Machine_Learning/Anomaly_based_ids/Anomaly_based_IDS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "PRqZ-x0AsRo0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import glob"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"\"\n",
        "files = [file for file in glob.glob(path + \"**/*.csv\", recursive=True)]"
      ],
      "metadata": {
        "id": "jBMCOcyRsed7"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "[print(f) for f in files]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_b-FZfVxWB-",
        "outputId": "efbd2b22-c771-4b20-ab2e-0c8967e538eb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Monday-WorkingHours.pcap_ISCX.csv\n",
            "Friday-WorkingHours-Morning.pcap_ISCX.csv\n",
            "Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv\n",
            "Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv\n",
            "Tuesday-WorkingHours.pcap_ISCX.csv\n",
            "Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv\n",
            "Wednesday-workingHours.pcap_ISCX.csv\n",
            "Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = [pd.read_csv(f) for f in files]"
      ],
      "metadata": {
        "id": "XnqrxbtmxWd1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for d in dataset:\n",
        "    print(d.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e_9xUDfxr5D",
        "outputId": "f60a1147-4d21-415a-a335-e2f20e901f8a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(363823, 79)\n",
            "(191033, 79)\n",
            "(170366, 79)\n",
            "(288602, 79)\n",
            "(232425, 79)\n",
            "(225745, 79)\n",
            "(229128, 79)\n",
            "(286467, 79)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,len(dataset)):\n",
        "    if i != len(dataset)-1:\n",
        "        same_columns = dataset[i].columns == dataset[i+1].columns\n",
        "\n",
        "        if False in same_columns:\n",
        "            print(i)\n",
        "            break\n",
        "\n",
        "same_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AsrffsByY_B",
        "outputId": "4d1bae90-5c06-4435-ba0c-93d507cbfdd2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.concat([d for d in dataset]).drop_duplicates(keep=False)\n",
        "dataset.reset_index(drop=True, inplace = True)"
      ],
      "metadata": {
        "id": "vTBXALS9zIs-"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ii23b84u1nBj",
        "outputId": "13b66a03-cc18-4351-830a-3a9a93bc05e4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1710918, 79)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Analysis"
      ],
      "metadata": {
        "id": "FIrH8_o21sK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYGPn4FI1tk4",
        "outputId": "613164d0-6a21-4245-e5fc-f7b7ad338297"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1710918 entries, 0 to 1710917\n",
            "Data columns (total 79 columns):\n",
            " #   Column                        Dtype  \n",
            "---  ------                        -----  \n",
            " 0    Destination Port             int64  \n",
            " 1    Flow Duration                int64  \n",
            " 2    Total Fwd Packets            int64  \n",
            " 3    Total Backward Packets       int64  \n",
            " 4   Total Length of Fwd Packets   int64  \n",
            " 5    Total Length of Bwd Packets  int64  \n",
            " 6    Fwd Packet Length Max        int64  \n",
            " 7    Fwd Packet Length Min        int64  \n",
            " 8    Fwd Packet Length Mean       float64\n",
            " 9    Fwd Packet Length Std        float64\n",
            " 10  Bwd Packet Length Max         int64  \n",
            " 11   Bwd Packet Length Min        int64  \n",
            " 12   Bwd Packet Length Mean       float64\n",
            " 13   Bwd Packet Length Std        float64\n",
            " 14  Flow Bytes/s                  float64\n",
            " 15   Flow Packets/s               float64\n",
            " 16   Flow IAT Mean                float64\n",
            " 17   Flow IAT Std                 float64\n",
            " 18   Flow IAT Max                 int64  \n",
            " 19   Flow IAT Min                 int64  \n",
            " 20  Fwd IAT Total                 int64  \n",
            " 21   Fwd IAT Mean                 float64\n",
            " 22   Fwd IAT Std                  float64\n",
            " 23   Fwd IAT Max                  float64\n",
            " 24   Fwd IAT Min                  float64\n",
            " 25  Bwd IAT Total                 float64\n",
            " 26   Bwd IAT Mean                 float64\n",
            " 27   Bwd IAT Std                  float64\n",
            " 28   Bwd IAT Max                  float64\n",
            " 29   Bwd IAT Min                  float64\n",
            " 30  Fwd PSH Flags                 float64\n",
            " 31   Bwd PSH Flags                float64\n",
            " 32   Fwd URG Flags                float64\n",
            " 33   Bwd URG Flags                float64\n",
            " 34   Fwd Header Length            float64\n",
            " 35   Bwd Header Length            float64\n",
            " 36  Fwd Packets/s                 float64\n",
            " 37   Bwd Packets/s                float64\n",
            " 38   Min Packet Length            float64\n",
            " 39   Max Packet Length            float64\n",
            " 40   Packet Length Mean           float64\n",
            " 41   Packet Length Std            float64\n",
            " 42   Packet Length Variance       float64\n",
            " 43  FIN Flag Count                float64\n",
            " 44   SYN Flag Count               float64\n",
            " 45   RST Flag Count               float64\n",
            " 46   PSH Flag Count               float64\n",
            " 47   ACK Flag Count               float64\n",
            " 48   URG Flag Count               float64\n",
            " 49   CWE Flag Count               float64\n",
            " 50   ECE Flag Count               float64\n",
            " 51   Down/Up Ratio                float64\n",
            " 52   Average Packet Size          float64\n",
            " 53   Avg Fwd Segment Size         float64\n",
            " 54   Avg Bwd Segment Size         float64\n",
            " 55   Fwd Header Length.1          float64\n",
            " 56  Fwd Avg Bytes/Bulk            float64\n",
            " 57   Fwd Avg Packets/Bulk         float64\n",
            " 58   Fwd Avg Bulk Rate            float64\n",
            " 59   Bwd Avg Bytes/Bulk           float64\n",
            " 60   Bwd Avg Packets/Bulk         float64\n",
            " 61  Bwd Avg Bulk Rate             float64\n",
            " 62  Subflow Fwd Packets           float64\n",
            " 63   Subflow Fwd Bytes            float64\n",
            " 64   Subflow Bwd Packets          float64\n",
            " 65   Subflow Bwd Bytes            float64\n",
            " 66  Init_Win_bytes_forward        float64\n",
            " 67   Init_Win_bytes_backward      float64\n",
            " 68   act_data_pkt_fwd             float64\n",
            " 69   min_seg_size_forward         float64\n",
            " 70  Active Mean                   float64\n",
            " 71   Active Std                   float64\n",
            " 72   Active Max                   float64\n",
            " 73   Active Min                   float64\n",
            " 74  Idle Mean                     float64\n",
            " 75   Idle Std                     float64\n",
            " 76   Idle Max                     float64\n",
            " 77   Idle Min                     float64\n",
            " 78   Label                        object \n",
            "dtypes: float64(65), int64(13), object(1)\n",
            "memory usage: 1.0+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "3-OofSzD1x3H",
        "outputId": "e46c419c-6eb3-4cbf-96ac-e69cc6dbde57"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Destination Port   Flow Duration   Total Fwd Packets  \\\n",
              "count       1.710918e+06    1.710918e+06        1.710918e+06   \n",
              "mean        7.964489e+03    1.881830e+07        1.270921e+01   \n",
              "std         1.832050e+04    3.692858e+07        9.563504e+02   \n",
              "min         0.000000e+00   -1.300000e+01        1.000000e+00   \n",
              "25%         5.300000e+01    3.120000e+02        2.000000e+00   \n",
              "50%         8.000000e+01    8.361900e+04        2.000000e+00   \n",
              "75%         4.430000e+02    6.202575e+06        7.000000e+00   \n",
              "max         6.553400e+04    1.200000e+08        2.197590e+05   \n",
              "\n",
              "        Total Backward Packets  Total Length of Fwd Packets  \\\n",
              "count             1.710918e+06                 1.710918e+06   \n",
              "mean              1.459537e+01                 7.079400e+02   \n",
              "std               1.272519e+03                 1.235030e+04   \n",
              "min               0.000000e+00                 0.000000e+00   \n",
              "25%               1.000000e+00                 2.600000e+01   \n",
              "50%               2.000000e+00                 6.600000e+01   \n",
              "75%               6.000000e+00                 3.730000e+02   \n",
              "max               2.919220e+05                 1.290000e+07   \n",
              "\n",
              "        Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
              "count                  1.710918e+06            1.710918e+06   \n",
              "mean                   2.411682e+04            2.718669e+02   \n",
              "std                    2.883845e+06            8.556339e+02   \n",
              "min                    0.000000e+00            0.000000e+00   \n",
              "25%                    6.000000e+00            6.000000e+00   \n",
              "50%                    1.600000e+02            4.300000e+01   \n",
              "75%                    3.378000e+03            3.120000e+02   \n",
              "max                    6.554530e+08            2.482000e+04   \n",
              "\n",
              "        Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
              "count            1.710918e+06             1.710918e+06   \n",
              "mean             1.836962e+01             7.178785e+01   \n",
              "std              6.732782e+01             2.209954e+02   \n",
              "min              0.000000e+00             0.000000e+00   \n",
              "25%              0.000000e+00             6.000000e+00   \n",
              "50%              0.000000e+00             3.800000e+01   \n",
              "75%              3.600000e+01             5.528571e+01   \n",
              "max              2.325000e+03             5.940857e+03   \n",
              "\n",
              "        Fwd Packet Length Std  ...   act_data_pkt_fwd   min_seg_size_forward  \\\n",
              "count            1.710918e+06  ...       1.710916e+06           1.710915e+06   \n",
              "mean             9.302499e+01  ...       7.651990e+00          -2.573017e+03   \n",
              "std              3.402864e+02  ...       8.130960e+02           9.636863e+05   \n",
              "min              0.000000e+00  ...       0.000000e+00          -5.368707e+08   \n",
              "25%              0.000000e+00  ...       0.000000e+00           2.000000e+01   \n",
              "50%              0.000000e+00  ...       1.000000e+00           2.000000e+01   \n",
              "75%              9.764984e+01  ...       3.000000e+00           3.200000e+01   \n",
              "max              7.125597e+03  ...       2.135570e+05           1.380000e+02   \n",
              "\n",
              "        Active Mean    Active Std    Active Max    Active Min     Idle Mean  \\\n",
              "count  1.710915e+06  1.710915e+06  1.710915e+06  1.710915e+06  1.710915e+06   \n",
              "mean   1.112091e+05  5.504008e+04  2.058968e+05  7.999493e+04  1.010442e+07   \n",
              "std    7.556761e+05  4.502664e+05  1.173583e+06  6.760158e+05  2.523762e+07   \n",
              "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "25%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "50%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "75%    4.000000e+00  0.000000e+00  4.000000e+00  4.000000e+00  5.259626e+06   \n",
              "max    1.100000e+08  7.050000e+07  1.100000e+08  1.100000e+08  1.200000e+08   \n",
              "\n",
              "           Idle Std      Idle Max      Idle Min  \n",
              "count  1.710915e+06  1.710915e+06  1.710915e+06  \n",
              "mean   7.521471e+05  1.067253e+07  9.519948e+06  \n",
              "std    5.619527e+06  2.621691e+07  2.491269e+07  \n",
              "min    0.000000e+00  0.000000e+00  0.000000e+00  \n",
              "25%    0.000000e+00  0.000000e+00  0.000000e+00  \n",
              "50%    0.000000e+00  0.000000e+00  0.000000e+00  \n",
              "75%    0.000000e+00  5.259922e+06  5.142683e+06  \n",
              "max    7.660000e+07  1.200000e+08  1.200000e+08  \n",
              "\n",
              "[8 rows x 78 columns]"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>Flow Duration</th>\n",
              "      <th>Total Fwd Packets</th>\n",
              "      <th>Total Backward Packets</th>\n",
              "      <th>Total Length of Fwd Packets</th>\n",
              "      <th>Total Length of Bwd Packets</th>\n",
              "      <th>Fwd Packet Length Max</th>\n",
              "      <th>Fwd Packet Length Min</th>\n",
              "      <th>Fwd Packet Length Mean</th>\n",
              "      <th>Fwd Packet Length Std</th>\n",
              "      <th>...</th>\n",
              "      <th>act_data_pkt_fwd</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active Mean</th>\n",
              "      <th>Active Std</th>\n",
              "      <th>Active Max</th>\n",
              "      <th>Active Min</th>\n",
              "      <th>Idle Mean</th>\n",
              "      <th>Idle Std</th>\n",
              "      <th>Idle Max</th>\n",
              "      <th>Idle Min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>1.710918e+06</td>\n",
              "      <td>...</td>\n",
              "      <td>1.710916e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "      <td>1.710915e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.964489e+03</td>\n",
              "      <td>1.881830e+07</td>\n",
              "      <td>1.270921e+01</td>\n",
              "      <td>1.459537e+01</td>\n",
              "      <td>7.079400e+02</td>\n",
              "      <td>2.411682e+04</td>\n",
              "      <td>2.718669e+02</td>\n",
              "      <td>1.836962e+01</td>\n",
              "      <td>7.178785e+01</td>\n",
              "      <td>9.302499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>7.651990e+00</td>\n",
              "      <td>-2.573017e+03</td>\n",
              "      <td>1.112091e+05</td>\n",
              "      <td>5.504008e+04</td>\n",
              "      <td>2.058968e+05</td>\n",
              "      <td>7.999493e+04</td>\n",
              "      <td>1.010442e+07</td>\n",
              "      <td>7.521471e+05</td>\n",
              "      <td>1.067253e+07</td>\n",
              "      <td>9.519948e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.832050e+04</td>\n",
              "      <td>3.692858e+07</td>\n",
              "      <td>9.563504e+02</td>\n",
              "      <td>1.272519e+03</td>\n",
              "      <td>1.235030e+04</td>\n",
              "      <td>2.883845e+06</td>\n",
              "      <td>8.556339e+02</td>\n",
              "      <td>6.732782e+01</td>\n",
              "      <td>2.209954e+02</td>\n",
              "      <td>3.402864e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>8.130960e+02</td>\n",
              "      <td>9.636863e+05</td>\n",
              "      <td>7.556761e+05</td>\n",
              "      <td>4.502664e+05</td>\n",
              "      <td>1.173583e+06</td>\n",
              "      <td>6.760158e+05</td>\n",
              "      <td>2.523762e+07</td>\n",
              "      <td>5.619527e+06</td>\n",
              "      <td>2.621691e+07</td>\n",
              "      <td>2.491269e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.300000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-5.368707e+08</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.300000e+01</td>\n",
              "      <td>3.120000e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.600000e+01</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.000000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.000000e+01</td>\n",
              "      <td>8.361900e+04</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>6.600000e+01</td>\n",
              "      <td>1.600000e+02</td>\n",
              "      <td>4.300000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.800000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.430000e+02</td>\n",
              "      <td>6.202575e+06</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>3.730000e+02</td>\n",
              "      <td>3.378000e+03</td>\n",
              "      <td>3.120000e+02</td>\n",
              "      <td>3.600000e+01</td>\n",
              "      <td>5.528571e+01</td>\n",
              "      <td>9.764984e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>5.259626e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.259922e+06</td>\n",
              "      <td>5.142683e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.553400e+04</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>2.197590e+05</td>\n",
              "      <td>2.919220e+05</td>\n",
              "      <td>1.290000e+07</td>\n",
              "      <td>6.554530e+08</td>\n",
              "      <td>2.482000e+04</td>\n",
              "      <td>2.325000e+03</td>\n",
              "      <td>5.940857e+03</td>\n",
              "      <td>7.125597e+03</td>\n",
              "      <td>...</td>\n",
              "      <td>2.135570e+05</td>\n",
              "      <td>1.380000e+02</td>\n",
              "      <td>1.100000e+08</td>\n",
              "      <td>7.050000e+07</td>\n",
              "      <td>1.100000e+08</td>\n",
              "      <td>1.100000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>7.660000e+07</td>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>1.200000e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 78 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d46e144e-fc85-423d-9c70-d1fb8cbdb39c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d46e144e-fc85-423d-9c70-d1fb8cbdb39c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d46e144e-fc85-423d-9c70-d1fb8cbdb39c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-076707e7-0e0f-4794-8fbd-b07667641888\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-076707e7-0e0f-4794-8fbd-b07667641888')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-076707e7-0e0f-4794-8fbd-b07667641888 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset[' Label'].unique())\n",
        "len(dataset[' Label'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jd_P4oNq50Pk",
        "outputId": "574c6e8d-6f57-4418-e759-bb3bcb3f8005"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['BENIGN' nan 'Bot' 'Web Attack � Brute Force' 'Web Attack � XSS'\n",
            " 'Web Attack � Sql Injection' 'Infiltration' 'FTP-Patator' 'SSH-Patator'\n",
            " 'DDoS' 'DoS slowloris' 'DoS Slowhttptest' 'DoS Hulk' 'PortScan']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "lwYsVYG_138_",
        "outputId": "cd108e16-efde-4cab-ebcb-9737464c6b6c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Destination Port   Flow Duration   Total Fwd Packets  \\\n",
              "0              49188               4                   2   \n",
              "1              49486               3                   2   \n",
              "2                 88             609                   7   \n",
              "3                 88             879                   9   \n",
              "4                 88            1160                   9   \n",
              "\n",
              "    Total Backward Packets  Total Length of Fwd Packets  \\\n",
              "0                        0                           12   \n",
              "1                        0                           12   \n",
              "2                        4                          484   \n",
              "3                        4                          656   \n",
              "4                        6                         3134   \n",
              "\n",
              "    Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
              "0                             0                       6   \n",
              "1                             0                       6   \n",
              "2                           414                     233   \n",
              "3                          3064                     313   \n",
              "4                          3048                    1552   \n",
              "\n",
              "    Fwd Packet Length Min   Fwd Packet Length Mean   Fwd Packet Length Std  \\\n",
              "0                       6                 6.000000                0.000000   \n",
              "1                       6                 6.000000                0.000000   \n",
              "2                       0                69.142857              111.967895   \n",
              "3                       0                72.888889              136.153814   \n",
              "4                       0               348.222222              682.482560   \n",
              "\n",
              "   ...   min_seg_size_forward  Active Mean   Active Std   Active Max  \\\n",
              "0  ...                   20.0          0.0          0.0          0.0   \n",
              "1  ...                   20.0          0.0          0.0          0.0   \n",
              "2  ...                   20.0          0.0          0.0          0.0   \n",
              "3  ...                   20.0          0.0          0.0          0.0   \n",
              "4  ...                   20.0          0.0          0.0          0.0   \n",
              "\n",
              "    Active Min  Idle Mean   Idle Std   Idle Max   Idle Min   Label  \n",
              "0          0.0        0.0        0.0        0.0        0.0  BENIGN  \n",
              "1          0.0        0.0        0.0        0.0        0.0  BENIGN  \n",
              "2          0.0        0.0        0.0        0.0        0.0  BENIGN  \n",
              "3          0.0        0.0        0.0        0.0        0.0  BENIGN  \n",
              "4          0.0        0.0        0.0        0.0        0.0  BENIGN  \n",
              "\n",
              "[5 rows x 79 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab697189-af9d-4e1e-a784-1cf9efe133d0\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Destination Port</th>\n",
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              "      <th>Total Backward Packets</th>\n",
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              "      <th>Total Length of Bwd Packets</th>\n",
              "      <th>Fwd Packet Length Max</th>\n",
              "      <th>Fwd Packet Length Min</th>\n",
              "      <th>Fwd Packet Length Mean</th>\n",
              "      <th>Fwd Packet Length Std</th>\n",
              "      <th>...</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active Mean</th>\n",
              "      <th>Active Std</th>\n",
              "      <th>Active Max</th>\n",
              "      <th>Active Min</th>\n",
              "      <th>Idle Mean</th>\n",
              "      <th>Idle Std</th>\n",
              "      <th>Idle Max</th>\n",
              "      <th>Idle Min</th>\n",
              "      <th>Label</th>\n",
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              "      <th>0</th>\n",
              "      <td>49188</td>\n",
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              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49486</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>88</td>\n",
              "      <td>609</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>484</td>\n",
              "      <td>414</td>\n",
              "      <td>233</td>\n",
              "      <td>0</td>\n",
              "      <td>69.142857</td>\n",
              "      <td>111.967895</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88</td>\n",
              "      <td>879</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>656</td>\n",
              "      <td>3064</td>\n",
              "      <td>313</td>\n",
              "      <td>0</td>\n",
              "      <td>72.888889</td>\n",
              "      <td>136.153814</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>88</td>\n",
              "      <td>1160</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3134</td>\n",
              "      <td>3048</td>\n",
              "      <td>1552</td>\n",
              "      <td>0</td>\n",
              "      <td>348.222222</td>\n",
              "      <td>682.482560</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 79 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-ab697189-af9d-4e1e-a784-1cf9efe133d0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ab697189-af9d-4e1e-a784-1cf9efe133d0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7b19c3fa-13c8-4b7b-81e6-765c263da6fc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7b19c3fa-13c8-4b7b-81e6-765c263da6fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "-VPOwUCh4OOC"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset[' Label'].where(dataset[' Label'] != \"BENIGN\")"
      ],
      "metadata": {
        "id": "H6GKI31B4PL7"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "chart = sns.countplot(data, palette=\"Set1\")\n",
        "plt.xticks(rotation=45, horizontalalignment=\"right\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "BQ6_X88y4RcR",
        "outputId": "9d997270-e6fc-4fe0-f51e-0bd8fe8f379e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-ddaf9ee2c49c>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  chart = sns.countplot(data, palette=\"Set1\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([     0.,  20000.,  40000.,  60000.,  80000., 100000., 120000.,\n",
              "        140000.]),\n",
              " [Text(0.0, 0, '0'),\n",
              "  Text(20000.0, 0, '20000'),\n",
              "  Text(40000.0, 0, '40000'),\n",
              "  Text(60000.0, 0, '60000'),\n",
              "  Text(80000.0, 0, '80000'),\n",
              "  Text(100000.0, 0, '100000'),\n",
              "  Text(120000.0, 0, '120000'),\n",
              "  Text(140000.0, 0, '140000')])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning"
      ],
      "metadata": {
        "id": "XyGF82lk6DsM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Renaming columns"
      ],
      "metadata": {
        "id": "FXxsX6Ul6Sf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing whitespaces in column names.\n",
        "\n",
        "col_names = [col.replace(' ', '') for col in dataset.columns]\n",
        "dataset.columns = col_names\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "OPkYuUDQ6CTU",
        "outputId": "081f3a63-b123-4b5b-ee46-37485abfb9ae"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DestinationPort  FlowDuration  TotalFwdPackets  TotalBackwardPackets  \\\n",
              "0            49188             4                2                     0   \n",
              "1            49486             3                2                     0   \n",
              "2               88           609                7                     4   \n",
              "3               88           879                9                     4   \n",
              "4               88          1160                9                     6   \n",
              "\n",
              "   TotalLengthofFwdPackets  TotalLengthofBwdPackets  FwdPacketLengthMax  \\\n",
              "0                       12                        0                   6   \n",
              "1                       12                        0                   6   \n",
              "2                      484                      414                 233   \n",
              "3                      656                     3064                 313   \n",
              "4                     3134                     3048                1552   \n",
              "\n",
              "   FwdPacketLengthMin  FwdPacketLengthMean  FwdPacketLengthStd  ...  \\\n",
              "0                   6             6.000000            0.000000  ...   \n",
              "1                   6             6.000000            0.000000  ...   \n",
              "2                   0            69.142857          111.967895  ...   \n",
              "3                   0            72.888889          136.153814  ...   \n",
              "4                   0           348.222222          682.482560  ...   \n",
              "\n",
              "   min_seg_size_forward  ActiveMean  ActiveStd  ActiveMax  ActiveMin  \\\n",
              "0                  20.0         0.0        0.0        0.0        0.0   \n",
              "1                  20.0         0.0        0.0        0.0        0.0   \n",
              "2                  20.0         0.0        0.0        0.0        0.0   \n",
              "3                  20.0         0.0        0.0        0.0        0.0   \n",
              "4                  20.0         0.0        0.0        0.0        0.0   \n",
              "\n",
              "   IdleMean  IdleStd  IdleMax  IdleMin   Label  \n",
              "0       0.0      0.0      0.0      0.0  BENIGN  \n",
              "1       0.0      0.0      0.0      0.0  BENIGN  \n",
              "2       0.0      0.0      0.0      0.0  BENIGN  \n",
              "3       0.0      0.0      0.0      0.0  BENIGN  \n",
              "4       0.0      0.0      0.0      0.0  BENIGN  \n",
              "\n",
              "[5 rows x 79 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DestinationPort</th>\n",
              "      <th>FlowDuration</th>\n",
              "      <th>TotalFwdPackets</th>\n",
              "      <th>TotalBackwardPackets</th>\n",
              "      <th>TotalLengthofFwdPackets</th>\n",
              "      <th>TotalLengthofBwdPackets</th>\n",
              "      <th>FwdPacketLengthMax</th>\n",
              "      <th>FwdPacketLengthMin</th>\n",
              "      <th>FwdPacketLengthMean</th>\n",
              "      <th>FwdPacketLengthStd</th>\n",
              "      <th>...</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>ActiveMean</th>\n",
              "      <th>ActiveStd</th>\n",
              "      <th>ActiveMax</th>\n",
              "      <th>ActiveMin</th>\n",
              "      <th>IdleMean</th>\n",
              "      <th>IdleStd</th>\n",
              "      <th>IdleMax</th>\n",
              "      <th>IdleMin</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49188</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49486</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>88</td>\n",
              "      <td>609</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>484</td>\n",
              "      <td>414</td>\n",
              "      <td>233</td>\n",
              "      <td>0</td>\n",
              "      <td>69.142857</td>\n",
              "      <td>111.967895</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88</td>\n",
              "      <td>879</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>656</td>\n",
              "      <td>3064</td>\n",
              "      <td>313</td>\n",
              "      <td>0</td>\n",
              "      <td>72.888889</td>\n",
              "      <td>136.153814</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>88</td>\n",
              "      <td>1160</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3134</td>\n",
              "      <td>3048</td>\n",
              "      <td>1552</td>\n",
              "      <td>0</td>\n",
              "      <td>348.222222</td>\n",
              "      <td>682.482560</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 79 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca846630-9516-4f40-ac43-3917fae6e222')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ca846630-9516-4f40-ac43-3917fae6e222 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ca846630-9516-4f40-ac43-3917fae6e222');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-458f5eca-1318-4e46-8e1f-4e35e6178b00\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-458f5eca-1318-4e46-8e1f-4e35e6178b00')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-458f5eca-1318-4e46-8e1f-4e35e6178b00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use regular expressions to replace wierd characters with dunders.\n",
        "\n",
        "import re\n",
        "\n",
        "label_names = dataset['Label'].unique()\n",
        "label_names = [str(l) for l in label_names]\n",
        "\n",
        "label_names = [re.sub(\"[^a-zA-Z ]+\", \"\", l) for l in label_names]\n",
        "label_names = [re.sub(\"[\\s\\s]\", '_', l) for l in label_names]\n",
        "label_names = [lab.replace(\"__\", \"_\") for lab in label_names]\n",
        "\n",
        "label_names, len(label_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GwZzBQf6lc1",
        "outputId": "cc22530e-d96c-4cfa-e48d-a17eaac28677"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['BENIGN',\n",
              "  'Bot',\n",
              "  'Web_Attack_Brute_Force',\n",
              "  'Web_Attack_XSS',\n",
              "  'Web_Attack_Sql_Injection',\n",
              "  'Infiltration',\n",
              "  'FTPPatator',\n",
              "  'SSHPatator',\n",
              "  'DDoS',\n",
              "  'DoS_slowloris',\n",
              "  'DoS_Slowhttptest',\n",
              "  'DoS_Hulk',\n",
              "  'PortScan'],\n",
              " 13)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving cleaned dataset.\n",
        "\n",
        "dataset.to_csv(\"Dataset_clean.csv\", index=False)"
      ],
      "metadata": {
        "id": "3cCCYCmB6y_Z"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Removing NULL values"
      ],
      "metadata": {
        "id": "GasOlGVp66_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking if there are any NULL values in the dataset.\n",
        "\n",
        "dataset.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVtCF5lY6z7O",
        "outputId": "91d3763f-1271-4ab9-ef9c-619aa06f5437"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking which column/s contain NULL values.\n",
        "\n",
        "[col for col in dataset if dataset[col].isnull().values.any()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOnxf-5C7CnO",
        "outputId": "e594d124-817e-41ad-c092-f477c7fb6d2d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['FlowBytes/s',\n",
              " 'FwdIATMax',\n",
              " 'FwdIATMin',\n",
              " 'BwdIATTotal',\n",
              " 'BwdIATMean',\n",
              " 'BwdIATStd',\n",
              " 'BwdIATMax',\n",
              " 'BwdIATMin',\n",
              " 'FwdPSHFlags',\n",
              " 'BwdPSHFlags',\n",
              " 'FwdURGFlags',\n",
              " 'BwdURGFlags',\n",
              " 'FwdHeaderLength',\n",
              " 'BwdHeaderLength',\n",
              " 'FwdPackets/s',\n",
              " 'BwdPackets/s',\n",
              " 'MinPacketLength',\n",
              " 'MaxPacketLength',\n",
              " 'PacketLengthMean',\n",
              " 'PacketLengthStd',\n",
              " 'PacketLengthVariance',\n",
              " 'FINFlagCount',\n",
              " 'SYNFlagCount',\n",
              " 'RSTFlagCount',\n",
              " 'PSHFlagCount',\n",
              " 'ACKFlagCount',\n",
              " 'URGFlagCount',\n",
              " 'CWEFlagCount',\n",
              " 'ECEFlagCount',\n",
              " 'Down/UpRatio',\n",
              " 'AveragePacketSize',\n",
              " 'AvgFwdSegmentSize',\n",
              " 'AvgBwdSegmentSize',\n",
              " 'FwdHeaderLength.1',\n",
              " 'FwdAvgBytes/Bulk',\n",
              " 'FwdAvgPackets/Bulk',\n",
              " 'FwdAvgBulkRate',\n",
              " 'BwdAvgBytes/Bulk',\n",
              " 'BwdAvgPackets/Bulk',\n",
              " 'BwdAvgBulkRate',\n",
              " 'SubflowFwdPackets',\n",
              " 'SubflowFwdBytes',\n",
              " 'SubflowBwdPackets',\n",
              " 'SubflowBwdBytes',\n",
              " 'Init_Win_bytes_forward',\n",
              " 'Init_Win_bytes_backward',\n",
              " 'act_data_pkt_fwd',\n",
              " 'min_seg_size_forward',\n",
              " 'ActiveMean',\n",
              " 'ActiveStd',\n",
              " 'ActiveMax',\n",
              " 'ActiveMin',\n",
              " 'IdleMean',\n",
              " 'IdleStd',\n",
              " 'IdleMax',\n",
              " 'IdleMin',\n",
              " 'Label']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking how many NULL values it this column contains.\n",
        "\n",
        "dataset['FlowBytes/s'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fg3AghcS7C21",
        "outputId": "4768e5a2-33be-4b64-a997-54142498db0f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "216"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Considering that only 334 rows contain NULL vlaues in the entire dataset, which makes about 0.01%, we\n",
        "# can safely remove all NULL rows without spoiling the data.\n",
        "\n",
        "334/dataset.shape[0]*100\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VrrH7MHM7E8q",
        "outputId": "28690fb1-4739-4394-eed9-23ddba7d2436"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01952168368092451"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing rows that contain NULL values and checking if number of removed rows is equal to the number of null values.\n",
        "\n",
        "before = dataset.shape\n",
        "\n",
        "dataset.dropna(inplace=True)\n",
        "\n",
        "after = dataset.shape\n",
        "\n",
        "before[0] - after[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-pCxQhK7IJn",
        "outputId": "1b87ad96-245f-4720-f768-f65c4ab5e818"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "219"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().any().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-HlN1bB7Lcq",
        "outputId": "6470c88c-36ed-45fb-80c4-537682a99c2a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Removing non-finite values"
      ],
      "metadata": {
        "id": "u3iXviNd7lUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "lykWk5ov7NC0"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labl = dataset['Label']\n",
        "dataset = dataset.loc[:, dataset.columns != 'Label'].astype('float64')"
      ],
      "metadata": {
        "id": "UEc9kfEY7oDJ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking if all values are finite.\n",
        "\n",
        "np.all(np.isfinite(dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1g8KPVWa7ykG",
        "outputId": "2b5f3ac5-dc94-4889-9690-2e15d1116fb9"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking what column/s contain non-finite values.\n",
        "\n",
        "nonfinite = [col for col in dataset if not np.all(np.isfinite(dataset[col]))]"
      ],
      "metadata": {
        "id": "xPFcxG2P7ywF"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking how many non-finite values each column contains.\n",
        "\n",
        "finite = np.isfinite(dataset['FlowBytes/s']).sum()\n",
        "\n",
        "dataset.shape[0] - finite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0z7iyLt79Lv",
        "outputId": "de5a731e-36f4-497a-9841-5a8a60cc276f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "875"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking how many non-finite values each column contains.\n",
        "\n",
        "finite = np.isfinite(dataset['FlowPackets/s']).sum()\n",
        "\n",
        "dataset.shape[0] - finite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjM5QIYS79cL",
        "outputId": "b17e601f-8306-4296-8ad7-a39ba360a842"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "875"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.replace([np.inf, -np.inf], np.nan)"
      ],
      "metadata": {
        "id": "HGYNOM3n7_WO"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.any(np.isnan(dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDtwONco8DUD",
        "outputId": "d3b8bf2d-0f91-4ecb-b3e2-69485cb9a27e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.merge(labl, how='outer', left_index=True, right_index=True)"
      ],
      "metadata": {
        "id": "bLK4nhFU8E2O"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing new NaN values.\n",
        "\n",
        "dataset.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "cdVNYu0K8GvB"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization"
      ],
      "metadata": {
        "id": "Fjf33yxx8Qr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "g1CF7iXr8IqL"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "rand_perm = np.random.permutation(dataset.shape[0])"
      ],
      "metadata": {
        "id": "YJjUzlGx8WwE"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = dataset.columns[:-1]\n",
        "\n",
        "dataset_subset = dataset.loc[rand_perm[:1000],:]"
      ],
      "metadata": {
        "id": "d6GHkThA8htt"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_subset = dataset_subset.replace([np.inf, -np.inf], np.nan)\n",
        "dataset_subset.dropna(inplace=True)\n",
        "data_subset = dataset_subset[feature_cols].values"
      ],
      "metadata": {
        "id": "sqUTEowG8Yfm"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing the principal component analysis. With just 19 components the variance ratio remains 99%, which is great.\n",
        "\n",
        "pca = PCA(n_components=19)\n",
        "pca_res = pca.fit_transform(data_subset)\n",
        "\n",
        "#data_subset = None\n",
        "np.sum(pca.explained_variance_ratio_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaY6HNIU8bRx",
        "outputId": "7597e5ab-912a-449f-aa0c-59c1d375521f"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.999973917022049"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computing t-SNE\n",
        "\n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity=40, n_iter=1000)\n",
        "tsne_res = tsne.fit_transform(data_subset)\n",
        "print(\"done\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_vlAzOhXIFb",
        "outputId": "ced46dc1-739d-4ad9-e5f7-a29b2335d9f2"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_subset['tsne_firstD'] = tsne_res[:,0]\n",
        "dataset_subset['tsne_secondD'] = tsne_res[:,1]"
      ],
      "metadata": {
        "id": "SuqSOOXAXMEL"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,16))\n",
        "\n",
        "num_colors = len(dataset_subset['Label'].unique())\n",
        "\n",
        "sns.scatterplot(\n",
        "    x=\"tsne_firstD\", y=\"tsne_secondD\",\n",
        "    palette=sns.color_palette(\"hsv\", num_colors),\n",
        "    data=dataset_subset,\n",
        "    hue=\"Label\",\n",
        "    legend=\"full\",\n",
        "    alpha=0.3,\n",
        "    s = 100\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "I9OZkmZGYNVb",
        "outputId": "58c19789-895d-4ecd-fe7c-9fcf32734458"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='tsne_firstD', ylabel='tsne_secondD'>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preparation"
      ],
      "metadata": {
        "id": "gJIQLCb1ZtL3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling data"
      ],
      "metadata": {
        "id": "7NdBKipHZv-y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting dataset into features and labels.\n",
        "\n",
        "labels = dataset['Label']\n",
        "features = dataset.loc[:, dataset.columns != 'Label'].astype('float64')\n",
        "features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "INTHhEIoZzq8",
        "outputId": "c2427961-f02b-48ec-c4aa-2415e45b3b80"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DestinationPort  FlowDuration  TotalFwdPackets  TotalBackwardPackets  \\\n",
              "0          49188.0           4.0              2.0                   0.0   \n",
              "1          49486.0           3.0              2.0                   0.0   \n",
              "2             88.0         609.0              7.0                   4.0   \n",
              "3             88.0         879.0              9.0                   4.0   \n",
              "4             88.0        1160.0              9.0                   6.0   \n",
              "\n",
              "   TotalLengthofFwdPackets  TotalLengthofBwdPackets  FwdPacketLengthMax  \\\n",
              "0                     12.0                      0.0                 6.0   \n",
              "1                     12.0                      0.0                 6.0   \n",
              "2                    484.0                    414.0               233.0   \n",
              "3                    656.0                   3064.0               313.0   \n",
              "4                   3134.0                   3048.0              1552.0   \n",
              "\n",
              "   FwdPacketLengthMin  FwdPacketLengthMean  FwdPacketLengthStd  ...  \\\n",
              "0                 6.0             6.000000            0.000000  ...   \n",
              "1                 6.0             6.000000            0.000000  ...   \n",
              "2                 0.0            69.142857          111.967895  ...   \n",
              "3                 0.0            72.888889          136.153814  ...   \n",
              "4                 0.0           348.222222          682.482560  ...   \n",
              "\n",
              "   act_data_pkt_fwd  min_seg_size_forward  ActiveMean  ActiveStd  ActiveMax  \\\n",
              "0               1.0                  20.0         0.0        0.0        0.0   \n",
              "1               1.0                  20.0         0.0        0.0        0.0   \n",
              "2               5.0                  20.0         0.0        0.0        0.0   \n",
              "3               7.0                  20.0         0.0        0.0        0.0   \n",
              "4               7.0                  20.0         0.0        0.0        0.0   \n",
              "\n",
              "   ActiveMin  IdleMean  IdleStd  IdleMax  IdleMin  \n",
              "0        0.0       0.0      0.0      0.0      0.0  \n",
              "1        0.0       0.0      0.0      0.0      0.0  \n",
              "2        0.0       0.0      0.0      0.0      0.0  \n",
              "3        0.0       0.0      0.0      0.0      0.0  \n",
              "4        0.0       0.0      0.0      0.0      0.0  \n",
              "\n",
              "[5 rows x 78 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d49e6631-31a4-4bad-89fb-7454645094eb\" class=\"colab-df-container\">\n",
              "    <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>DestinationPort</th>\n",
              "      <th>FlowDuration</th>\n",
              "      <th>TotalFwdPackets</th>\n",
              "      <th>TotalBackwardPackets</th>\n",
              "      <th>TotalLengthofFwdPackets</th>\n",
              "      <th>TotalLengthofBwdPackets</th>\n",
              "      <th>FwdPacketLengthMax</th>\n",
              "      <th>FwdPacketLengthMin</th>\n",
              "      <th>FwdPacketLengthMean</th>\n",
              "      <th>FwdPacketLengthStd</th>\n",
              "      <th>...</th>\n",
              "      <th>act_data_pkt_fwd</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>ActiveMean</th>\n",
              "      <th>ActiveStd</th>\n",
              "      <th>ActiveMax</th>\n",
              "      <th>ActiveMin</th>\n",
              "      <th>IdleMean</th>\n",
              "      <th>IdleStd</th>\n",
              "      <th>IdleMax</th>\n",
              "      <th>IdleMin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49188.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49486.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>88.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>484.0</td>\n",
              "      <td>414.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.142857</td>\n",
              "      <td>111.967895</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88.0</td>\n",
              "      <td>879.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>656.0</td>\n",
              "      <td>3064.0</td>\n",
              "      <td>313.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.888889</td>\n",
              "      <td>136.153814</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>88.0</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3134.0</td>\n",
              "      <td>3048.0</td>\n",
              "      <td>1552.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>348.222222</td>\n",
              "      <td>682.482560</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 78 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d49e6631-31a4-4bad-89fb-7454645094eb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d49e6631-31a4-4bad-89fb-7454645094eb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d49e6631-31a4-4bad-89fb-7454645094eb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-573863c1-a164-4b03-b40c-5f5865e0eb59\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-573863c1-a164-4b03-b40c-5f5865e0eb59')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-573863c1-a164-4b03-b40c-5f5865e0eb59 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
        "\n",
        "scaler = RobustScaler()\n",
        "scaler.fit(features)\n",
        "\n",
        "features = scaler.transform(features)\n",
        "\n",
        "features[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLcFoDlYYQFC",
        "outputId": "09049ed6-cbae-4db4-c030-0c6e70967502"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.25917949e+02, -1.34914580e-02,  0.00000000e+00, -4.00000000e-01,\n",
              "       -1.55619597e-01, -4.73372781e-02, -1.20915033e-01,  1.66666667e-01,\n",
              "       -6.48985959e-01,  0.00000000e+00, -7.51404494e-02,  0.00000000e+00,\n",
              "       -3.07178631e-01,  0.00000000e+00,  4.81778100e+01,  5.27512226e+01,\n",
              "       -3.04798998e-02, -7.15150553e-03, -1.05412315e-02,  0.00000000e+00,\n",
              "       -1.55913032e-04, -3.78375674e-04,  0.00000000e+00, -1.44990373e-04,\n",
              "        1.21951220e-02, -1.13170500e-05, -6.46631334e-05,  0.00000000e+00,\n",
              "       -1.85244360e-05, -4.16666667e-02,  0.00000000e+00,  0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00, -1.81818182e-01, -3.03030303e-01,\n",
              "        7.80048000e+01, -1.24513720e-01,  1.71428571e-01, -7.48959778e-02,\n",
              "       -2.80621572e-01, -7.98310332e-02, -6.28235009e-03,  0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00,\n",
              "        1.00000000e+00,  0.00000000e+00,  0.00000000e+00, -1.00000000e+00,\n",
              "       -3.43169399e-01, -6.48985959e-01, -3.07178631e-01, -1.81818182e-01,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00, -1.55619597e-01,\n",
              "       -4.00000000e-01, -4.73372781e-02,  8.91004516e-03, -4.23728814e-03,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Encoding"
      ],
      "metadata": {
        "id": "gtfSet1xZ-IA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "LE = LabelEncoder()\n",
        "\n",
        "LE.fit(labels)\n",
        "labels = LE.transform(labels)\n",
        "\n",
        "np.unique(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZvYH8ncaCDS",
        "outputId": "133ade36-d5c1-4d9f-9677-e95248cf6406"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking that encoding reversal works.\n",
        "\n",
        "d = LE.inverse_transform(labels)\n",
        "d = pd.Series(d)\n",
        "d.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVAsARe-aIAW",
        "outputId": "48321667-8787-43a6-80c3-ee86264e7d7b"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['BENIGN', 'Bot', 'Web Attack � Brute Force', 'Web Attack � XSS',\n",
              "       'Web Attack � Sql Injection', 'Infiltration', 'FTP-Patator',\n",
              "       'SSH-Patator', 'DDoS', 'DoS slowloris', 'DoS Slowhttptest',\n",
              "       'DoS Hulk', 'PortScan'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting the data"
      ],
      "metadata": {
        "id": "5VeLdHesaLW5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=.2)\n",
        "\n",
        "features_train.shape, features_test.shape, labels_train.shape, labels_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qky-7wYIaOo3",
        "outputId": "36b028e8-2f5e-40a6-9425-097c7be01747"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1367859, 78), (341965, 78), (1367859,), (341965,))"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Training"
      ],
      "metadata": {
        "id": "BqOXQJVQaYDO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import datetime"
      ],
      "metadata": {
        "id": "0Pn-8UDsaKQo"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "\n",
        "    tf.keras.layers.Flatten(input_shape=(78,)),\n",
        "    tf.keras.layers.Dense(67, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(15, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziZOsd5fadJI",
        "outputId": "4b610286-c97e-4be4-f777-c4bff908ac2f"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "             loss='sparse_categorical_crossentropy',\n",
        "             metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "XkiV7_A1afN2"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "mSJvMfvgagxm"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_dir = os.path.join(\n",
        "    \"train_logs\",\n",
        "    datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"),\n",
        ")\n",
        "\n",
        "# TF callback that sets up TensorBoard with training logs.\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "\n",
        "# TF callback that stops training when best value of validationi loss function is reached. It also\n",
        "# restores weights from the best training iteration.\n",
        "eary_stop_callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10, restore_best_weights=True)"
      ],
      "metadata": {
        "id": "cbLLtLS2ajf8"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(features_train,\n",
        "          labels_train,\n",
        "          epochs=30,\n",
        "          callbacks=[tensorboard_callback, eary_stop_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdCy6Fqnandm",
        "outputId": "67e9625a-38bf-4da1-e813-748d6eda0495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.8687\n",
            "Epoch 2/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 3ms/step - accuracy: 0.9738 - loss: 0.9482\n",
            "Epoch 3/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.5483\n",
            "Epoch 4/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.7850\n",
            "Epoch 5/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 3ms/step - accuracy: 0.9735 - loss: 2.7357\n",
            "Epoch 6/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 3ms/step - accuracy: 0.9735 - loss: 0.6574\n",
            "Epoch 7/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 3ms/step - accuracy: 0.9742 - loss: 11.0740\n",
            "Epoch 8/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 3ms/step - accuracy: 0.9740 - loss: 1.2965\n",
            "Epoch 9/30\n",
            "\u001b[1m42746/42746\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.8835\n",
            "Epoch 10/30\n",
            "\u001b[1m14587/42746\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:14\u001b[0m 3ms/step - accuracy: 0.9745 - loss: 0.7716"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating model accuracy.\n",
        "model.evaluate(features_test, labels_test, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNKrEtYjapef",
        "outputId": "8b38005a-d68c-411e-9409-dd77df60c08e"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10687/10687 - 14s - 1ms/step - accuracy: 0.9769 - loss: 6.1665\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.166480541229248, 0.9769186973571777]"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the model.\n",
        "\n",
        "model.save('saved_models/IDS_model_' + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") + '.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eb9_gyRoarVN",
        "outputId": "c9a839bd-3e8b-4d1f-c749-04b8dbad34b8"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    }
  ]
}