modeling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Data Collection and Pre-Processing"
      ],
      "metadata": {
        "id": "1uuPBclnRE25"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqF8J5dg1mpV"
      },
      "source": [
        "###Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "3kuw_QKv_jly"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Collection"
      ],
      "metadata": {
        "id": "S0HBJ3IWRA7Q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYTOBe1zF9kt",
        "outputId": "38c9eb55-f49b-44e7-cd8f-fb3da1475dd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "upFtR5oeA8y7"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/DATA_MINING/CTU-IoT-Malware-Capture-1-1conn.log.labeled.csv', sep='|')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.to_csv('output_data.csv', index=False)"
      ],
      "metadata": {
        "id": "5gTCkltmfyRU"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "bgFGSQp2J0yQ",
        "outputId": "055e9fb5-8768-4f98-a5db-6fa856ffce86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ts                 uid        id.orig_h  id.orig_p  \\\n",
              "0        1.525880e+09  CUmrqr4svHuSXJy5z7  192.168.100.103    51524.0   \n",
              "1        1.525880e+09  CH98aB3s1kJeq6SFOc  192.168.100.103    56305.0   \n",
              "2        1.525880e+09   C3GBTkINvXNjVGtN5  192.168.100.103    41101.0   \n",
              "3        1.525880e+09   CDe43c1PtgynajGI6  192.168.100.103    60905.0   \n",
              "4        1.525880e+09  CJaDcG3MZzvf1YVYI4  192.168.100.103    44301.0   \n",
              "...               ...                 ...              ...        ...   \n",
              "1008743  1.526283e+09   CUyBg1WA04SBmf3ah  192.168.100.103    43763.0   \n",
              "1008744  1.526283e+09  CvaIoX2b9VynuYieBf  192.168.100.103    43763.0   \n",
              "1008745  1.526283e+09   CVHrUzIUN0mW5lQsl  192.168.100.103    43763.0   \n",
              "1008746  1.526283e+09  CkGlnL2pxzlusrFlLj  192.168.100.103    43763.0   \n",
              "1008747  1.526283e+09  CoOjVZ1zw3pWf7a9n5  192.168.100.103    43763.0   \n",
              "\n",
              "               id.resp_h  id.resp_p proto service  duration orig_bytes  ...  \\\n",
              "0         65.127.233.163       23.0   tcp       -  2.999051          0  ...   \n",
              "1          63.150.16.171       23.0   tcp       -         -          -  ...   \n",
              "2           111.40.23.49       23.0   tcp       -         -          -  ...   \n",
              "3        131.174.215.147       23.0   tcp       -  2.998796          0  ...   \n",
              "4            91.42.47.63       23.0   tcp       -         -          -  ...   \n",
              "...                  ...        ...   ...     ...       ...        ...  ...   \n",
              "1008743    16.219.83.137    64906.0   udp       -         -          -  ...   \n",
              "1008744   100.57.245.196    39435.0   udp       -         -          -  ...   \n",
              "1008745     249.99.119.9    26169.0   udp       -         -          -  ...   \n",
              "1008746  205.103.167.192    18241.0   udp       -         -          -  ...   \n",
              "1008747    23.70.168.160    16854.0   udp       -         -          -  ...   \n",
              "\n",
              "        local_resp missed_bytes history orig_pkts  orig_ip_bytes resp_pkts  \\\n",
              "0                -          0.0       S       3.0          180.0       0.0   \n",
              "1                -          0.0       S       1.0           60.0       0.0   \n",
              "2                -          0.0       S       1.0           60.0       0.0   \n",
              "3                -          0.0       S       3.0          180.0       0.0   \n",
              "4                -          0.0       S       1.0           60.0       0.0   \n",
              "...            ...          ...     ...       ...            ...       ...   \n",
              "1008743          -          0.0       D       1.0           40.0       0.0   \n",
              "1008744          -          0.0       D       1.0           40.0       0.0   \n",
              "1008745          -          0.0       D       1.0           40.0       0.0   \n",
              "1008746          -          0.0       D       1.0           40.0       0.0   \n",
              "1008747          -          0.0       D       1.0           40.0       0.0   \n",
              "\n",
              "         resp_ip_bytes  tunnel_parents      label             detailed-label  \n",
              "0                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "1                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "2                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "3                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "4                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "...                ...             ...        ...                        ...  \n",
              "1008743            0.0               -     Benign                          -  \n",
              "1008744            0.0               -     Benign                          -  \n",
              "1008745            0.0               -     Benign                          -  \n",
              "1008746            0.0               -     Benign                          -  \n",
              "1008747            0.0               -     Benign                          -  \n",
              "\n",
              "[1008748 rows x 23 columns]"
            ],
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              "\n",
              "  <div id=\"df-0ce2ec87-e9eb-4236-b3e8-85a268171732\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ts</th>\n",
              "      <th>uid</th>\n",
              "      <th>id.orig_h</th>\n",
              "      <th>id.orig_p</th>\n",
              "      <th>id.resp_h</th>\n",
              "      <th>id.resp_p</th>\n",
              "      <th>proto</th>\n",
              "      <th>service</th>\n",
              "      <th>duration</th>\n",
              "      <th>orig_bytes</th>\n",
              "      <th>...</th>\n",
              "      <th>local_resp</th>\n",
              "      <th>missed_bytes</th>\n",
              "      <th>history</th>\n",
              "      <th>orig_pkts</th>\n",
              "      <th>orig_ip_bytes</th>\n",
              "      <th>resp_pkts</th>\n",
              "      <th>resp_ip_bytes</th>\n",
              "      <th>tunnel_parents</th>\n",
              "      <th>label</th>\n",
              "      <th>detailed-label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CUmrqr4svHuSXJy5z7</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>51524.0</td>\n",
              "      <td>65.127.233.163</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>2.999051</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CH98aB3s1kJeq6SFOc</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>56305.0</td>\n",
              "      <td>63.150.16.171</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>C3GBTkINvXNjVGtN5</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>41101.0</td>\n",
              "      <td>111.40.23.49</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CDe43c1PtgynajGI6</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>60905.0</td>\n",
              "      <td>131.174.215.147</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>2.998796</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CJaDcG3MZzvf1YVYI4</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>44301.0</td>\n",
              "      <td>91.42.47.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1008743</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CUyBg1WA04SBmf3ah</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>16.219.83.137</td>\n",
              "      <td>64906.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008744</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CvaIoX2b9VynuYieBf</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>100.57.245.196</td>\n",
              "      <td>39435.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>1008745</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CVHrUzIUN0mW5lQsl</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>249.99.119.9</td>\n",
              "      <td>26169.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CkGlnL2pxzlusrFlLj</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>205.103.167.192</td>\n",
              "      <td>18241.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>1008747</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CoOjVZ1zw3pWf7a9n5</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>23.70.168.160</td>\n",
              "      <td>16854.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
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              "      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-0ce2ec87-e9eb-4236-b3e8-85a268171732 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-0ce2ec87-e9eb-4236-b3e8-85a268171732');\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-ea874c3b-e58c-4fa0-9117-71eca1be1e0e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea874c3b-e58c-4fa0-9117-71eca1be1e0e')\"\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",
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              "    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-ea874c3b-e58c-4fa0-9117-71eca1be1e0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ts                 uid        id.orig_h  id.orig_p  \\\n",
              "0        1.525880e+09  CUmrqr4svHuSXJy5z7  192.168.100.103    51524.0   \n",
              "1        1.525880e+09  CH98aB3s1kJeq6SFOc  192.168.100.103    56305.0   \n",
              "2        1.525880e+09   C3GBTkINvXNjVGtN5  192.168.100.103    41101.0   \n",
              "3        1.525880e+09   CDe43c1PtgynajGI6  192.168.100.103    60905.0   \n",
              "4        1.525880e+09  CJaDcG3MZzvf1YVYI4  192.168.100.103    44301.0   \n",
              "...               ...                 ...              ...        ...   \n",
              "1008743  1.526283e+09   CUyBg1WA04SBmf3ah  192.168.100.103    43763.0   \n",
              "1008744  1.526283e+09  CvaIoX2b9VynuYieBf  192.168.100.103    43763.0   \n",
              "1008745  1.526283e+09   CVHrUzIUN0mW5lQsl  192.168.100.103    43763.0   \n",
              "1008746  1.526283e+09  CkGlnL2pxzlusrFlLj  192.168.100.103    43763.0   \n",
              "1008747  1.526283e+09  CoOjVZ1zw3pWf7a9n5  192.168.100.103    43763.0   \n",
              "\n",
              "               id.resp_h  id.resp_p proto service  duration orig_bytes  ...  \\\n",
              "0         65.127.233.163       23.0   tcp       -  2.999051          0  ...   \n",
              "1          63.150.16.171       23.0   tcp       -         -          -  ...   \n",
              "2           111.40.23.49       23.0   tcp       -         -          -  ...   \n",
              "3        131.174.215.147       23.0   tcp       -  2.998796          0  ...   \n",
              "4            91.42.47.63       23.0   tcp       -         -          -  ...   \n",
              "...                  ...        ...   ...     ...       ...        ...  ...   \n",
              "1008743    16.219.83.137    64906.0   udp       -         -          -  ...   \n",
              "1008744   100.57.245.196    39435.0   udp       -         -          -  ...   \n",
              "1008745     249.99.119.9    26169.0   udp       -         -          -  ...   \n",
              "1008746  205.103.167.192    18241.0   udp       -         -          -  ...   \n",
              "1008747    23.70.168.160    16854.0   udp       -         -          -  ...   \n",
              "\n",
              "        local_resp missed_bytes history orig_pkts  orig_ip_bytes resp_pkts  \\\n",
              "0                -          0.0       S       3.0          180.0       0.0   \n",
              "1                -          0.0       S       1.0           60.0       0.0   \n",
              "2                -          0.0       S       1.0           60.0       0.0   \n",
              "3                -          0.0       S       3.0          180.0       0.0   \n",
              "4                -          0.0       S       1.0           60.0       0.0   \n",
              "...            ...          ...     ...       ...            ...       ...   \n",
              "1008743          -          0.0       D       1.0           40.0       0.0   \n",
              "1008744          -          0.0       D       1.0           40.0       0.0   \n",
              "1008745          -          0.0       D       1.0           40.0       0.0   \n",
              "1008746          -          0.0       D       1.0           40.0       0.0   \n",
              "1008747          -          0.0       D       1.0           40.0       0.0   \n",
              "\n",
              "         resp_ip_bytes  tunnel_parents      label             detailed-label  \n",
              "0                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "1                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "2                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "3                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "4                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "...                ...             ...        ...                        ...  \n",
              "1008743            0.0               -     Benign                          -  \n",
              "1008744            0.0               -     Benign                          -  \n",
              "1008745            0.0               -     Benign                          -  \n",
              "1008746            0.0               -     Benign                          -  \n",
              "1008747            0.0               -     Benign                          -  \n",
              "\n",
              "[1008748 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b0cb0d41-6b93-4aac-8d3a-6ab00898f060\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ts</th>\n",
              "      <th>uid</th>\n",
              "      <th>id.orig_h</th>\n",
              "      <th>id.orig_p</th>\n",
              "      <th>id.resp_h</th>\n",
              "      <th>id.resp_p</th>\n",
              "      <th>proto</th>\n",
              "      <th>service</th>\n",
              "      <th>duration</th>\n",
              "      <th>orig_bytes</th>\n",
              "      <th>...</th>\n",
              "      <th>local_resp</th>\n",
              "      <th>missed_bytes</th>\n",
              "      <th>history</th>\n",
              "      <th>orig_pkts</th>\n",
              "      <th>orig_ip_bytes</th>\n",
              "      <th>resp_pkts</th>\n",
              "      <th>resp_ip_bytes</th>\n",
              "      <th>tunnel_parents</th>\n",
              "      <th>label</th>\n",
              "      <th>detailed-label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CUmrqr4svHuSXJy5z7</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>51524.0</td>\n",
              "      <td>65.127.233.163</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>2.999051</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CH98aB3s1kJeq6SFOc</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>56305.0</td>\n",
              "      <td>63.150.16.171</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>C3GBTkINvXNjVGtN5</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>41101.0</td>\n",
              "      <td>111.40.23.49</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CDe43c1PtgynajGI6</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>60905.0</td>\n",
              "      <td>131.174.215.147</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>2.998796</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CJaDcG3MZzvf1YVYI4</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>44301.0</td>\n",
              "      <td>91.42.47.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008743</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CUyBg1WA04SBmf3ah</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>16.219.83.137</td>\n",
              "      <td>64906.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008744</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CvaIoX2b9VynuYieBf</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>100.57.245.196</td>\n",
              "      <td>39435.0</td>\n",
              "      <td>udp</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>Benign</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008745</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CVHrUzIUN0mW5lQsl</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>249.99.119.9</td>\n",
              "      <td>26169.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CkGlnL2pxzlusrFlLj</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>205.103.167.192</td>\n",
              "      <td>18241.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008747</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CoOjVZ1zw3pWf7a9n5</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>23.70.168.160</td>\n",
              "      <td>16854.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1008748 rows × 23 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-b0cb0d41-6b93-4aac-8d3a-6ab00898f060')\"\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-b0cb0d41-6b93-4aac-8d3a-6ab00898f060 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-b0cb0d41-6b93-4aac-8d3a-6ab00898f060');\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-c1763458-4d9d-43da-afa2-2c5dcc84a97e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c1763458-4d9d-43da-afa2-2c5dcc84a97e')\"\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-c1763458-4d9d-43da-afa2-2c5dcc84a97e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtW7d4ARPAAT"
      },
      "source": [
        "##Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynxpmEwePghH",
        "outputId": "6add92cd-548c-4317-d451-2607f6d45659"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ts                0\n",
              "uid               0\n",
              "id.orig_h         0\n",
              "id.orig_p         0\n",
              "id.resp_h         0\n",
              "id.resp_p         0\n",
              "proto             0\n",
              "service           0\n",
              "duration          0\n",
              "orig_bytes        0\n",
              "resp_bytes        0\n",
              "conn_state        0\n",
              "local_orig        0\n",
              "local_resp        0\n",
              "missed_bytes      0\n",
              "history           0\n",
              "orig_pkts         0\n",
              "orig_ip_bytes     0\n",
              "resp_pkts         0\n",
              "resp_ip_bytes     0\n",
              "tunnel_parents    0\n",
              "label             0\n",
              "detailed-label    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ts                0\n",
              "uid               0\n",
              "id.orig_h         0\n",
              "id.orig_p         0\n",
              "id.resp_h         0\n",
              "id.resp_p         0\n",
              "proto             0\n",
              "service           0\n",
              "duration          0\n",
              "orig_bytes        0\n",
              "resp_bytes        0\n",
              "conn_state        0\n",
              "local_orig        0\n",
              "local_resp        0\n",
              "missed_bytes      0\n",
              "history           0\n",
              "orig_pkts         0\n",
              "orig_ip_bytes     0\n",
              "resp_pkts         0\n",
              "resp_ip_bytes     0\n",
              "tunnel_parents    0\n",
              "label             0\n",
              "detailed-label    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#Checking na values\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1eBBoy2P2QZ"
      },
      "source": [
        "We don't have na values in our dataset but we have '-'values in our data which is null"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxBEG0w2MnNT",
        "outputId": "e95b0078-a9ce-4e33-a4f8-414a22f2bd15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total unique values sum for column ts is:\n",
            "1.525880e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "               ..\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526283e+09    1\n",
            "Name: ts, Length: 1008748, dtype: int64\n",
            "Total unique values sum for column uid is:\n",
            "CUmrqr4svHuSXJy5z7    1\n",
            "COj4Eq4lmR86amgfI6    1\n",
            "CBqL9l4KOG0Y3zauml    1\n",
            "CLIXVIOgCuf9Pv6j      1\n",
            "CImdWC3kB3Zhudx4q3    1\n",
            "                     ..\n",
            "CIEU4w1lSTwgbUFwHe    1\n",
            "CaXPwRJbmRa0E2N32     1\n",
            "CHMbUQ2DPEF61n2Chf    1\n",
            "CwWZ3m2uWO6qokLuu4    1\n",
            "CoOjVZ1zw3pWf7a9n5    1\n",
            "Name: uid, Length: 1008748, dtype: int64\n",
            "Total unique values sum for column id.orig_h is:\n",
            "192.168.100.103    991061\n",
            "192.168.100.1        1651\n",
            "4.68.110.10            43\n",
            "194.70.98.42           23\n",
            "218.248.235.161        13\n",
            "                    ...  \n",
            "203.233.35.193          1\n",
            "84.61.235.57            1\n",
            "209.180.197.181         1\n",
            "119.166.138.30          1\n",
            "255.176.39.22           1\n",
            "Name: id.orig_h, Length: 15004, dtype: int64\n",
            "Total unique values sum for column id.orig_p is:\n",
            "43763.0    399650\n",
            "3.0         14516\n",
            "123.0        8551\n",
            "11.0         2903\n",
            "23.0          158\n",
            "            ...  \n",
            "41861.0         1\n",
            "33950.0         1\n",
            "47075.0         1\n",
            "4.0             1\n",
            "448.0           1\n",
            "Name: id.orig_p, Length: 28243, dtype: int64\n",
            "Total unique values sum for column id.resp_h is:\n",
            "192.168.100.103    17687\n",
            "147.231.100.5       4313\n",
            "213.239.154.12      1428\n",
            "37.187.104.44       1408\n",
            "89.221.214.130      1402\n",
            "                   ...  \n",
            "48.19.58.31            1\n",
            "213.71.0.16            1\n",
            "241.61.159.121         1\n",
            "88.236.105.181         1\n",
            "23.70.168.160          1\n",
            "Name: id.resp_h, Length: 597107, dtype: int64\n",
            "Total unique values sum for column id.resp_p is:\n",
            "23.0       271561\n",
            "8080.0     135382\n",
            "2323.0      88184\n",
            "9527.0      44338\n",
            "123.0        8553\n",
            "            ...  \n",
            "10962.0         1\n",
            "43527.0         1\n",
            "14119.0         1\n",
            "45660.0         1\n",
            "688.0           1\n",
            "Name: id.resp_p, Length: 65426, dtype: int64\n",
            "Total unique values sum for column proto is:\n",
            "tcp     583134\n",
            "udp     408193\n",
            "icmp     17421\n",
            "Name: proto, dtype: int64\n",
            "Total unique values sum for column service is:\n",
            "-       1005507\n",
            "http       3238\n",
            "dhcp          1\n",
            "ssh           1\n",
            "dns           1\n",
            "Name: service, dtype: int64\n",
            "Total unique values sum for column duration is:\n",
            "-            796300\n",
            "2.998791       4494\n",
            "2.998792       4342\n",
            "2.998790       4167\n",
            "2.998793       3836\n",
            "              ...  \n",
            "11.675080         1\n",
            "11.743790         1\n",
            "11.780521         1\n",
            "11.847977         1\n",
            "6.339738          1\n",
            "Name: duration, Length: 16650, dtype: int64\n",
            "Total unique values sum for column orig_bytes is:\n",
            "-      796300\n",
            "0      195511\n",
            "48       6518\n",
            "18       3315\n",
            "180       834\n",
            "        ...  \n",
            "172         1\n",
            "155         1\n",
            "560         1\n",
            "540         1\n",
            "640         1\n",
            "Name: orig_bytes, Length: 171, dtype: int64\n",
            "Total unique values sum for column resp_bytes is:\n",
            "-      796300\n",
            "0      198819\n",
            "48       6487\n",
            "144       766\n",
            "27        458\n",
            "        ...  \n",
            "451         1\n",
            "554         1\n",
            "440         1\n",
            "552         1\n",
            "465         1\n",
            "Name: resp_bytes, Length: 479, dtype: int64\n",
            "Total unique values sum for column conn_state is:\n",
            "S0        971229\n",
            "OTH        17421\n",
            "SF         13881\n",
            "REJ         4346\n",
            "RSTR        1465\n",
            "RSTOS0       197\n",
            "RSTRH         66\n",
            "RSTO          47\n",
            "S2            40\n",
            "SH            29\n",
            "S1            27\n",
            "Name: conn_state, dtype: int64\n",
            "Total unique values sum for column local_orig is:\n",
            "-    1008748\n",
            "Name: local_orig, dtype: int64\n",
            "Total unique values sum for column local_resp is:\n",
            "-    1008748\n",
            "Name: local_resp, dtype: int64\n",
            "Total unique values sum for column missed_bytes is:\n",
            "0.0    1008748\n",
            "Name: missed_bytes, dtype: int64\n",
            "Total unique values sum for column history is:\n",
            "S           569605\n",
            "D           401606\n",
            "-            17421\n",
            "Dd            6569\n",
            "Sr            4346\n",
            "             ...  \n",
            "ShADdfR          1\n",
            "FfA              1\n",
            "ShAFafR          1\n",
            "HaDdAFTf         1\n",
            "^hADr            1\n",
            "Name: history, Length: 126, dtype: int64\n",
            "Total unique values sum for column orig_pkts is:\n",
            "1.0     806724\n",
            "3.0     192106\n",
            "5.0       2237\n",
            "2.0       1187\n",
            "4.0       1072\n",
            "8.0        571\n",
            "28.0       416\n",
            "29.0       320\n",
            "27.0       292\n",
            "24.0       271\n",
            "11.0       245\n",
            "12.0       244\n",
            "26.0       237\n",
            "23.0       236\n",
            "25.0       210\n",
            "15.0       202\n",
            "13.0       189\n",
            "9.0        186\n",
            "6.0        165\n",
            "7.0        162\n",
            "22.0       131\n",
            "18.0       131\n",
            "30.0       128\n",
            "19.0       118\n",
            "10.0       105\n",
            "45.0       101\n",
            "14.0        94\n",
            "21.0        92\n",
            "20.0        81\n",
            "31.0        68\n",
            "44.0        64\n",
            "16.0        56\n",
            "0.0         45\n",
            "17.0        32\n",
            "32.0        31\n",
            "34.0        25\n",
            "35.0        22\n",
            "33.0        21\n",
            "37.0        20\n",
            "43.0        20\n",
            "38.0        17\n",
            "36.0        16\n",
            "46.0        14\n",
            "39.0        11\n",
            "40.0         9\n",
            "41.0         7\n",
            "42.0         4\n",
            "47.0         4\n",
            "50.0         3\n",
            "48.0         2\n",
            "51.0         1\n",
            "49.0         1\n",
            "52.0         1\n",
            "60.0         1\n",
            "Name: orig_pkts, dtype: int64\n",
            "Total unique values sum for column orig_ip_bytes is:\n",
            "40.0      399801\n",
            "60.0      382628\n",
            "180.0     190632\n",
            "68.0        8678\n",
            "76.0        8462\n",
            "           ...  \n",
            "1209.0         1\n",
            "811.0          1\n",
            "1073.0         1\n",
            "394.0          1\n",
            "948.0          1\n",
            "Name: orig_ip_bytes, Length: 1249, dtype: int64\n",
            "Total unique values sum for column resp_pkts is:\n",
            "0.0     988854\n",
            "1.0      10885\n",
            "5.0       1468\n",
            "4.0       1410\n",
            "6.0        658\n",
            "         ...  \n",
            "49.0         1\n",
            "72.0         1\n",
            "51.0         1\n",
            "45.0         1\n",
            "46.0         1\n",
            "Name: resp_pkts, Length: 69, dtype: int64\n",
            "Total unique values sum for column resp_ip_bytes is:\n",
            "0.0       988854\n",
            "76.0        6487\n",
            "40.0        4208\n",
            "216.0        953\n",
            "412.0        603\n",
            "           ...  \n",
            "604.0          1\n",
            "2438.0         1\n",
            "2272.0         1\n",
            "4008.0         1\n",
            "345.0          1\n",
            "Name: resp_ip_bytes, Length: 1141, dtype: int64\n",
            "Total unique values sum for column tunnel_parents is:\n",
            "-    1008748\n",
            "Name: tunnel_parents, dtype: int64\n",
            "Total unique values sum for column label is:\n",
            "Malicious    539473\n",
            "Benign       469275\n",
            "Name: label, dtype: int64\n",
            "Total unique values sum for column detailed-label is:\n",
            "PartOfAHorizontalPortScan    539465\n",
            "-                            469275\n",
            "C&C                               8\n",
            "Name: detailed-label, dtype: int64\n",
            "Total unique values sum for column ts is:\n",
            "1.525880e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "               ..\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526283e+09    1\n",
            "Name: ts, Length: 1008748, dtype: int64\n",
            "Total unique values sum for column uid is:\n",
            "CUmrqr4svHuSXJy5z7    1\n",
            "COj4Eq4lmR86amgfI6    1\n",
            "CBqL9l4KOG0Y3zauml    1\n",
            "CLIXVIOgCuf9Pv6j      1\n",
            "CImdWC3kB3Zhudx4q3    1\n",
            "                     ..\n",
            "CIEU4w1lSTwgbUFwHe    1\n",
            "CaXPwRJbmRa0E2N32     1\n",
            "CHMbUQ2DPEF61n2Chf    1\n",
            "CwWZ3m2uWO6qokLuu4    1\n",
            "CoOjVZ1zw3pWf7a9n5    1\n",
            "Name: uid, Length: 1008748, dtype: int64\n",
            "Total unique values sum for column id.orig_h is:\n",
            "192.168.100.103    991061\n",
            "192.168.100.1        1651\n",
            "4.68.110.10            43\n",
            "194.70.98.42           23\n",
            "218.248.235.161        13\n",
            "                    ...  \n",
            "203.233.35.193          1\n",
            "84.61.235.57            1\n",
            "209.180.197.181         1\n",
            "119.166.138.30          1\n",
            "255.176.39.22           1\n",
            "Name: id.orig_h, Length: 15004, dtype: int64\n",
            "Total unique values sum for column id.orig_p is:\n",
            "43763.0    399650\n",
            "3.0         14516\n",
            "123.0        8551\n",
            "11.0         2903\n",
            "23.0          158\n",
            "            ...  \n",
            "41861.0         1\n",
            "33950.0         1\n",
            "47075.0         1\n",
            "4.0             1\n",
            "448.0           1\n",
            "Name: id.orig_p, Length: 28243, dtype: int64\n",
            "Total unique values sum for column id.resp_h is:\n",
            "192.168.100.103    17687\n",
            "147.231.100.5       4313\n",
            "213.239.154.12      1428\n",
            "37.187.104.44       1408\n",
            "89.221.214.130      1402\n",
            "                   ...  \n",
            "48.19.58.31            1\n",
            "213.71.0.16            1\n",
            "241.61.159.121         1\n",
            "88.236.105.181         1\n",
            "23.70.168.160          1\n",
            "Name: id.resp_h, Length: 597107, dtype: int64\n",
            "Total unique values sum for column id.resp_p is:\n",
            "23.0       271561\n",
            "8080.0     135382\n",
            "2323.0      88184\n",
            "9527.0      44338\n",
            "123.0        8553\n",
            "            ...  \n",
            "10962.0         1\n",
            "43527.0         1\n",
            "14119.0         1\n",
            "45660.0         1\n",
            "688.0           1\n",
            "Name: id.resp_p, Length: 65426, dtype: int64\n",
            "Total unique values sum for column proto is:\n",
            "tcp     583134\n",
            "udp     408193\n",
            "icmp     17421\n",
            "Name: proto, dtype: int64\n",
            "Total unique values sum for column service is:\n",
            "-       1005507\n",
            "http       3238\n",
            "dhcp          1\n",
            "ssh           1\n",
            "dns           1\n",
            "Name: service, dtype: int64\n",
            "Total unique values sum for column duration is:\n",
            "-            796300\n",
            "2.998791       4494\n",
            "2.998792       4342\n",
            "2.998790       4167\n",
            "2.998793       3836\n",
            "              ...  \n",
            "11.675080         1\n",
            "11.743790         1\n",
            "11.780521         1\n",
            "11.847977         1\n",
            "6.339738          1\n",
            "Name: duration, Length: 16650, dtype: int64\n",
            "Total unique values sum for column orig_bytes is:\n",
            "-      796300\n",
            "0      195511\n",
            "48       6518\n",
            "18       3315\n",
            "180       834\n",
            "        ...  \n",
            "172         1\n",
            "155         1\n",
            "560         1\n",
            "540         1\n",
            "640         1\n",
            "Name: orig_bytes, Length: 171, dtype: int64\n",
            "Total unique values sum for column resp_bytes is:\n",
            "-      796300\n",
            "0      198819\n",
            "48       6487\n",
            "144       766\n",
            "27        458\n",
            "        ...  \n",
            "451         1\n",
            "554         1\n",
            "440         1\n",
            "552         1\n",
            "465         1\n",
            "Name: resp_bytes, Length: 479, dtype: int64\n",
            "Total unique values sum for column conn_state is:\n",
            "S0        971229\n",
            "OTH        17421\n",
            "SF         13881\n",
            "REJ         4346\n",
            "RSTR        1465\n",
            "RSTOS0       197\n",
            "RSTRH         66\n",
            "RSTO          47\n",
            "S2            40\n",
            "SH            29\n",
            "S1            27\n",
            "Name: conn_state, dtype: int64\n",
            "Total unique values sum for column local_orig is:\n",
            "-    1008748\n",
            "Name: local_orig, dtype: int64\n",
            "Total unique values sum for column local_resp is:\n",
            "-    1008748\n",
            "Name: local_resp, dtype: int64\n",
            "Total unique values sum for column missed_bytes is:\n",
            "0.0    1008748\n",
            "Name: missed_bytes, dtype: int64\n",
            "Total unique values sum for column history is:\n",
            "S           569605\n",
            "D           401606\n",
            "-            17421\n",
            "Dd            6569\n",
            "Sr            4346\n",
            "             ...  \n",
            "ShADdfR          1\n",
            "FfA              1\n",
            "ShAFafR          1\n",
            "HaDdAFTf         1\n",
            "^hADr            1\n",
            "Name: history, Length: 126, dtype: int64\n",
            "Total unique values sum for column orig_pkts is:\n",
            "1.0     806724\n",
            "3.0     192106\n",
            "5.0       2237\n",
            "2.0       1187\n",
            "4.0       1072\n",
            "8.0        571\n",
            "28.0       416\n",
            "29.0       320\n",
            "27.0       292\n",
            "24.0       271\n",
            "11.0       245\n",
            "12.0       244\n",
            "26.0       237\n",
            "23.0       236\n",
            "25.0       210\n",
            "15.0       202\n",
            "13.0       189\n",
            "9.0        186\n",
            "6.0        165\n",
            "7.0        162\n",
            "22.0       131\n",
            "18.0       131\n",
            "30.0       128\n",
            "19.0       118\n",
            "10.0       105\n",
            "45.0       101\n",
            "14.0        94\n",
            "21.0        92\n",
            "20.0        81\n",
            "31.0        68\n",
            "44.0        64\n",
            "16.0        56\n",
            "0.0         45\n",
            "17.0        32\n",
            "32.0        31\n",
            "34.0        25\n",
            "35.0        22\n",
            "33.0        21\n",
            "37.0        20\n",
            "43.0        20\n",
            "38.0        17\n",
            "36.0        16\n",
            "46.0        14\n",
            "39.0        11\n",
            "40.0         9\n",
            "41.0         7\n",
            "42.0         4\n",
            "47.0         4\n",
            "50.0         3\n",
            "48.0         2\n",
            "51.0         1\n",
            "49.0         1\n",
            "52.0         1\n",
            "60.0         1\n",
            "Name: orig_pkts, dtype: int64\n",
            "Total unique values sum for column orig_ip_bytes is:\n",
            "40.0      399801\n",
            "60.0      382628\n",
            "180.0     190632\n",
            "68.0        8678\n",
            "76.0        8462\n",
            "           ...  \n",
            "1209.0         1\n",
            "811.0          1\n",
            "1073.0         1\n",
            "394.0          1\n",
            "948.0          1\n",
            "Name: orig_ip_bytes, Length: 1249, dtype: int64\n",
            "Total unique values sum for column resp_pkts is:\n",
            "0.0     988854\n",
            "1.0      10885\n",
            "5.0       1468\n",
            "4.0       1410\n",
            "6.0        658\n",
            "         ...  \n",
            "49.0         1\n",
            "72.0         1\n",
            "51.0         1\n",
            "45.0         1\n",
            "46.0         1\n",
            "Name: resp_pkts, Length: 69, dtype: int64\n",
            "Total unique values sum for column resp_ip_bytes is:\n",
            "0.0       988854\n",
            "76.0        6487\n",
            "40.0        4208\n",
            "216.0        953\n",
            "412.0        603\n",
            "           ...  \n",
            "604.0          1\n",
            "2438.0         1\n",
            "2272.0         1\n",
            "4008.0         1\n",
            "345.0          1\n",
            "Name: resp_ip_bytes, Length: 1141, dtype: int64\n",
            "Total unique values sum for column tunnel_parents is:\n",
            "-    1008748\n",
            "Name: tunnel_parents, dtype: int64\n",
            "Total unique values sum for column label is:\n",
            "Malicious    539473\n",
            "Benign       469275\n",
            "Name: label, dtype: int64\n",
            "Total unique values sum for column detailed-label is:\n",
            "PartOfAHorizontalPortScan    539465\n",
            "-                            469275\n",
            "C&C                               8\n",
            "Name: detailed-label, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Checking all the unique values in the data\n",
        "col = df.columns\n",
        "for i in col:\n",
        "  print(f\"Total unique values sum for column {i} is:\")\n",
        "  print(df[i].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NZPXxZUjjs_",
        "outputId": "815c8569-3b6a-4d40-94fd-0eb6c9415aac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1008748 entries, 0 to 1008747\n",
            "Data columns (total 23 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   ts              1008748 non-null  float64\n",
            " 1   uid             1008748 non-null  object \n",
            " 2   id.orig_h       1008748 non-null  object \n",
            " 3   id.orig_p       1008748 non-null  float64\n",
            " 4   id.resp_h       1008748 non-null  object \n",
            " 5   id.resp_p       1008748 non-null  float64\n",
            " 6   proto           1008748 non-null  object \n",
            " 7   service         1008748 non-null  object \n",
            " 8   duration        1008748 non-null  object \n",
            " 9   orig_bytes      1008748 non-null  object \n",
            " 10  resp_bytes      1008748 non-null  object \n",
            " 11  conn_state      1008748 non-null  object \n",
            " 12  local_orig      1008748 non-null  object \n",
            " 13  local_resp      1008748 non-null  object \n",
            " 14  missed_bytes    1008748 non-null  float64\n",
            " 15  history         1008748 non-null  object \n",
            " 16  orig_pkts       1008748 non-null  float64\n",
            " 17  orig_ip_bytes   1008748 non-null  float64\n",
            " 18  resp_pkts       1008748 non-null  float64\n",
            " 19  resp_ip_bytes   1008748 non-null  float64\n",
            " 20  tunnel_parents  1008748 non-null  object \n",
            " 21  label           1008748 non-null  object \n",
            " 22  detailed-label  1008748 non-null  object \n",
            "dtypes: float64(8), object(15)\n",
            "memory usage: 177.0+ MB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1008748 entries, 0 to 1008747\n",
            "Data columns (total 23 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   ts              1008748 non-null  float64\n",
            " 1   uid             1008748 non-null  object \n",
            " 2   id.orig_h       1008748 non-null  object \n",
            " 3   id.orig_p       1008748 non-null  float64\n",
            " 4   id.resp_h       1008748 non-null  object \n",
            " 5   id.resp_p       1008748 non-null  float64\n",
            " 6   proto           1008748 non-null  object \n",
            " 7   service         1008748 non-null  object \n",
            " 8   duration        1008748 non-null  object \n",
            " 9   orig_bytes      1008748 non-null  object \n",
            " 10  resp_bytes      1008748 non-null  object \n",
            " 11  conn_state      1008748 non-null  object \n",
            " 12  local_orig      1008748 non-null  object \n",
            " 13  local_resp      1008748 non-null  object \n",
            " 14  missed_bytes    1008748 non-null  float64\n",
            " 15  history         1008748 non-null  object \n",
            " 16  orig_pkts       1008748 non-null  float64\n",
            " 17  orig_ip_bytes   1008748 non-null  float64\n",
            " 18  resp_pkts       1008748 non-null  float64\n",
            " 19  resp_ip_bytes   1008748 non-null  float64\n",
            " 20  tunnel_parents  1008748 non-null  object \n",
            " 21  label           1008748 non-null  object \n",
            " 22  detailed-label  1008748 non-null  object \n",
            "dtypes: float64(8), object(15)\n",
            "memory usage: 177.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMEbnCVrPY1n"
      },
      "source": [
        "**Observation(Data Understanding)**\n",
        "\n",
        "a)--> Columns ts(timestamps) and uid(unique id ) contains all the unique values which will not be usefull for our model.\n",
        "\n",
        "b)--> Most of the columns contains 0.0 in many of the instances the instances.\n",
        "\n",
        "c)-->All the columns which contains \"-\" in their column are null.\n",
        "\n",
        "d)-->Column 'detailed-label' is the part of our target variable we will not consider this variable.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5WxPyox1cVz"
      },
      "source": [
        "###Missing Values Handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "u7k0PoZKbfge"
      },
      "outputs": [],
      "source": [
        "#a)Dropping ts and uid\n",
        "df = df.drop(columns=['ts','uid'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oubLLc-AjGOY",
        "outputId": "65536454-c7a4-4263-ba72-0da59ab1e273"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with 0.0 \n",
            "['id.resp_p', 'missed_bytes', 'orig_pkts', 'orig_ip_bytes', 'resp_pkts', 'resp_ip_bytes']\n"
          ]
        }
      ],
      "source": [
        "#b) Further working for columns which contains 0.0\n",
        "missing_val_col = []\n",
        "\n",
        "# Iterate through each column in the DataFrame\n",
        "for column in df.columns:\n",
        "    # Check if the column contains 0.0\n",
        "    if 0.0 in df[column].unique():\n",
        "        missing_val_col.append(column)\n",
        "\n",
        "# Print the list of column names that contain 0.0\n",
        "print(\"Columns with 0.0 \")\n",
        "print(missing_val_col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_mR_qHDjGHh",
        "outputId": "83a3e8a3-4d54-4678-963d-89924775633e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column 'id.resp_p' has 0.33% 0.0 values.\n",
            "Column 'missed_bytes' has 100.00% 0.0 values.\n",
            "Column 'orig_pkts' has 0.00% 0.0 values.\n",
            "Column 'orig_ip_bytes' has 0.00% 0.0 values.\n",
            "Column 'resp_pkts' has 98.03% 0.0 values.\n",
            "Column 'resp_ip_bytes' has 98.03% 0.0 values.\n"
          ]
        }
      ],
      "source": [
        "# Initialize a dictionary to store the percentage of 0.0 values for each column\n",
        "percentage_dict = {}\n",
        "\n",
        "# Calculate the percentage of \"-\" values for each column\n",
        "for column in missing_val_col:\n",
        "    percentage = (df[column] == 0.0).mean() * 100\n",
        "    percentage_dict[column] = percentage\n",
        "\n",
        "# Print the percentage of \"-\" values for each column\n",
        "for column, percentage in percentage_dict.items():\n",
        "    print(f\"Column '{column}' has {percentage:.2f}% 0.0 values.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "IWpmHoCLmEe0"
      },
      "outputs": [],
      "source": [
        "# Dropping all the columns which higher amount of 0.0 values\n",
        "df = df.drop(columns=['missed_bytes','resp_pkts','resp_ip_bytes'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZ82zPS3QDB4",
        "outputId": "09a569aa-e3e2-463d-9ba1-b3fa8676eccd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-eaf727ef3804>:7: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if '-' in df[column].unique():\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with '-' character:\n",
            "['service', 'duration', 'orig_bytes', 'resp_bytes', 'local_orig', 'local_resp', 'history', 'tunnel_parents', 'detailed-label']\n"
          ]
        }
      ],
      "source": [
        "#c) Further working for columns which contains \"-\"\n",
        "missing_val_col_2 = []\n",
        "\n",
        "# Iterate through each column in the DataFrame\n",
        "for column in df.columns:\n",
        "    # Check if the column contains \"-\"\n",
        "    if '-' in df[column].unique():\n",
        "        missing_val_col_2.append(column)\n",
        "\n",
        "# Print the list of column names that contain \"-\"\n",
        "print(\"Columns with '-' character:\")\n",
        "print(missing_val_col_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EpJ8bCQCQ9yG",
        "outputId": "492318e2-0571-4252-e18c-04a18b22f664"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column 'service' has 99.68% '-' values.\n",
            "Column 'duration' has 78.94% '-' values.\n",
            "Column 'orig_bytes' has 78.94% '-' values.\n",
            "Column 'resp_bytes' has 78.94% '-' values.\n",
            "Column 'local_orig' has 100.00% '-' values.\n",
            "Column 'local_resp' has 100.00% '-' values.\n",
            "Column 'history' has 1.73% '-' values.\n",
            "Column 'tunnel_parents' has 100.00% '-' values.\n",
            "Column 'detailed-label' has 46.52% '-' values.\n"
          ]
        }
      ],
      "source": [
        "# Initialize a dictionary to store the percentage of \"-\" values for each column\n",
        "percentage_dict_2 = {}\n",
        "\n",
        "# Calculate the percentage of \"-\" values for each column\n",
        "for column in missing_val_col_2:\n",
        "    percentage = (df[column] == '-').mean() * 100\n",
        "    percentage_dict_2[column] = percentage\n",
        "\n",
        "# Print the percentage of \"-\" values for each column\n",
        "for column, percentage in percentage_dict_2.items():\n",
        "    print(f\"Column '{column}' has {percentage:.2f}% '-' values.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ei1WSsZKTbV9"
      },
      "source": [
        "Dropping all the columns contains \"-\" and 0.0 values except 'history'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "nRhsAZHvTrBO"
      },
      "outputs": [],
      "source": [
        "# Dropping all the columns which have \"-\" values\n",
        "\n",
        "# Keep only the \"history\" column and drop all other columns in 'missing_val_col'\n",
        "columns_to_drop = [col for col in missing_val_col_2 if col != \"history\"]\n",
        "df = df.drop(columns=columns_to_drop)\n",
        "\n",
        "# Now 'df' will contain only the \"history\" column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuG2mGpGY_6i"
      },
      "source": [
        "###Imputing value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "6dj9hvvsY3Qp"
      },
      "outputs": [],
      "source": [
        "# Calculate the mode of the \"history\" column\n",
        "mode_value = df['history'].mode().iloc[0]\n",
        "\n",
        "# Replace \"-\" values with the mode\n",
        "df['history'].replace('-', mode_value, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Basic EDA"
      ],
      "metadata": {
        "id": "AL_G8xq3vPTq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a DataFrame named 'df' with a 'proto' column\n",
        "\n",
        "# Get unique values in the 'proto' column\n",
        "unique_values = df['proto'].unique()\n",
        "\n",
        "# Count the occurrences of each unique value\n",
        "value_counts = df['proto'].value_counts()\n",
        "\n",
        "# Create a bar plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(unique_values, value_counts)\n",
        "plt.xlabel('Protocol')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Unique Values in \"proto\" Column')\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Add values on top of the bars\n",
        "for bar, value in zip(bars, value_counts):\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2 - 0.1, bar.get_height() + 0.2, value, ha='center')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "CqjeXr60v0Um",
        "outputId": "d3932fd6-6e5a-45bc-b8e8-5ec50137ecdb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a DataFrame named 'df' with 'proto' and 'target' columns\n",
        "\n",
        "# Create a bar plot showing the count of each unique combination of 'proto' and 'target'\n",
        "plt.figure(figsize=(12, 6))\n",
        "ax = sns.countplot(data=df, x='proto', hue='label')\n",
        "plt.xlabel('Protocol')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Count of Unique Combinations of \"proto\" and \"target\"')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(title='Target', loc='upper right')\n",
        "\n",
        "# Annotate each bar with the percentage value\n",
        "total = len(df)\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x() + p.get_width() / 2., height + 3, '{:.1f}%'.format(height / total * 100), ha=\"center\")\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "od47qykmxx9p",
        "outputId": "e7f4a650-4245-4678-b0af-eed27c7d6085"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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Hjh3t/sqcE3XfzfOaZP+5OX/+vM6cOaPGjRvr0qVL2rdvn11bLy8v/fe//zWfu7m56YEHHrD7HK5cuVLFihXTU089ZS7Lnz+/evXqdUd13oyzs7M5Z0B6errOnj2r1NRU1a9fP8vP/7XvVVpamtatW6f27durePHi5vLy5cuboyIyLF26VOnp6XrmmWfszlMBAQGqUKFCpksZACArhAMAcAu8vb0l/fsLa3YcPXpUTk5OKl++vN3ygIAA+fr63vYXeUl2X4AlmV+AbuULztWOHj2q4sWLZ/oimzG8+05qvZlrr5318PDINGy2YMGCNzy206dP6/Lly6pQoUKmdZUqVbqtuk6fPq34+HjNmTNHfn5+do+uXbtK+r/J5rJyK5+X06dP69KlS1nWWqVKFaWnp9tdOyxl/gz4+PhIkgIDAzMtz+q1y+q1qlixYrautb9231Lm9+jy5csaNWqUOYdFkSJF5Ofnp/j4eCUkJNx0H1k5evSoKlSoICcn+19hrvc5vdnPibu7u95++22tWrVK/v7+atKkiSZOnKjY2Nib1iFl/dmqUqWKzpw5o4sXL97CkWWfk5OTXYAk/fu+Scr03pUtW9bueU7UfTfPa9K/l9c8+eST8vHxkbe3t/z8/MwA4NrPTcmSJTOdP679HB49elTly5fP1O52zwu3Yv78+apZs6Y8PDxUuHBh+fn5acWKFVl+/q99r06dOqXLly9nep0lZVp28OBBGYahChUqZDpX7d2794bnKQDIwJwDAHALvL29Vbx48UyTtt1MVhNHZde1k3BlcHZ2znK5cZ2J5xzFw8NDly9fznLdpUuXzDZXu96x5ZTrvR/Xvtbp6emSpP/+978KCwvLcpuaNWtedz+VK1eW9O9cCNdeX5wTrvc6ZbU8pz8X2fn89e/fX3PnztXAgQMVHBwsHx8f2Ww2derUyXxt77bs1Dlw4EC1a9dOy5Yt05o1a/T6669rwoQJWr9+verUqZMrdd4tV/8VPqfdyXnteuLj4/XII4/I29tb48aNU1BQkDw8PLR9+3YNHz480+cmL58HP/vsM3Xp0kXt27fX0KFDVbRoUTk7O2vChAk6fPhwpvZ38l6lp6fLZrNp1apVWb4mzCsAIDsIBwDgFrVt21Zz5sxRZGSkgoODb9i2dOnSSk9P18GDB+0mWIuLi1N8fLxKly5tLitYsKDi4+Pttk9OTtbJkydvu9Zb+eW9dOnSWrdunc6fP283eiBjGO/Vtd6K0qVLm7NuX2v//v131PfV/Pz8lC9fPh08ePC6+8mQ8dfja1/va//i6efnpwIFCigtLU0hISG3XFPr1q3l7Oyszz777KaTEvr5+Sl//vyZapX+fQ+cnJwyjQi4U1m9VgcOHMix2cu/+uorhYWFafLkyeayK1euZHrdb/VzunPnTqWnp9uNHrjTz2lQUJBefvllvfzyyzp48KBq166tyZMn67PPPrtuHVLmz1ZGLUWKFJGnp+dt1XIz6enp+uuvv8zRAtK/75ukm753t1L39d6XWzmv3aqNGzfqn3/+0dKlS9WkSRNzeXR09G33Wbp0ae3evVuGYdgdU1avwe243uv01VdfqVy5clq6dKldm9GjR2er36JFi8rDw0OHDh3KtO7aZUFBQTIMQ2XLlrX7XNxKvQDAZQUAcIuGDRsmT09P9ejRQ3FxcZnWHz58WFOnTpUkPfbYY5L+vXPA1d59911JUps2bcxlQUFB+vHHH+3azZkz57ojB7LD09Mz0xex63nssceUlpam6dOn2y1/7733ZLPZMl3jml2PPfaYjh8/rmXLltktT0pK0kcffaSiRYuqbt26t9X31ZydnRUaGqply5YpJibGXL53716tWbPGrq23t7eKFCmS6fWeOXNmpj47duyor7/+OsvRIlffOjIrgYGB6tmzp3744QdNmzYt0/r09HRNnjxZx48fl7Ozs1q2bKlvv/3Wbmh4XFycFi5cqIcffti8TCGnLFu2TH///bf5/Ndff9XWrVtv+72+lrOzc6a/4E6bNi3TZzrjy2h2PquPPfaYYmNjzbtySP/eVm/atGny8vIybxuYXZcuXdKVK1fslgUFBalAgQI3vNVdsWLFVLt2bc2fP9+u7t27d+uHH34wf/bvlqt/Tg3D0PTp0+Xq6qrmzZvfcLtbqft678utnNduVcZfva/+3CQnJ2f62bwVjz32mE6cOKGvvvrKXHbp0iXNmTPntvu8mqenZ5aXCWR1LFu3blVkZGS2+nV2dlZISIiWLVumEydOmMsPHTqUaQ6YDh06yNnZWWPHjs30M2cYhv7555+b1gsAjBwAgFsUFBSkhQsX6tlnn1WVKlXUuXNnVa9eXcnJydqyZYt5WzVJqlWrlsLCwjRnzhxzuOyvv/6q+fPnq3379mrWrJnZb48ePdSnTx917NhRLVq00B9//KE1a9aYtxC7HfXq1dMHH3yg8ePHq3z58ipatKgeffTRLNu2a9dOzZo102uvvaYjR46oVq1a+uGHH/Ttt99q4MCBdpPP3YpevXrpk08+0dNPP61u3bqpTp06+ueff7Ro0SLt3r1bCxYsMCftulNjx47V6tWr1bhxY7344ovml8Zq1app586ddm179Oiht956Sz169FD9+vX1448/mn99vdpbb72lDRs2qGHDhurZs6eqVq2qs2fPavv27Vq3bp3Onj17w5omT56sw4cPa8CAAVq6dKnatm2rggULKiYmRkuWLNG+ffvUqVMnSdL48eO1du1aPfzww3rxxRfl4uKi2bNnKykpKct7t9+p8uXL6+GHH1bfvn2VlJSkKVOmqHDhwho2bFiO9N+2bVt9+umn8vHxUdWqVRUZGal169aZtzrMULt2bTk7O+vtt99WQkKC3N3d9eijj6po0aKZ+uzVq5dmz56tLl26KCoqSmXKlNFXX32lzZs3a8qUKdmeLDTDgQMH1Lx5cz3zzDOqWrWqXFxc9M033yguLs58X65n0qRJat26tYKDg9W9e3fzloA+Pj4aM2bMLdVxKzw8PLR69WqFhYWpYcOGWrVqlVasWKFXX301y1vc3W7d9erVk/Tv7fo6deokV1dXtWvX7pbOa7fqoYceUsGCBRUWFqYBAwbIZrPp008/vaPLBHr27Knp06erc+fOioqKUrFixfTpp58qf/78t93n1erVq6dFixZp8ODBatCggby8vNSuXTu1bdtWS5cu1ZNPPqk2bdooOjpas2bNUtWqVXXhwoVs9T1mzBj98MMPatSokfr27WsGuNWrV9eOHTvMdkFBQRo/frxGjBihI0eOqH379ipQoICio6P1zTffqFevXhoyZMgN6wUAbmUIALfpwIEDRs+ePY0yZcoYbm5uRoECBYxGjRoZ06ZNs7vdVkpKijF27FijbNmyhqurqxEYGGiMGDHCro1hGEZaWpoxfPhwo0iRIkb+/PmN0NBQ49ChQ9e9leG2bdvsts+4XdrVt+WKjY012rRpYxQoUMCQdNPbGp4/f94YNGiQUbx4ccPV1dWoUKGCMWnSJLvb5RnGrd3K0DD+vX3XoEGDzNfA29vbaNasmbFq1apMbcPCwgxPT89My693S71rb/+4adMmo169eoabm5tRrlw5Y9asWVlue+nSJaN79+6Gj4+PUaBAAeOZZ54xTp06lWWfcXFxRnh4uBEYGGi4uroaAQEBRvPmzY05c+Zk6/hTU1ONjz76yGjcuLHh4+NjuLq6GqVLlza6du2a6TaH27dvN0JDQw0vLy8jf/78RrNmzYwtW7bYtbneZyDjODNuZ5fh2tc049ZpkyZNMiZPnmwEBgYa7u7uRuPGjY0//vgjyz6vJskIDw/PdJzXflbPnTtndO3a1ShSpIjh5eVlhIaGGvv27cvUzjAM48MPPzTKlStn3nYy43N87a0MDePf9yOjXzc3N6NGjRqZbkt59TFe6+r3+MyZM0Z4eLhRuXJlw9PT0/Dx8TEaNmxod9u7G1m3bp3RqFEjI1++fIa3t7fRrl07488//7Rrk9O3MvT09DQOHz5stGzZ0sifP7/h7+9vjB492khLSzPb3ej4s1u3YRjGG2+8YZQoUcJwcnKyu61hds9rt2Pz5s3Ggw8+aOTLl88oXry4MWzYMGPNmjWZzm/XOw+FhYVlulXf0aNHjccff9zInz+/UaRIEeOll14yVq9enSO3Mrxw4YLxn//8x/D19TUkmftOT0833nzzTaN06dKGu7u7UadOHWP58uWZ6rvZexUREWHUqVPHcHNzM4KCgoyPPvrIePnllw0PD49Mbb/++mvj4YcfNjw9PQ1PT0+jcuXKRnh4uLF///6b1gsANsPIAzO2AABwF40ZMybL4bbAvaZLly766quvsv2XZ9yf2rdvrz179mQ5bwgA3C7mHAAAAADyqGvv9nLw4EGtXLlSTZs2dUxBAO5bzDkAAAAA5FHlypVTly5dVK5cOR09elQffPCB3NzccmxuEADIQDgAAAAA5FGtWrXSF198odjYWLm7uys4OFhvvvmmKlSo4OjSANxnmHMAAAAAAACLY84BAAAAAAAsjnAAAAAAAACLY86BXJSenq4TJ06oQIECstlsji4HAAAAAHCfMwxD58+fV/HixeXkdP3xAYQDuejEiRMKDAx0dBkAAAAAAIs5duyYSpYsed31hAO5qECBApL+fVO8vb0dXA0AAAAA4H6XmJiowMBA8/vo9RAO5KKMSwm8vb0JBwAAAAAAueZml7YzISEAAAAAABZHOAAAAAAAgMURDgAAAAAAYHHMOQAAAAAAFpeenq7k5GRHl4Hb4OrqKmdn5zvuh3AAAAAAACwsOTlZ0dHRSk9Pd3QpuE2+vr4KCAi46aSDN0I4AAAAAAAWZRiGTp48KWdnZwUGBsrJiSvP7yWGYejSpUs6deqUJKlYsWK33RfhAAAAAABYVGpqqi5duqTixYsrf/78ji4HtyFfvnySpFOnTqlo0aK3fYkBsRAAAAAAWFRaWpokyc3NzcGV4E5kBDspKSm33QfhAAAAAABY3J1cqw7Hy4n3j3AAecqYMWNks9nsHpUrVzbX9+7dW0FBQcqXL5/8/Pz0xBNPaN++fTfss0uXLpn6bNWqlbk+KSlJL7zwgry9vVWxYkWtW7fObvtJkyapf//+OXugAAAAAJCHMOcA8pxq1arZfUF3cfm/j2m9evX0/PPPq1SpUjp79qzGjBmjli1bKjo6+obX1rRq1Upz5841n7u7u5v/P2fOHEVFRSkyMlKrVq3Sf/7zH8XFxclmsyk6Oloffvihfvvttxw+SgAAAADIOxg5gDzHxcVFAQEB5qNIkSLmul69eqlJkyYqU6aM6tatq/Hjx+vYsWM6cuTIDft0d3e367NgwYLmur179+rxxx9XtWrVFB4ertOnT+vMmTOSpL59++rtt9+Wt7f3XTlWAAAAAP/n2hG/1z7GjBnj0NqWLVvmsP3fbYQDyHMOHjyo4sWLq1y5cnr++ecVExOTZbuLFy9q7ty5Klu2rAIDA2/Y58aNG1W0aFFVqlRJffv21T///GOuq1Wrln7++WddvnxZa9asUbFixVSkSBF9/vnn8vDw0JNPPpmjxwcAAAAgaydPnjQfU6ZMkbe3t92yIUOG3FJ/ycnJd6nS+w/hAPKUhg0bat68eVq9erU++OADRUdHq3Hjxjp//rzZZubMmfLy8pKXl5dWrVqltWvX3nB21VatWmnBggWKiIjQ22+/rU2bNql169bmzKzdunVTrVq1VLVqVf3vf//T4sWLde7cOY0aNUrTpk3TyJEjVb58eYWGhurvv/++668BAAAAYFVXj/b18fGRzWYzn1+8eFHPP/+8/P395eXlpQYNGmSaL6xMmTJ644031LlzZ3l7e6tXr16SpA8//FCBgYHKnz+/nnzySb377rvy9fW12/bbb79V3bp15eHhoXLlymns2LFKTU01+5WkJ598UjabzXx+P7EZhmE4ugirSExMlI+PjxISEhimnk3x8fEqXbq03n33XXXv3l2SlJCQoFOnTunkyZN655139Pfff2vz5s3y8PDIVp9//fWXgoKCtG7dOjVv3jzLNl27dlXt2rVVtmxZvfrqq9q6dasmTpyo3bt36+uvv86x4wMAAAAc6cqVK4qOjlbZsmWz/ft0bpk3b54GDhyo+Ph4SdIff/yhX375RY0aNZK7u7sWLFigd955R/v371epUqUk/fslPuMPfe3bt5ckxcbGqkmTJnr77bf1+OOPa926dXr99deVlpZm9v3TTz+pbdu2ev/999W4cWMdPnxYvXr1UpcuXTR69GidPn1aRYsW1dy5c9WqVSs5OzvLz8/PAa9K1m70Pmb3eygjB5Cn+fr6qmLFijp06JC5zMfHRxUqVFCTJk301Vdfad++ffrmm2+y3We5cuVUpEgRuz6vtmHDBu3Zs0f9+vXTxo0b9dhjj8nT01PPPPOMNm7ceKeHBAAAAOA21KpVS71791b16tVVoUIFvfHGGwoKCtJ3331n1+7RRx/Vyy+/rKCgIAUFBWnatGlq3bq1hgwZoooVK+rFF19U69at7bYZO3asXnnlFYWFhalcuXJq0aKF3njjDc2ePVuSzCDA19dXAQEBeSoYyCmEA8jTLly4oMOHD6tYsWJZrjcMQ4ZhKCkpKdt9Hj9+XP/880+WfV65ckXh4eGaPXu2nJ2dlZaWppSUFElSSkqKeSkCAAAAgNx14cIFDRkyRFWqVJGvr6+8vLy0d+/eTHOU1a9f3+75/v379cADD9gtu/b5H3/8oXHjxpmXL3t5ealnz546efKkLl26dHcOKI8hHECeMmTIEG3atElHjhzRli1b9OSTT8rZ2VnPPfec/vrrL02YMEFRUVGKiYnRli1b9PTTTytfvnx67LHHzD4qV65sjiS4cOGChg4dql9++UVHjhxRRESEnnjiCXMOgWu98cYbeuyxx1SnTh1JUqNGjbR06VLt3LlT06dPV6NGjXLnhQAAAABgZ8iQIfrmm2/05ptv6qefftKOHTtUo0aNTJMOenp63nLfFy5c0NixY7Vjxw7zsWvXLh08eDDPXW5xt7jcvAmQe44fP67nnntO//zzj/z8/PTwww/rl19+kZ+fn1JSUvTTTz9pypQpOnfunPz9/dWkSRNt2bJFRYsWNfvYv3+/EhISJEnOzs7auXOn5s+fr/j4eBUvXlwtW7bUG2+8IXd3d7t97969W4sXL9aOHTvMZU899ZQ2btyoxo0bq1KlSlq4cGGuvA4AAAAA7G3evFldunQx7yZ24cKFm97SXJIqVaqkbdu22S279nndunW1f/9+lS9f/rr9uLq63tcjiZmQMBc5ekLCekMX5Po+4ThRkzo7ugQAAADkcffShIQdOnRQdHS05s6dK5vNptdff10bN25Ut27dNGXKFEn/Tkg4cOBADRw40Oxn8+bNatKkiSZNmqR27dpp/fr1eu2115SWlqZz585JktasWaO2bdtq5MiReuqpp+Tk5KQ//vhDu3fv1vjx4yVJFStWVEhIiEaNGiV3d3cVLFgwN1+OG2JCQgAAAACAJbz77rsqWLCgHnroIbVr106hoaGqW7fuTbdr1KiRZs2apXfffVe1atXS6tWrNWjQILsv0aGhoVq+fLl++OEHNWjQQA8++KDee+89lS5d2mwzefJkrV27VoGBgeZlyPcTRg7kIkYOIDcxcgAAAAA3k5dHDtxNPXv21L59+/TTTz85upQckRMjB5hzAAAAAABwX3vnnXfUokULeXp6atWqVZo/f75mzpzp6LLyFMIBAAAAAMB97ddff9XEiRN1/vx5lStXTu+//7569Ojh6LLyFMIBAAAAAMB9bfHixY4uIc9jQkIAAAAAACyOcAAAAAAAAIsjHAAAAAAAwOIIBwAAAAAAsDjCAQAAAAAALI5wAAAAAAAAiyMcAAAAAABY2pEjR2Sz2bRjxw5J0saNG2Wz2RQfH5+t7Zs2baqBAwfetfpyg4ujCwAAAAAA5C31hi7I1f1FTep8y9t06dJF8+fPV+/evTVr1iy7deHh4Zo5c6bCwsI0b968W+77oYce0smTJ+Xj45Ot9kuXLpWrq+st7ycvYeQAAAAAAOCeFBgYqC+//FKXL182l125ckULFy5UqVKlbrtfNzc3BQQEyGazZat9oUKFVKBAgdveX15AOAAAAAAAuCfVrVtXgYGBWrp0qbls6dKlKlWqlOrUqWMuW716tR5++GH5+vqqcOHCatu2rQ4fPnzdfrO6rGDz5s1q2rSp8ufPr4IFCyo0NFTnzp2TlPmygnPnzqlz584qWLCg8ufPr9atW+vgwYPm+jFjxqh27dp2+5wyZYrKlCljV8MDDzwgT09P+fr6qlGjRjp69OgtvkLZRzgAAAAAALhndevWTXPnzjWff/LJJ+ratatdm4sXL2rw4MH67bffFBERIScnJz355JNKT0/P1j527Nih5s2bq2rVqoqMjNTPP/+sdu3aKS0tLcv2Xbp00W+//abvvvtOkZGRMgxDjz32mFJSUrK1v9TUVLVv316PPPKIdu7cqcjISPXq1SvbIxluB3MOAAAAAADuWf/97381YsQI86/qmzdv1pdffqmNGzeabTp27Gi3zSeffCI/Pz/9+eefql69+k33MXHiRNWvX18zZ840l1WrVi3LtgcPHtR3332nzZs366GHHpIkff755woMDNSyZcv09NNP33R/iYmJSkhIUNu2bRUUFCRJqlKlyk23uxOMHAAAAAAA3LP8/PzUpk0bzZs3T3PnzlWbNm1UpEgRuzYHDx7Uc889p3Llysnb29scvh8TE5OtfWSMHMiOvXv3ysXFRQ0bNjSXFS5cWJUqVdLevXuz1UehQoXUpUsXhYaGql27dpo6dapOnjyZrW1vF+EAAAAAAOCe1q1bN82bN0/z589Xt27dMq1v166dzp49qw8//FBbt27V1q1bJUnJycnZ6j9fvnw5Wq+Tk5MMw7Bbdu0lB3PnzlVkZKQeeughLVq0SBUrVtQvv/ySo3XY1XTXegYAAAAAIBe0atVKycnJSklJUWhoqN26f/75R/v379fIkSPVvHlzValSxZxIMLtq1qypiIiIbLWtUqWKUlNTzQDi6hqqVq0q6d/RDrGxsXYBwY4dOzL1VadOHY0YMUJbtmxR9erVtXDhwluq+1YQDgAAAAAA7mnOzs7au3ev/vzzTzk7O9utK1iwoAoXLqw5c+bo0KFDWr9+vQYPHnxL/Y8YMULbtm3Tiy++qJ07d2rfvn364IMPdObMmUxtK1SooCeeeEI9e/bUzz//rD/++EP//e9/VaJECT3xxBOS/r27wenTpzVx4kQdPnxYM2bM0KpVq8w+oqOjNWLECEVGRuro0aP64YcfdPDgwbs67wATEgIAAAAA7ERN6uzoEm6Zt7d3lsudnJz05ZdfasCAAapevboqVaqk999/X02bNs123xUrVtQPP/ygV199VQ888IDy5cunhg0b6rnnnsuy/dy5c/XSSy+pbdu2Sk5OVpMmTbRy5Uq5urpK+nd0wcyZM/Xmm2/qjTfeUMeOHTVkyBDNmTNHkpQ/f37t27dP8+fP1z///KNixYopPDxcvXv3vrUX5RbYjGsvdMBdk5iYKB8fHyUkJFz3g3s31Ru6INf3Cce5F0/oAAAAyF1XrlxRdHS0ypYtKw8PD0eXg9t0o/cxu99DuawAAAAAAACLIxwAAAAAAMDiCAcAAAAAALA4wgEAAAAAACzOoeHAmDFjZLPZ7B6VK1c211+5ckXh4eEqXLiwvLy81LFjR8XFxdn1ERMTozZt2ih//vwqWrSohg4dqtTUVLs2GzduVN26deXu7q7y5ctr3rx5mWqZMWOGypQpIw8PDzVs2FC//vqr3frs1AIAAAAAwL3I4SMHqlWrppMnT5qPn3/+2Vw3aNAgff/991qyZIk2bdqkEydOqEOHDub6tLQ0tWnTRsnJydqyZYvmz5+vefPmadSoUWab6OhotWnTRs2aNdOOHTs0cOBA9ejRQ2vWrDHbLFq0SIMHD9b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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**\n",
        "\n",
        "--> This shows that if we take this column we will be geting the following result:\n",
        "\n",
        "1. Our model will give us our network is Benign with 100% accuracy for udp and icmp.\n",
        "\n",
        "2. For tcp as data is imbalance our model predict it as a malicious if we give as input as a prototype=\"tcp\".\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MejM18aVx5GY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "bj3n_MRO5Z_k",
        "outputId": "637c27bb-8f4a-4243-d7de-225a833c95ba"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               id.orig_h  id.orig_p        id.resp_h  id.resp_p proto  \\\n",
              "0        192.168.100.103    51524.0   65.127.233.163       23.0   tcp   \n",
              "1        192.168.100.103    56305.0    63.150.16.171       23.0   tcp   \n",
              "2        192.168.100.103    41101.0     111.40.23.49       23.0   tcp   \n",
              "3        192.168.100.103    60905.0  131.174.215.147       23.0   tcp   \n",
              "4        192.168.100.103    44301.0      91.42.47.63       23.0   tcp   \n",
              "...                  ...        ...              ...        ...   ...   \n",
              "1008743  192.168.100.103    43763.0    16.219.83.137    64906.0   udp   \n",
              "1008744  192.168.100.103    43763.0   100.57.245.196    39435.0   udp   \n",
              "1008745  192.168.100.103    43763.0     249.99.119.9    26169.0   udp   \n",
              "1008746  192.168.100.103    43763.0  205.103.167.192    18241.0   udp   \n",
              "1008747  192.168.100.103    43763.0    23.70.168.160    16854.0   udp   \n",
              "\n",
              "        conn_state history  orig_pkts  orig_ip_bytes      label  \n",
              "0               S0       S        3.0          180.0  Malicious  \n",
              "1               S0       S        1.0           60.0  Malicious  \n",
              "2               S0       S        1.0           60.0  Malicious  \n",
              "3               S0       S        3.0          180.0  Malicious  \n",
              "4               S0       S        1.0           60.0  Malicious  \n",
              "...            ...     ...        ...            ...        ...  \n",
              "1008743         S0       D        1.0           40.0     Benign  \n",
              "1008744         S0       D        1.0           40.0     Benign  \n",
              "1008745         S0       D        1.0           40.0     Benign  \n",
              "1008746         S0       D        1.0           40.0     Benign  \n",
              "1008747         S0       D        1.0           40.0     Benign  \n",
              "\n",
              "[1008748 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-237d92d9-c1c3-4dd6-b192-8e46a81e32b1\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id.orig_h</th>\n",
              "      <th>id.orig_p</th>\n",
              "      <th>id.resp_h</th>\n",
              "      <th>id.resp_p</th>\n",
              "      <th>proto</th>\n",
              "      <th>conn_state</th>\n",
              "      <th>history</th>\n",
              "      <th>orig_pkts</th>\n",
              "      <th>orig_ip_bytes</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>51524.0</td>\n",
              "      <td>65.127.233.163</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>56305.0</td>\n",
              "      <td>63.150.16.171</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>41101.0</td>\n",
              "      <td>111.40.23.49</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>60905.0</td>\n",
              "      <td>131.174.215.147</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>44301.0</td>\n",
              "      <td>91.42.47.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008743</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>16.219.83.137</td>\n",
              "      <td>64906.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008744</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>100.57.245.196</td>\n",
              "      <td>39435.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008745</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>249.99.119.9</td>\n",
              "      <td>26169.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>205.103.167.192</td>\n",
              "      <td>18241.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008747</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>23.70.168.160</td>\n",
              "      <td>16854.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1008748 rows × 10 columns</p>\n",
              "</div>\n",
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              "      buttonEl.style.display =\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "    }\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-eb89ef5f-76ce-4b3b-82a2-b3f66182851b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Encoding"
      ],
      "metadata": {
        "id": "Wuvi3lXr6f8X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()\n",
        "\n",
        "# extract categorical attributes\n",
        "cat_df = df.select_dtypes(include=['object']).copy()\n",
        "\n",
        "# encode the categorical attributes\n",
        "for column in cat_df.columns:\n",
        "    df[column] = encoder.fit_transform(df[column])"
      ],
      "metadata": {
        "id": "fyMxL0Au5t9O"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Attack Class Distribution\n",
        "df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ifk_w7Z06iN_",
        "outputId": "1fbb4dc5-5851-4a8c-984b-3deee3a207ef"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    539473\n",
              "0    469275\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find important features for further machine learning modeling, we can use feature selection techniques or tree-based algorithms that provide feature importance scores."
      ],
      "metadata": {
        "id": "qlR7Twqu6sHW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Feature Engineering"
      ],
      "metadata": {
        "id": "e6-DM1qk6t7r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Correlation matrix"
      ],
      "metadata": {
        "id": "poj1C9oRNR7C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()\n",
        "plt.figure(figsize=(10, 8))  # Set the figure size\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "id": "Lsx4Pzen6omw",
        "outputId": "cd50050d-13be-4e48-9076-0ea345582b78"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "### Feature importance"
      ],
      "metadata": {
        "id": "0FlgGDkoNVct"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate the features and the target variable\n",
        "X = df.drop('label', axis=1)\n",
        "y = df['label']\n",
        "\n",
        "# Create a Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Fit the model to your data\n",
        "rf_classifier.fit(X, y)\n",
        "\n",
        "# Get feature importances\n",
        "feature_importances = rf_classifier.feature_importances_\n",
        "\n",
        "# Create a DataFrame to display feature names and their importance scores\n",
        "feature_importance_df = pd.DataFrame({'Feature': X.columns,\n",
        "                                      'Importance': feature_importances})\n",
        "\n",
        "# Sort the features by importance in descending order\n",
        "feature_importance_df = feature_importance_df.sort_values(by='Importance',\n",
        "                                                          ascending=False)\n",
        "\n",
        "# Create a bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\n",
        "plt.xlabel('Importance')\n",
        "plt.title('Feature Importance')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ICJkq7s9Mm98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "88e6b110-344e-4093-8438-638f344f3ccb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Proto, history and id.resp_p are all intercorrelated so we are only taking history as input**"
      ],
      "metadata": {
        "id": "Al-PPwvBOtKa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Prediction using all the important features"
      ],
      "metadata": {
        "id": "SSULKl_X6I54"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# X = df.drop('label', axis=1)"
      ],
      "metadata": {
        "id": "3nXf0uAR9jC3"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate the features and the target variable\n",
        "# X = df.drop('label', axis=1)\n",
        "X = df.drop(['label','proto','id.resp_p'], axis=1)\n",
        "y = df['label']"
      ],
      "metadata": {
        "id": "obOpuhhZ6Ht_"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CAvsKYzi--PK",
        "outputId": "314b9e35-ecb6-42b8-e3a2-507acf7b6020"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id.orig_h  id.orig_p  id.resp_h  conn_state  history  orig_pkts  \\\n",
              "0             6478    51524.0     501746           6       21        3.0   \n",
              "1             6478    56305.0     496872           6       21        1.0   \n",
              "2             6478    41101.0      36215           6       21        1.0   \n",
              "3             6478    60905.0      88035           6       21        3.0   \n",
              "4             6478    44301.0     576062           6       21        1.0   \n",
              "...            ...        ...        ...         ...      ...        ...   \n",
              "1008743       6478    43763.0     165796           6        1        1.0   \n",
              "1008744       6478    43763.0       7220           6        1        1.0   \n",
              "1008745       6478    43763.0     389190           6        1        1.0   \n",
              "1008746       6478    43763.0     293502           6        1        1.0   \n",
              "1008747       6478    43763.0     354657           6        1        1.0   \n",
              "\n",
              "         orig_ip_bytes  \n",
              "0                180.0  \n",
              "1                 60.0  \n",
              "2                 60.0  \n",
              "3                180.0  \n",
              "4                 60.0  \n",
              "...                ...  \n",
              "1008743           40.0  \n",
              "1008744           40.0  \n",
              "1008745           40.0  \n",
              "1008746           40.0  \n",
              "1008747           40.0  \n",
              "\n",
              "[1008748 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0610441-bbc2-41cb-8b5d-5f604fbb4a83\" 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>id.orig_h</th>\n",
              "      <th>id.orig_p</th>\n",
              "      <th>id.resp_h</th>\n",
              "      <th>conn_state</th>\n",
              "      <th>history</th>\n",
              "      <th>orig_pkts</th>\n",
              "      <th>orig_ip_bytes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6478</td>\n",
              "      <td>51524.0</td>\n",
              "      <td>501746</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6478</td>\n",
              "      <td>56305.0</td>\n",
              "      <td>496872</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6478</td>\n",
              "      <td>41101.0</td>\n",
              "      <td>36215</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6478</td>\n",
              "      <td>60905.0</td>\n",
              "      <td>88035</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6478</td>\n",
              "      <td>44301.0</td>\n",
              "      <td>576062</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008743</th>\n",
              "      <td>6478</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>165796</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008744</th>\n",
              "      <td>6478</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>7220</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008745</th>\n",
              "      <td>6478</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>389190</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>6478</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>293502</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008747</th>\n",
              "      <td>6478</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>354657</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1008748 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-a0610441-bbc2-41cb-8b5d-5f604fbb4a83 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-a0610441-bbc2-41cb-8b5d-5f604fbb4a83');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8192d3fb-603d-4324-861f-3f4aadf5fba3')\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\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-8192d3fb-603d-4324-861f-3f4aadf5fba3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Splitting"
      ],
      "metadata": {
        "id": "FXeUel4GS0au"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into a training set and a testing set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Standardize the feature data (scaling)\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "RWqq0DAB8W_Y"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Building and Model Training"
      ],
      "metadata": {
        "id": "9XFzQIR8TdfY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Create a list of classifiers without hyperparameter tuning\n",
        "classifiers = [\n",
        "    {\n",
        "        \"name\": \"Logistic Regression\",\n",
        "        \"model\": LogisticRegression(),\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Decision Tree\",\n",
        "        \"model\": DecisionTreeClassifier(),\n",
        "    },\n",
        "\n",
        "    {\n",
        "        \"name\": \"K-Nearest Neighbors\",\n",
        "        \"model\": KNeighborsClassifier(),\n",
        "    }\n",
        "]\n",
        "\n",
        "# Loop through each classifier\n",
        "for classifier_info in classifiers:\n",
        "    name = classifier_info[\"name\"]\n",
        "    model = classifier_info[\"model\"]\n",
        "\n",
        "    # Fit the model on the training data\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the testing data\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Calculate accuracy\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    # Generate a classification report\n",
        "    report = classification_report(y_test, y_pred)\n",
        "\n",
        "    print(f\"Classifier: {name}\")\n",
        "    print(f\"Accuracy: {accuracy:.2f}\")\n",
        "    print(report)\n",
        "\n",
        "    # Perform cross-validation to assess model performance\n",
        "    cross_val_scores = cross_val_score(model, X, y, cv=5)\n",
        "    mean_score = np.mean(cross_val_scores)\n",
        "    print(f\"Cross-Validation Mean Accuracy: {mean_score:.2f}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQgjWaYtQX6l",
        "outputId": "4c3ffad9-7ee0-44bd-e5b6-6310dcb1f8dd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier: Logistic Regression\n",
            "Accuracy: 0.96\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95     93852\n",
            "           1       0.93      1.00      0.96    107898\n",
            "\n",
            "    accuracy                           0.96    201750\n",
            "   macro avg       0.96      0.95      0.96    201750\n",
            "weighted avg       0.96      0.96      0.96    201750\n",
            "\n",
            "Cross-Validation Mean Accuracy: 0.95\n",
            "\n",
            "Classifier: Decision Tree\n",
            "Accuracy: 0.99\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99     93852\n",
            "           1       0.99      0.99      0.99    107898\n",
            "\n",
            "    accuracy                           0.99    201750\n",
            "   macro avg       0.99      0.99      0.99    201750\n",
            "weighted avg       0.99      0.99      0.99    201750\n",
            "\n",
            "Cross-Validation Mean Accuracy: 0.92\n",
            "\n",
            "Classifier: K-Nearest Neighbors\n",
            "Accuracy: 0.96\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.91      0.95     93852\n",
            "           1       0.93      0.99      0.96    107898\n",
            "\n",
            "    accuracy                           0.96    201750\n",
            "   macro avg       0.96      0.95      0.95    201750\n",
            "weighted avg       0.96      0.96      0.95    201750\n",
            "\n",
            "Cross-Validation Mean Accuracy: 0.92\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Decision Tree works better but there might be overfitting so will use Random forest in the overfitting case and tune our model using hyperparameters.**"
      ],
      "metadata": {
        "id": "FKu2vMlEnTfL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import RandomForestClassifier\n",
        "# from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# # Create a Random Forest classifier\n",
        "# random_forest_classifier = RandomForestClassifier()\n",
        "\n",
        "# # Define a set of hyperparameters and their possible values to search\n",
        "# param_grid = {\n",
        "#     'n_estimators': [50, 100, 200],\n",
        "#     'max_depth': [None, 10, 20, 30],\n",
        "#     'min_samples_split': [2, 5, 10],\n",
        "#     'min_samples_leaf': [1, 2, 4]\n",
        "# }\n",
        "\n",
        "# # Create a GridSearchCV object to find the best hyperparameters\n",
        "# grid_search = GridSearchCV(random_forest_classifier, param_grid, cv=5, n_jobs=-1)\n",
        "\n",
        "# # Fit the GridSearchCV on the training data\n",
        "# grid_search.fit(X_train, y_train)\n",
        "\n",
        "# # Get the best Random Forest model with the optimized hyperparameters\n",
        "# best_random_forest = grid_search.best_estimator_\n",
        "\n",
        "# # Make predictions on the testing data using the best model\n",
        "# y_pred_rf = best_random_forest.predict(X_test)\n",
        "\n",
        "# # Calculate accuracy and generate a classification report\n",
        "# accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "# report_rf = classification_report(y_test, y_pred_rf)\n",
        "\n",
        "# print(\"Random Forest Classifier (with Hyperparameter Tuning)\")\n",
        "# print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n",
        "# print(f\"Accuracy: {accuracy_rf:.2f}\")\n",
        "# print(report_rf)\n",
        "\n",
        "# # Perform cross-validation to assess the performance of the best model\n",
        "# cross_val_scores_rf = cross_val_score(best_random_forest, X, y, cv=5)\n",
        "# mean_score_rf = np.mean(cross_val_scores_rf)\n",
        "# print(f\"Cross-Validation Mean Accuracy (Random Forest): {mean_score_rf:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKdgOD6kl3-b",
        "outputId": "bbfb01b0-8af6-45e6-9ef0-88fd2fb90e9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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      "metadata": {
        "id": "fvurHUa7M0eH"
      }
    },
    {
      "cell_type": "markdown",
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)"
      ],
      "metadata": {
        "id": "t9caPwi9MflT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have tried to train our model using Random Forest which will overcome the problem of overfitting and we have set some hyperparameters but because of the computational complexity it is taking too much time to run our model. So we will consider logistic regression.\n",
        "\n",
        "After 3-4 hour our code has automatically stopped"
      ],
      "metadata": {
        "id": "7gfzgYWiLwBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Create a Logistic Regression classifier\n",
        "logistic_regression_classifier = LogisticRegression()\n",
        "\n",
        "# Define a smaller set of hyperparameters and their possible values to search\n",
        "param_grid = {\n",
        "    'C': [0.001, 0.01, 0.1, 1],\n",
        "    'penalty': ['l1', 'l2']\n",
        "}\n",
        "\n",
        "# Create a GridSearchCV object to find the best hyperparameters\n",
        "grid_search = GridSearchCV(logistic_regression_classifier, param_grid, cv=5, n_jobs=-1)\n",
        "\n",
        "# Fit the GridSearchCV on the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best Logistic Regression model with the optimized hyperparameters\n",
        "best_logistic_regression = grid_search.best_estimator_\n",
        "\n",
        "# Make predictions on the testing data using the best model\n",
        "y_pred_lr = best_logistic_regression.predict(X_test)\n",
        "\n",
        "# Calculate accuracy and generate a classification report\n",
        "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
        "report_lr = classification_report(y_test, y_pred_lr)\n",
        "\n",
        "print(\"Logistic Regression (with Smaller Hyperparameter Tuning)\")\n",
        "print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n",
        "print(f\"Accuracy: {accuracy_lr:.2f}\")\n",
        "print(report_lr)\n",
        "\n",
        "# Perform cross-validation to assess the performance of the best model\n",
        "cross_val_scores_lr = cross_val_score(best_logistic_regression, X, y, cv=5)\n",
        "mean_score_lr = np.mean(cross_val_scores_lr)\n",
        "print(f\"Cross-Validation Mean Accuracy (Logistic Regression): {mean_score_lr:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wx-EXwkHnRoK",
        "outputId": "99fa5405-7eb0-4a4b-f7fb-21ff22afcb18"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "20 fits failed out of a total of 40.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "20 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan 0.95686854        nan 0.95686606        nan 0.95686854\n",
            "        nan 0.95686483]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression (with Smaller Hyperparameter Tuning)\n",
            "Best Hyperparameters: {'C': 0.001, 'penalty': 'l2'}\n",
            "Accuracy: 0.96\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95     93852\n",
            "           1       0.93      1.00      0.96    107898\n",
            "\n",
            "    accuracy                           0.96    201750\n",
            "   macro avg       0.96      0.95      0.96    201750\n",
            "weighted avg       0.96      0.96      0.96    201750\n",
            "\n",
            "Cross-Validation Mean Accuracy (Logistic Regression): 0.95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Evaluation, Selection and Testing"
      ],
      "metadata": {
        "id": "M1u_nh1rVfUX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Create a Logistic Regression classifier with the best hyperparameters\n",
        "best_logistic_regression = LogisticRegression(C=0.001, penalty='l2')\n",
        "\n",
        "# Fit the Logistic Regression model with the best hyperparameters on the training data\n",
        "best_logistic_regression.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data using the best model\n",
        "y_pred_lr = best_logistic_regression.predict(X_test)\n",
        "\n",
        "# Calculate accuracy and generate a classification report\n",
        "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
        "report_lr = classification_report(y_test, y_pred_lr)\n",
        "\n",
        "print(\"Logistic Regression (with Best Hyperparameters)\")\n",
        "print(\"Hyperparameters: {'C': 0.001, 'penalty': 'l2'}\")\n",
        "print(f\"Accuracy: {accuracy_lr:.2f}\")\n",
        "print(report_lr)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Xf_U0oqPie4",
        "outputId": "9e020989-2cd0-4ca7-ff84-1b42e40b67f1"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression (with Best Hyperparameters)\n",
            "Hyperparameters: {'C': 0.001, 'penalty': 'l2'}\n",
            "Accuracy: 0.96\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95     93852\n",
            "           1       0.93      1.00      0.96    107898\n",
            "\n",
            "    accuracy                           0.96    201750\n",
            "   macro avg       0.96      0.95      0.96    201750\n",
            "weighted avg       0.96      0.96      0.96    201750\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Deployment"
      ],
      "metadata": {
        "id": "2vhu_KaZP2AS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Create a Pickle file using serialization\n",
        "import pickle\n",
        "pickle_out = open(\"best_logistic_regression.pkl\",\"wb\")\n",
        "pickle.dump(best_logistic_regression, pickle_out)\n",
        "#Serialization\n",
        "pickle_out.close()"
      ],
      "metadata": {
        "id": "bBFFWrDvPlcP"
      },
      "execution_count": 38,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}