n_estimators=30, random_state=1)"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "PxeUXPHHmk3C",
        "outputId": "54492363-1028-4859-c586-782e67c9ac82"
      },
      "source": [
        "gcv.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'max_depth': 17, 'max_features': 7, 'min_samples_leaf': 3, 'n_estimators': 30}"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "4VFX3ERUmlvk",
        "outputId": "5c5f8f0f-674f-4c0c-f646-ce63425d5f7f"
      },
      "source": [
        "gcv.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.95393450118321"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iiskwr3VmoL5"
      },
      "source": [
        "## Final model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4-QVNeDYmnC",
        "outputId": "bb59c502-f664-4882-a248-433b7c21df0a"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(5086, 10) (5086,)\n",
            "(2181, 10) (2181,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "7F8aWhX3mm8B",
        "outputId": "cbb69312-2d41-4eca-93d0-97d194a69604"
      },
      "source": [
        "rfc = RandomForestClassifier(max_depth=17, max_features=10, min_samples_leaf=3, n_estimators=50, random_state=42, oob_score=True)\n",
        "# rfc = RandomForestClassifier(n_estimators=250, random_state=1)\n",
        "rfc.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=17, max_features=10, min_samples_leaf=3,\n",
              "                       n_estimators=50, oob_score=True, random_state=42)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "yULIEGsxmtsQ",
        "outputId": "b9eda3ad-c57b-472d-be97-8fed2161802b"
      },
      "source": [
        "features = X.columns\n",
        "importances = rfc.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "    \n",
        "for index, i in enumerate(indices[:10]):\n",
        "    print('{}.\\t#{}\\t{:.3f}\\t{}'.format(index + 1, i, importances[i], features[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.\t#2\t0.373\tMax Packet Length\n",
            "2.\t#0\t0.256\tAverage Packet Size\n",
            "3.\t#8\t0.111\tFwd IAT Std\n",
            "4.\t#9\t0.068\tFwd Header Length\n",
            "5.\t#5\t0.064\tTotal Length of Fwd Packets\n",
            "6.\t#6\t0.049\tFlow IAT Mean\n",
            "7.\t#4\t0.045\tFwd IAT Min\n",
            "8.\t#1\t0.016\tFlow Bytes/s\n",
            "9.\t#3\t0.014\tFwd Packet Length Mean\n",
            "10.\t#7\t0.004\tFwd Packet Length Max\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOYABC_QYmnD",
        "outputId": "f836fca5-900c-4338-b08a-568bceb8802d"
      },
      "source": [
        "y_pred = rfc.predict(X_test)\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1516,   15],\n",
              "       [  19,  631]], dtype=int64)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "id": "WWgbCOjZoIYX",
        "outputId": "fc94afaf-e952-434b-daf8-d9c898056036"
      },
      "source": [
        "import sklearn.metrics as metrics\n",
        "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "precision = metrics.precision_score(y_test, y_pred)\n",
        "recall = metrics.recall_score(y_test, y_pred)\n",
        "f1 = metrics.f1_score(y_test, y_pred)\n",
        "print('Accuracy =', accuracy)\n",
        "print('Precision =', precision)\n",
        "print('Recall =', recall)\n",
        "print('F1 =', f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9844108207244383\n",
            "Precision = 0.9767801857585139\n",
            "Recall = 0.9707692307692307\n",
            "F1 = 0.9737654320987653\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PElFLh5oYzU"
      },
      "source": [
        "## Model saving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjZyFNFIoMFK"
      },
      "source": [
        "import pickle\n",
        "with open('webattack_detection_rf_model.pkl', 'wb') as f:\n",
        "    pickle.dump(rfc, f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnHSPBrDodIs"
      },
      "source": [
        "## Model approbation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98uX2hrvofRR"
      },
      "source": [
        "Open the previously saved model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "hxBtpLABob_z",
        "outputId": "8b0ef6ee-591e-4ed3-bac4-de1067520773"
      },
      "source": [
        "with open('webattack_detection_rf_model.pkl', 'rb') as f:\n",
        "    rfc = pickle.load(f)\n",
        "rfc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=17, max_features=10, min_samples_leaf=3,\n",
              "                       n_estimators=50, oob_score=True, random_state=42)"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znNcDQd-oh5G"
      },
      "source": [
        "Reopen the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "z_vhT5fcoguX",
        "outputId": "8ce12f85-6c36-4c07-f3af-e78affc90718"
      },
      "source": [
        "df = pd.read_csv('web_attacks_balanced.csv')\n",
        "df['Label'] = df['Label'].apply(lambda x: 0 if x == 'BENIGN' else 1)\n",
        "y_test = df['Label'].values\n",
        "X_test = df[webattack_features]\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7267, 10) (7267,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfPlkmv5YmnE"
      },
      "source": [
        "If necessary, we will work with our own dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pt4g5tNCYmnE"
      },
      "source": [
        "#df = pd.read_csv('mix_labelled.csv')\n",
        "#y_test = df['Label'].values\n",
        "#X_test = df[webattack_features]\n",
        "#print(X_test.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wmcv42wHoltz",
        "outputId": "fbbe0e7b-7a01-4b02-c7e3-498334b70a6e"
      },
      "source": [
        "X_test.head()"
      ],
      "execution_count": null,
      "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>Average Packet Size</th>\n",
              "      <th>Flow Bytes/s</th>\n",
              "      <th>Max Packet Length</th>\n",
              "      <th>Fwd Packet Length Mean</th>\n",
              "      <th>Fwd IAT Min</th>\n",
              "      <th>Total Length of Fwd Packets</th>\n",
              "      <th>Flow IAT Mean</th>\n",
              "      <th>Fwd Packet Length Max</th>\n",
              "      <th>Fwd IAT Std</th>\n",
              "      <th>Fwd Header Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>80.75</td>\n",
              "      <td>3.689366e+03</td>\n",
              "      <td>103.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>25659.333330</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.75</td>\n",
              "      <td>3.635433e+03</td>\n",
              "      <td>103.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>26040.000000</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.50</td>\n",
              "      <td>1.082927e+06</td>\n",
              "      <td>79.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>68.333333</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>94.25</td>\n",
              "      <td>1.928994e+06</td>\n",
              "      <td>112.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>56.333333</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>94.25</td>\n",
              "      <td>1.097643e+06</td>\n",
              "      <td>112.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Average Packet Size  Flow Bytes/s  Max Packet Length  \\\n",
              "0                80.75  3.689366e+03              103.0   \n",
              "1                80.75  3.635433e+03              103.0   \n",
              "2                63.50  1.082927e+06               79.0   \n",
              "3                94.25  1.928994e+06              112.0   \n",
              "4                94.25  1.097643e+06              112.0   \n",
              "\n",
              "   Fwd Packet Length Mean  Fwd IAT Min  Total Length of Fwd Packets  \\\n",
              "0                    39.0          4.0                         78.0   \n",
              "1                    39.0          3.0                         78.0   \n",
              "2                    32.0         37.0                         64.0   \n",
              "3                    51.0          3.0                        102.0   \n",
              "4                    51.0          4.0                        102.0   \n",
              "\n",
              "   Flow IAT Mean  Fwd Packet Length Max  Fwd IAT Std  Fwd Header Length  \n",
              "0   25659.333330                   39.0          0.0               64.0  \n",
              "1   26040.000000                   39.0          0.0               64.0  \n",
              "2      68.333333                   32.0          0.0               64.0  \n",
              "3      56.333333                   51.0          0.0               64.0  \n",
              "4      99.000000                   51.0          0.0               64.0  "
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "-lSUUhseopZz",
        "outputId": "ef9ad3e0-48ca-43b6-9e64-26e1b8add59a"
      },
      "source": [
        "import time\n",
        "seconds = time.time()\n",
        "y_pred = rfc.predict(X_test)\n",
        "print(\"Total operation time:\", time.time() - seconds, \"seconds\")\n",
        "\n",
        "print(\"Benign records detected (0), attacks detected (1):\")\n",
        "unique, counts = np.unique(y_pred, return_counts=True)\n",
        "dict(zip(unique, counts))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total operation time: 0.08200478553771973 seconds\n",
            "Benign records detected (0), attacks detected (1):\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{0: 5102, 1: 2165}"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J2czidjro0at"
      },
      "source": [
        "Confusion matrix:\n",
        "\n",
        "      0  1 - predicted value (Wikipedia uses different convention for axes)\n",
        "    0 TN FP\n",
        "    1 FN TP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "-g03Us5potkl",
        "outputId": "502b9a3b-2cb4-4363-ad66-40f879b52186"
      },
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[5044,   43],\n",
              "       [  58, 2122]], dtype=int64)"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "id": "qBDPbUU_o6Pi",
        "outputId": "8ad0fd77-427b-472c-daae-cda92b67ffc2"
      },
      "source": [
        "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "precision = metrics.precision_score(y_test, y_pred)\n",
        "recall = metrics.recall_score(y_test, y_pred)\n",
        "f1 = metrics.f1_score(y_test, y_pred)\n",
        "print('Accuracy =', accuracy)\n",
        "print('Precision =', precision)\n",
        "print('Recall =', recall)\n",
        "print('F1 =', f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9861015549745424\n",
            "Precision = 0.9801385681293302\n",
            "Recall = 0.9733944954128441\n",
            "F1 = 0.9767548906789413\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4OU8WyZpJdD"
      },
      "source": [
        "Manual calculation of the confusion matrix:\n",
        "    \n",
        "    array([[5075,   12],\n",
        "           [   1, 2179]], dtype=int64)\n",
        "  \n",
        "      0  1 - predicted value (Wikipedia uses different convention for axes)\n",
        "    0 TN FP\n",
        "    1 FN TP\n",
        "\n",
        "    Точность\n",
        "    Precision = TP / (TP + FP) = 2179 / (2179 + 12) = 0.9945230488361478\n",
        "\n",
        "    Полнота\n",
        "    Recall = TP / (TP + FN) = 2179 / (2179 + 1) = 0.9995412844036697\n",
        "\n",
        "    F-мера, параметр = 1, гармоническое среднее точности и полноты, множитель = 2\n",
        "    F1 = 2 * (precision * recall) / (precision + recall) = 0.9970258522077328\n",
        "\n",
        "    Доля правильных ответов\n",
        "    Accuracy = TP + TN / (TP + TN + FP + FN) = (2179 + 5075) / 7267 = 0.998211091234347"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyyORmRrpDTz"
      },
      "source": [
        "predict = pd.DataFrame({'Predict': rfc.predict(X_test)})\n",
        "label = pd.DataFrame({'Label': y_test})\n",
        "result = X_test.join(label).join(predict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Vgpf2SVmpnyp",
        "outputId": "ea5e58a0-f808-45ee-ecdc-4f3e0327cf57"
      },
      "source": [
        "result[result['Predict'] == 1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
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              "      <th>409</th>\n",
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              "      <td>455.121233</td>\n",
              "      <td>1066.0</td>\n",
              "      <td>138.428571</td>\n",
              "      <td>323.0</td>\n",
              "      <td>969.0</td>\n",
              "      <td>4.791930e+05</td>\n",
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              "      <td>139.000000</td>\n",
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              "      <td>178.750000</td>\n",
              "      <td>195.0</td>\n",
              "      <td>2145.0</td>\n",
              "      <td>8.573942e+04</td>\n",
              "      <td>566.0</td>\n",
              "      <td>4.370286e+05</td>\n",
              "      <td>392.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>220.306263</td>\n",
              "      <td>763.0</td>\n",
              "      <td>89.500000</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>1206.155288</td>\n",
              "      <td>2896.0</td>\n",
              "      <td>211.678571</td>\n",
              "      <td>705.0</td>\n",
              "      <td>5927.0</td>\n",
              "      <td>4.653305e+05</td>\n",
              "      <td>561.0</td>\n",
              "      <td>1.474704e+06</td>\n",
              "      <td>904.0</td>\n",
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              "      <td>5.000000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
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              "      <td>632.0</td>\n",
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              "      <td>1.350099e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.863098e+06</td>\n",
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              "      <td>1</td>\n",
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              "      <th>509</th>\n",
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              "      <td>177.814815</td>\n",
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              "      <td>842.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.177770e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.497237e+06</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>962.0</td>\n",
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              "      <td>1.177897e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.497337e+06</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>743.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.845963e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.914828e+06</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>744.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.845958e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.914816e+06</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>614.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.982412e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.204463e+06</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>428.365079</td>\n",
              "      <td>2588.955720</td>\n",
              "      <td>7240.0</td>\n",
              "      <td>204.759036</td>\n",
              "      <td>274.0</td>\n",
              "      <td>16995.0</td>\n",
              "      <td>1.667823e+05</td>\n",
              "      <td>602.0</td>\n",
              "      <td>5.991718e+05</td>\n",
              "      <td>2664.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>4.134080e+06</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>703.0</td>\n",
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              "      <td>1.862511e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.949988e+06</td>\n",
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              "      <td>1.764813e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.742735e+06</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>710.0</td>\n",
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              "      <td>1.292165e+06</td>\n",
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              "      <td>2.740091e+06</td>\n",
              "      <td>104.0</td>\n",
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              "      <th>598</th>\n",
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              "      <td>879.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.319660e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.127227e+06</td>\n",
              "      <td>104.0</td>\n",
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              "    <tr>\n",
              "      <th>604</th>\n",
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              "      <td>3.117663e+05</td>\n",
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              "      <td>1.862601e+06</td>\n",
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              "      <td>3.957119e+06</td>\n",
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              "      <td>4.069545e+06</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>871.0</td>\n",
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              "      <td>1.819138e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.857743e+06</td>\n",
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              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5102</th>\n",
              "      <td>131.625000</td>\n",
              "      <td>210.219880</td>\n",
              "      <td>530.0</td>\n",
              "      <td>130.750000</td>\n",
              "      <td>49.0</td>\n",
              "      <td>523.0</td>\n",
              "      <td>7.155773e+05</td>\n",
              "      <td>523.0</td>\n",
              "      <td>1.734431e+03</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5104</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.100000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5126</th>\n",
              "      <td>297.000000</td>\n",
              "      <td>474.282453</td>\n",
              "      <td>1840.0</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>49.0</td>\n",
              "      <td>536.0</td>\n",
              "      <td>7.156676e+05</td>\n",
              "      <td>536.0</td>\n",
              "      <td>2.216452e+03</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5128</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.300000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5130</th>\n",
              "      <td>302.250000</td>\n",
              "      <td>482.879544</td>\n",
              "      <td>1881.0</td>\n",
              "      <td>107.400000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>537.0</td>\n",
              "      <td>7.153514e+05</td>\n",
              "      <td>537.0</td>\n",
              "      <td>2.499207e+06</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5136</th>\n",
              "      <td>474.900000</td>\n",
              "      <td>947.435391</td>\n",
              "      <td>2701.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>5.569421e+05</td>\n",
              "      <td>600.0</td>\n",
              "      <td>2.234767e+06</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5147</th>\n",
              "      <td>327.500000</td>\n",
              "      <td>523.435945</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>119.800000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>7.150554e+05</td>\n",
              "      <td>599.0</td>\n",
              "      <td>2.499199e+06</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5150</th>\n",
              "      <td>474.800000</td>\n",
              "      <td>946.427763</td>\n",
              "      <td>2701.0</td>\n",
              "      <td>119.800000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>5.574177e+05</td>\n",
              "      <td>599.0</td>\n",
              "      <td>5.465625e+03</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5161</th>\n",
              "      <td>327.500000</td>\n",
              "      <td>523.041474</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>149.750000</td>\n",
              "      <td>343.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>7.155947e+05</td>\n",
              "      <td>599.0</td>\n",
              "      <td>2.252041e+03</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5162</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.300000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5163</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.090000e+02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5210</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.200000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5505</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.600000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5701</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5873</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.300000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5902</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6327</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.800000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6631</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.100000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6748</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.400000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6757</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.300000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6758</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.200000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6845</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.100000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6853</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.400000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6906</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.200000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6960</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.400000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6974</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.800000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.000000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7253</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.500000e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7264</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.463344e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.538345e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2165 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Average Packet Size  Flow Bytes/s  Max Packet Length  \\\n",
              "159              0.000000      0.000000                0.0   \n",
              "181              0.000000      0.000000                0.0   \n",
              "252              0.000000      0.000000                0.0   \n",
              "404            238.785714    644.729782             1047.0   \n",
              "407              0.000000      0.000000                0.0   \n",
              "409            199.916667    455.121233             1066.0   \n",
              "411            218.454545    478.624430             1066.0   \n",
              "482            618.150000   7589.090322             5330.0   \n",
              "483            140.125000    220.306263              763.0   \n",
              "484            549.319149   1206.155288             2896.0   \n",
              "486              0.000000      0.000000                0.0   \n",
              "504              0.000000      0.000000                0.0   \n",
              "508              0.000000      0.000000                0.0   \n",
              "509            508.023256   5277.457273             7926.0   \n",
              "510              0.000000      0.000000                0.0   \n",
              "511              0.000000      0.000000                0.0   \n",
              "588              0.000000      0.000000                0.0   \n",
              "589              0.000000      0.000000                0.0   \n",
              "590              0.000000      0.000000                0.0   \n",
              "591            428.365079   2588.955720             7240.0   \n",
              "592              0.000000      0.000000                0.0   \n",
              "593              0.000000      0.000000                0.0   \n",
              "595              0.000000      0.000000                0.0   \n",
              "597              0.000000      0.000000                0.0   \n",
              "598              0.000000      0.000000                0.0   \n",
              "604            401.478964   3940.159328             5792.0   \n",
              "607              0.000000      0.000000                0.0   \n",
              "608              0.000000      0.000000                0.0   \n",
              "613              0.000000      0.000000                0.0   \n",
              "616              0.000000      0.000000                0.0   \n",
              "...                   ...           ...                ...   \n",
              "5102           131.625000    210.219880              530.0   \n",
              "5104             0.000000      0.000000                0.0   \n",
              "5126           297.000000    474.282453             1840.0   \n",
              "5128             0.000000      0.000000                0.0   \n",
              "5130           302.250000    482.879544             1881.0   \n",
              "5136           474.900000    947.435391             2701.0   \n",
              "5147           327.500000    523.435945             2021.0   \n",
              "5150           474.800000    946.427763             2701.0   \n",
              "5161           327.500000    523.041474             2021.0   \n",
              "5162             0.000000      0.000000                0.0   \n",
              "5163             0.000000      0.000000                0.0   \n",
              "5210             0.000000      0.000000                0.0   \n",
              "5505             0.000000      0.000000                0.0   \n",
              "5701             0.000000      0.000000                0.0   \n",
              "5873             0.000000      0.000000                0.0   \n",
              "5902             0.000000      0.000000                0.0   \n",
              "6016             0.000000      0.000000                0.0   \n",
              "6327             0.000000      0.000000                0.0   \n",
              "6631             0.000000      0.000000                0.0   \n",
              "6748             0.000000      0.000000                0.0   \n",
              "6757             0.000000      0.000000                0.0   \n",
              "6758             0.000000      0.000000                0.0   \n",
              "6845             0.000000      0.000000                0.0   \n",
              "6853             0.000000      0.000000                0.0   \n",
              "6906             0.000000      0.000000                0.0   \n",
              "6960             0.000000      0.000000                0.0   \n",
              "6974             0.000000      0.000000                0.0   \n",
              "7100             0.000000      0.000000                0.0   \n",
              "7253             0.000000      0.000000                0.0   \n",
              "7264             0.000000      0.000000                0.0   \n",
              "\n",
              "      Fwd Packet Length Mean  Fwd IAT Min  Total Length of Fwd Packets  \\\n",
              "159                 0.000000          0.0                          0.0   \n",
              "181                 0.000000          0.0                          0.0   \n",
              "252                 0.000000          0.0                          0.0   \n",
              "404               146.000000          4.0                       1022.0   \n",
              "407                 0.000000          0.0                          0.0   \n",
              "409               138.428571        323.0                        969.0   \n",
              "411               139.000000         18.0                        973.0   \n",
              "482               178.750000        195.0                       2145.0   \n",
              "483                89.500000          4.0                        358.0   \n",
              "484               211.678571        705.0                       5927.0   \n",
              "486                 0.000000          0.0                          0.0   \n",
              "504                 0.000000          0.0                          0.0   \n",
              "508                 0.000000        632.0                          0.0   \n",
              "509               177.814815        250.0                       4801.0   \n",
              "510                 0.000000        842.0                          0.0   \n",
              "511                 0.000000        962.0                          0.0   \n",
              "588                 0.000000        743.0                          0.0   \n",
              "589                 0.000000        744.0                          0.0   \n",
              "590                 0.000000        614.0                          0.0   \n",
              "591               204.759036        274.0                      16995.0   \n",
              "592                 0.000000        867.0                          0.0   \n",
              "593                 0.000000        703.0                          0.0   \n",
              "595                 0.000000        706.0                          0.0   \n",
              "597                 0.000000        710.0                          0.0   \n",
              "598                 0.000000        879.0                          0.0   \n",
              "604               213.348039          3.0                      43523.0   \n",
              "607                 0.000000        807.0                          0.0   \n",
              "608                 0.000000        683.0                          0.0   \n",
              "613                 0.000000        661.0                          0.0   \n",
              "616                 0.000000        871.0                          0.0   \n",
              "...                      ...          ...                          ...   \n",
              "5102              130.750000         49.0                        523.0   \n",
              "5104                0.000000          0.0                          0.0   \n",
              "5126              134.000000         49.0                        536.0   \n",
              "5128                0.000000          0.0                          0.0   \n",
              "5130              107.400000          4.0                        537.0   \n",
              "5136              100.000000          3.0                        600.0   \n",
              "5147              119.800000          4.0                        599.0   \n",
              "5150              119.800000          4.0                        599.0   \n",
              "5161              149.750000        343.0                        599.0   \n",
              "5162                0.000000          0.0                          0.0   \n",
              "5163                0.000000          0.0                          0.0   \n",
              "5210                0.000000          0.0                          0.0   \n",
              "5505                0.000000          0.0                          0.0   \n",
              "5701                0.000000          0.0                          0.0   \n",
              "5873                0.000000          0.0                          0.0   \n",
              "5902                0.000000          0.0                          0.0   \n",
              "6016                0.000000          0.0                          0.0   \n",
              "6327                0.000000          0.0                          0.0   \n",
              "6631                0.000000          0.0                          0.0   \n",
              "6748                0.000000          0.0                          0.0   \n",
              "6757                0.000000          0.0                          0.0   \n",
              "6758                0.000000          0.0                          0.0   \n",
              "6845                0.000000          0.0                          0.0   \n",
              "6853                0.000000          0.0                          0.0   \n",
              "6906                0.000000          0.0                          0.0   \n",
              "6960                0.000000          0.0                          0.0   \n",
              "6974                0.000000          0.0                          0.0   \n",
              "7100                0.000000          0.0                          0.0   \n",
              "7253                0.000000          0.0                          0.0   \n",
              "7264                0.000000          0.0                          0.0   \n",
              "\n",
              "      Flow IAT Mean  Fwd Packet Length Max   Fwd IAT Std  Fwd Header Length  \\\n",
              "159    5.100000e+01                    0.0  0.000000e+00               32.0   \n",
              "181    4.500000e+01                    0.0  0.000000e+00               32.0   \n",
              "252    5.100000e+01                    0.0  0.000000e+00               32.0   \n",
              "404    3.988552e+05                  372.0  5.614102e+04              232.0   \n",
              "407    8.100000e+01                    0.0  0.000000e+00               32.0   \n",
              "409    4.791930e+05                  561.0  2.003103e+06              232.0   \n",
              "411    5.020638e+05                  565.0  2.021330e+06              232.0   \n",
              "482    8.573942e+04                  566.0  4.370286e+05              392.0   \n",
              "483    7.269101e+05                  358.0  4.872589e+04              136.0   \n",
              "484    4.653305e+05                  561.0  1.474704e+06              904.0   \n",
              "486    6.600000e+01                    0.0  0.000000e+00               32.0   \n",
              "504    5.000000e+01                    0.0  0.000000e+00               32.0   \n",
              "508    1.350099e+06                    0.0  2.863098e+06              104.0   \n",
              "509    9.855486e+04                  602.0  4.109942e+05              872.0   \n",
              "510    1.177770e+06                    0.0  2.497237e+06              104.0   \n",
              "511    1.177897e+06                    0.0  2.497337e+06              104.0   \n",
              "588    1.845963e+06                    0.0  3.914828e+06              104.0   \n",
              "589    1.845958e+06                    0.0  3.914816e+06              104.0   \n",
              "590    1.982412e+06                    0.0  4.204463e+06              104.0   \n",
              "591    1.667823e+05                  602.0  5.991718e+05             2664.0   \n",
              "592    1.949402e+06                    0.0  4.134080e+06              104.0   \n",
              "593    1.862511e+06                    0.0  3.949988e+06              104.0   \n",
              "595    1.764813e+06                    0.0  3.742735e+06              104.0   \n",
              "597    1.292165e+06                    0.0  2.740091e+06              104.0   \n",
              "598    5.319660e+05                    0.0  1.127227e+06              104.0   \n",
              "604    1.022249e+05                  602.0  3.117663e+05             6536.0   \n",
              "607    1.862601e+06                    0.0  3.950033e+06              104.0   \n",
              "608    1.865859e+06                    0.0  3.957119e+06              104.0   \n",
              "613    1.918843e+06                    0.0  4.069545e+06              104.0   \n",
              "616    1.819138e+06                    0.0  3.857743e+06              104.0   \n",
              "...             ...                    ...           ...                ...   \n",
              "5102   7.155773e+05                  523.0  1.734431e+03              136.0   \n",
              "5104   7.100000e+01                    0.0  0.000000e+00               32.0   \n",
              "5126   7.156676e+05                  536.0  2.216452e+03              136.0   \n",
              "5128   7.300000e+01                    0.0  0.000000e+00               32.0   \n",
              "5130   7.153514e+05                  537.0  2.499207e+06              168.0   \n",
              "5136   5.569421e+05                  600.0  2.234767e+06              200.0   \n",
              "5147   7.150554e+05                  599.0  2.499199e+06              168.0   \n",
              "5150   5.574177e+05                  599.0  5.465625e+03              168.0   \n",
              "5161   7.155947e+05                  599.0  2.252041e+03              136.0   \n",
              "5162   7.300000e+01                    0.0  0.000000e+00               32.0   \n",
              "5163   1.090000e+02                    0.0  0.000000e+00               32.0   \n",
              "5210   4.200000e+01                    0.0  0.000000e+00               32.0   \n",
              "5505   9.600000e+01                    0.0  0.000000e+00               32.0   \n",
              "5701   5.000000e+01                    0.0  0.000000e+00               32.0   \n",
              "5873   3.300000e+01                    0.0  0.000000e+00               32.0   \n",
              "5902   5.000000e+01                    0.0  0.000000e+00               32.0   \n",
              "6016   5.000000e+01                    0.0  0.000000e+00               32.0   \n",
              "6327   7.800000e+01                    0.0  0.000000e+00               32.0   \n",
              "6631   5.100000e+01                    0.0  0.000000e+00               32.0   \n",
              "6748   7.400000e+01                    0.0  0.000000e+00               32.0   \n",
              "6757   3.300000e+01                    0.0  0.000000e+00               32.0   \n",
              "6758   4.200000e+01                    0.0  0.000000e+00               32.0   \n",
              "6845   5.100000e+01                    0.0  0.000000e+00               32.0   \n",
              "6853   7.400000e+01                    0.0  0.000000e+00               32.0   \n",
              "6906   8.200000e+01                    0.0  0.000000e+00               32.0   \n",
              "6960   3.400000e+01                    0.0  0.000000e+00               32.0   \n",
              "6974   7.800000e+01                    0.0  0.000000e+00               32.0   \n",
              "7100   6.000000e+01                    0.0  0.000000e+00               32.0   \n",
              "7253   7.500000e+01                    0.0  0.000000e+00               32.0   \n",
              "7264   1.463344e+06                    0.0  3.538345e+06                0.0   \n",
              "\n",
              "      Label  Predict  \n",
              "159       0        1  \n",
              "181       0        1  \n",
              "252       0        1  \n",
              "404       1        1  \n",
              "407       1        1  \n",
              "409       1        1  \n",
              "411       1        1  \n",
              "482       1        1  \n",
              "483       1        1  \n",
              "484       1        1  \n",
              "486       1        1  \n",
              "504       0        1  \n",
              "508       1        1  \n",
              "509       1        1  \n",
              "510       1        1  \n",
              "511       1        1  \n",
              "588       1        1  \n",
              "589       1        1  \n",
              "590       1        1  \n",
              "591       1        1  \n",
              "592       1        1  \n",
              "593       1        1  \n",
              "595       1        1  \n",
              "597       1        1  \n",
              "598       1        1  \n",
              "604       1        1  \n",
              "607       1        1  \n",
              "608       1        1  \n",
              "613       1        1  \n",
              "616       1        1  \n",
              "...     ...      ...  \n",
              "5102      1        1  \n",
              "5104      1        1  \n",
              "5126      1        1  \n",
              "5128      1        1  \n",
              "5130      1        1  \n",
              "5136      1        1  \n",
              "5147      1        1  \n",
              "5150      1        1  \n",
              "5161      1        1  \n",
              "5162      1        1  \n",
              "5163      1        1  \n",
              "5210      0        1  \n",
              "5505      0        1  \n",
              "5701      0        1  \n",
              "5873      0        1  \n",
              "5902      0        1  \n",
              "6016      0        1  \n",
              "6327      0        1  \n",
              "6631      0        1  \n",
              "6748      0        1  \n",
              "6757      0        1  \n",
              "6758      0        1  \n",
              "6845      0        1  \n",
              "6853      0        1  \n",
              "6906      0        1  \n",
              "6960      0        1  \n",
              "6974      0        1  \n",
              "7100      0        1  \n",
              "7253      0        1  \n",
              "7264      0        1  \n",
              "\n",
              "[2165 rows x 12 columns]"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "yD7m3P1lppwu",
        "outputId": "d02e0f4d-e60a-4fc0-d583-f6c709777a3e"
      },
      "source": [
        "result[410:430]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Average Packet Size</th>\n",
              "      <th>Flow Bytes/s</th>\n",
              "      <th>Max Packet Length</th>\n",
              "      <th>Fwd Packet Length Mean</th>\n",
              "      <th>Fwd IAT Min</th>\n",
              "      <th>Total Length of Fwd Packets</th>\n",
              "      <th>Flow IAT Mean</th>\n",
              "      <th>Fwd Packet Length Max</th>\n",
              "      <th>Fwd IAT Std</th>\n",
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              "      <th>Label</th>\n",
              "      <th>Predict</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>5.800000e+01</td>\n",
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              "      <th>411</th>\n",
              "      <td>218.454545</td>\n",
              "      <td>4.786244e+02</td>\n",
              "      <td>1066.0</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>18.0</td>\n",
              "      <td>973.0</td>\n",
              "      <td>5.020638e+05</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.021330e+06</td>\n",
              "      <td>232.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>10.333333</td>\n",
              "      <td>1.127273e+05</td>\n",
              "      <td>31.0</td>\n",
              "      <td>10.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1.375000e+02</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1.930402e+02</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.875000e+05</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>16.333333</td>\n",
              "      <td>2.150000e+07</td>\n",
              "      <td>31.0</td>\n",
              "      <td>14.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.400000e+06</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>7.674036e+02</td>\n",
              "      <td>51.0</td>\n",
              "      <td>28.500000</td>\n",
              "      <td>82095.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>4.104750e+04</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.200000e+07</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>80.333333</td>\n",
              "      <td>2.617946e+01</td>\n",
              "      <td>498.0</td>\n",
              "      <td>90.863636</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>3.149316e+06</td>\n",
              "      <td>498.0</td>\n",
              "      <td>4.795221e+06</td>\n",
              "      <td>452.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>420</th>\n",
              "      <td>73.000000</td>\n",
              "      <td>8.219001e+03</td>\n",
              "      <td>89.0</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>49.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>1.030133e+04</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421</th>\n",
              "      <td>92.000000</td>\n",
              "      <td>4.159355e+03</td>\n",
              "      <td>72.0</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>3.077400e+04</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>422</th>\n",
              "      <td>85.000000</td>\n",
              "      <td>4.043302e+03</td>\n",
              "      <td>78.0</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>3.066800e+04</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>75.833333</td>\n",
              "      <td>6.895187e+03</td>\n",
              "      <td>135.0</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>1.212440e+04</td>\n",
              "      <td>37.0</td>\n",
              "      <td>3.094714e+04</td>\n",
              "      <td>104.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>1.509259e+06</td>\n",
              "      <td>133.0</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>48.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>7.200000e+01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>63.500000</td>\n",
              "      <td>6.859660e+03</td>\n",
              "      <td>73.0</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>1.059333e+04</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>86.000000</td>\n",
              "      <td>1.471698e+06</td>\n",
              "      <td>124.0</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>47.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>7.066667e+01</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>59.250000</td>\n",
              "      <td>1.217391e+06</td>\n",
              "      <td>57.0</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>5.366667e+01</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>428</th>\n",
              "      <td>108.500000</td>\n",
              "      <td>1.176578e+04</td>\n",
              "      <td>148.0</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>49.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.099233e+04</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.185567e+03</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1940.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.940000e+03</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Average Packet Size  Flow Bytes/s  Max Packet Length  \\\n",
              "410             0.000000  0.000000e+00                0.0   \n",
              "411           218.454545  4.786244e+02             1066.0   \n",
              "412            10.333333  1.127273e+05               31.0   \n",
              "413             9.000000  1.875000e+05                6.0   \n",
              "414            16.333333  2.150000e+07               31.0   \n",
              "415             9.000000  2.400000e+06                6.0   \n",
              "416            38.000000  7.674036e+02               51.0   \n",
              "417             9.000000  6.000000e+06                6.0   \n",
              "418             9.000000  1.200000e+07                6.0   \n",
              "419            80.333333  2.617946e+01              498.0   \n",
              "420            73.000000  8.219001e+03               89.0   \n",
              "421            92.000000  4.159355e+03               72.0   \n",
              "422            85.000000  4.043302e+03               78.0   \n",
              "423            75.833333  6.895187e+03              135.0   \n",
              "424            89.000000  1.509259e+06              133.0   \n",
              "425            63.500000  6.859660e+03               73.0   \n",
              "426            86.000000  1.471698e+06              124.0   \n",
              "427            59.250000  1.217391e+06               57.0   \n",
              "428           108.500000  1.176578e+04              148.0   \n",
              "429             9.000000  6.185567e+03                6.0   \n",
              "\n",
              "     Fwd Packet Length Mean  Fwd IAT Min  Total Length of Fwd Packets  \\\n",
              "410                0.000000          0.0                          0.0   \n",
              "411              139.000000         18.0                        973.0   \n",
              "412               10.333333          1.0                         31.0   \n",
              "413                6.000000          0.0                          6.0   \n",
              "414               14.333333          1.0                         43.0   \n",
              "415                6.000000          0.0                          6.0   \n",
              "416               28.500000      82095.0                         57.0   \n",
              "417                6.000000          0.0                          6.0   \n",
              "418                6.000000          1.0                         12.0   \n",
              "419               90.863636        104.0                       1999.0   \n",
              "420               38.000000         49.0                         76.0   \n",
              "421               56.000000          0.0                         56.0   \n",
              "422               46.000000          0.0                         46.0   \n",
              "423               37.000000          3.0                        148.0   \n",
              "424               30.000000         48.0                         60.0   \n",
              "425               36.000000          4.0                         72.0   \n",
              "426               32.000000         47.0                         64.0   \n",
              "427               41.000000          3.0                         82.0   \n",
              "428               46.000000         49.0                         92.0   \n",
              "429                6.000000       1940.0                         12.0   \n",
              "\n",
              "     Flow IAT Mean  Fwd Packet Length Max   Fwd IAT Std  Fwd Header Length  \\\n",
              "410   5.800000e+01                    0.0  0.000000e+00               32.0   \n",
              "411   5.020638e+05                  565.0  2.021330e+06              232.0   \n",
              "412   1.375000e+02                   31.0  1.930402e+02               96.0   \n",
              "413   6.400000e+01                    6.0  0.000000e+00               20.0   \n",
              "414   1.000000e+00                   31.0  0.000000e+00               60.0   \n",
              "415   5.000000e+00                    6.0  0.000000e+00               20.0   \n",
              "416   4.104750e+04                   51.0  0.000000e+00               40.0   \n",
              "417   2.000000e+00                    6.0  0.000000e+00               20.0   \n",
              "418   1.000000e+00                    6.0  0.000000e+00               40.0   \n",
              "419   3.149316e+06                  498.0  4.795221e+06              452.0   \n",
              "420   1.030133e+04                   38.0  0.000000e+00               64.0   \n",
              "421   3.077400e+04                   56.0  0.000000e+00               20.0   \n",
              "422   3.066800e+04                   46.0  0.000000e+00               20.0   \n",
              "423   1.212440e+04                   37.0  3.094714e+04              104.0   \n",
              "424   7.200000e+01                   30.0  0.000000e+00               40.0   \n",
              "425   1.059333e+04                   36.0  0.000000e+00               40.0   \n",
              "426   7.066667e+01                   32.0  0.000000e+00               40.0   \n",
              "427   5.366667e+01                   41.0  0.000000e+00               40.0   \n",
              "428   1.099233e+04                   46.0  0.000000e+00               40.0   \n",
              "429   1.940000e+03                    6.0  0.000000e+00               40.0   \n",
              "\n",
              "     Label  Predict  \n",
              "410      0        0  \n",
              "411      1        1  \n",
              "412      0        0  \n",
              "413      0        0  \n",
              "414      0        0  \n",
              "415      0        0  \n",
              "416      0        0  \n",
              "417      0        0  \n",
              "418      0        0  \n",
              "419      0        0  \n",
              "420      0        0  \n",
              "421      0        0  \n",
              "422      0        0  \n",
              "423      0        0  \n",
              "424      0        0  \n",
              "425      0        0  \n",
              "426      0        0  \n",
              "427      0        0  \n",
              "428      0        0  \n",
              "429      0        0  "
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    }
  ]
}