XG_boost.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNV7DHRm4FQ3b1RoF61OHV4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AlonVery/Model-s_for_Diploma/blob/main/%D0%9A%D0%BE%D0%BF%D0%B8%D1%8F_%D0%B1%D0%BB%D0%BE%D0%BA%D0%BD%D0%BE%D1%82%D0%B0_%22XG_boost_ipynb%22.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#XG перевод в RF\n"
      ],
      "metadata": {
        "id": "lJ5njimNyX3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scapy\n",
        "!pip install mocket\n",
        "!pip install lime\n",
        "!pip install shap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwcAN5BgvLRG",
        "outputId": "d3a557a8-efb8-4ee9-a3a5-ae88366c28eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scapy\n",
            "  Downloading scapy-2.5.0.tar.gz (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: scapy\n",
            "  Building wheel for scapy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scapy: filename=scapy-2.5.0-py2.py3-none-any.whl size=1444327 sha256=29f1bb7cc239ae2d22fb05abe3f2b01b1acfcf9793a0bb5ffd1d2e0935ae1021\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/b7/03/8344d8cf6695624746311bc0d389e9d05535ca83c35f90241d\n",
            "Successfully built scapy\n",
            "Installing collected packages: scapy\n",
            "Successfully installed scapy-2.5.0\n",
            "Collecting mocket\n",
            "  Downloading mocket-3.12.4.tar.gz (72 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.5/72.5 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: decorator>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from mocket) (4.4.2)\n",
            "Collecting httptools (from mocket)\n",
            "  Downloading httptools-0.6.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (341 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting python-magic>=0.4.5 (from mocket)\n",
            "  Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: urllib3>=1.25.3 in /usr/local/lib/python3.10/dist-packages (from mocket) (2.0.7)\n",
            "Building wheels for collected packages: mocket\n",
            "  Building wheel for mocket (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mocket: filename=mocket-3.12.4-py3-none-any.whl size=21899 sha256=b3886380bdb96964b31e1c2997d94b5846639b6c9fe2059400996b1c5ff27022\n",
            "  Stored in directory: /root/.cache/pip/wheels/a4/97/87/a48fe338ebd5c804536a9bb44df50472f26aed7f67476c9ab7\n",
            "Successfully built mocket\n",
            "Installing collected packages: python-magic, httptools, mocket\n",
            "Successfully installed httptools-0.6.1 mocket-3.12.4 python-magic-0.4.27\n",
            "Collecting lime\n",
            "  Downloading lime-0.2.0.1.tar.gz (275 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.7/275.7 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from lime) (3.7.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lime) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lime) (1.11.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from lime) (4.66.2)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from lime) (1.2.2)\n",
            "Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.10/dist-packages (from lime) (0.19.3)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (3.2.1)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (9.4.0)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (2024.2.12)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (1.6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (24.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->lime) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->lime) (3.4.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (4.50.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->lime) (1.16.0)\n",
            "Building wheels for collected packages: lime\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283835 sha256=4dd253b6ca2e0227666dca7acdfaf01f38571d23bebfbab4b9f65f0fc00e1e3f\n",
            "  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n",
            "Successfully built lime\n",
            "Installing collected packages: lime\n",
            "Successfully installed lime-0.2.0.1\n",
            "Collecting shap\n",
            "  Downloading shap-0.45.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (538 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m538.2/538.2 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shap) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from shap) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from shap) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from shap) (2.0.3)\n",
            "Requirement already satisfied: tqdm>=4.27.0 in /usr/local/lib/python3.10/dist-packages (from shap) (4.66.2)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.10/dist-packages (from shap) (24.0)\n",
            "Collecting slicer==0.0.7 (from shap)\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap) (0.58.1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap) (2.2.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap) (0.41.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2024.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (3.4.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->shap) (1.16.0)\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.45.0 slicer-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "behXn8gJu6fS",
        "outputId": "e815add4-7e01-4cee-8e07-4217b19de455"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.pipeline import Pipeline\n",
        "from keras.utils import to_categorical\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from tensorflow.keras.metrics import Accuracy\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler\n",
        "from scipy.cluster.hierarchy import dendrogram\n",
        "from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, LabelEncoder\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "import pickle\n",
        "import joblib\n",
        "from joblib import dump, load\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.pipeline import Pipeline\n",
        "from keras.utils import to_categorical\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from tensorflow.keras.metrics import Accuracy\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler\n",
        "from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, LabelEncoder\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score\n",
        "from sklearn.model_selection import learning_curve, validation_curve\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import learning_curve, validation_curve\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import joblib\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score\n",
        "import lime\n",
        "import lime.lime_tabular\n",
        "import shap\n",
        "from sklearn.model_selection import train_test_split\n",
        "from plotly.offline import init_notebook_mode, iplot, plot\n",
        "import plotly as py\n",
        "import plotly.express as px\n",
        "init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "import socket\n",
        "from scapy.all import sniff\n",
        "from unittest.mock import patch\n",
        "import random\n",
        "import pickle\n",
        "from joblib import dump, load\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/stepka123/basic-NL-SA/main/KDDTrain%2B.txt\")\n",
        "\n",
        "columns = (['duration','protocol_type','service','flag','src_bytes','dst_bytes','land','wrong_fragment','urgent','hot'\n",
        ",'num_failed_logins','logged_in','num_compromised','root_shell','su_attempted','num_root','num_file_creations'\n",
        ",'num_shells','num_access_files','num_outbound_cmds','is_host_login','is_guest_login','count','srv_count','serror_rate'\n",
        ",'srv_serror_rate','rerror_rate','srv_rerror_rate','same_srv_rate','diff_srv_rate','srv_diff_host_rate','dst_host_count','dst_host_srv_count'\n",
        ",'dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate','dst_host_srv_diff_host_rate','dst_host_serror_rate'\n",
        ",'dst_host_srv_serror_rate','dst_host_rerror_rate','dst_host_srv_rerror_rate','attack', 'level'])\n",
        "df.columns = columns\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "t7xM91-fvEKN",
        "outputId": "22d73a27-f987-4777-a418-704878e7e748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration protocol_type  service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           udp    other   SF        146          0     0   \n",
              "1         0           tcp  private   S0          0          0     0   \n",
              "2         0           tcp     http   SF        232       8153     0   \n",
              "3         0           tcp     http   SF        199        420     0   \n",
              "4         0           tcp  private  REJ          0          0     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
              "0               0       0    0  ...                    0.00   \n",
              "1               0       0    0  ...                    0.10   \n",
              "2               0       0    0  ...                    1.00   \n",
              "3               0       0    0  ...                    1.00   \n",
              "4               0       0    0  ...                    0.07   \n",
              "\n",
              "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
              "0                    0.60                         0.88   \n",
              "1                    0.05                         0.00   \n",
              "2                    0.00                         0.03   \n",
              "3                    0.00                         0.00   \n",
              "4                    0.07                         0.00   \n",
              "\n",
              "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
              "0                         0.00                  0.00   \n",
              "1                         0.00                  1.00   \n",
              "2                         0.04                  0.03   \n",
              "3                         0.00                  0.00   \n",
              "4                         0.00                  0.00   \n",
              "\n",
              "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
              "0                      0.00                   0.0                      0.00   \n",
              "1                      1.00                   0.0                      0.00   \n",
              "2                      0.01                   0.0                      0.01   \n",
              "3                      0.00                   0.0                      0.00   \n",
              "4                      0.00                   1.0                      1.00   \n",
              "\n",
              "    attack  level  \n",
              "0   normal     15  \n",
              "1  neptune     19  \n",
              "2   normal     21  \n",
              "3   normal     21  \n",
              "4  neptune     21  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7061624-63bc-4bdc-aaa9-2e2088708715\" 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>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack</th>\n",
              "      <th>level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 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-c7061624-63bc-4bdc-aaa9-2e2088708715')\"\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-c7061624-63bc-4bdc-aaa9-2e2088708715 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-c7061624-63bc-4bdc-aaa9-2e2088708715');\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-ecf511fb-4bf1-43e4-abe6-79bb3c6b84f7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecf511fb-4bf1-43e4-abe6-79bb3c6b84f7')\"\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-ecf511fb-4bf1-43e4-abe6-79bb3c6b84f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " df = df.drop(['level'], axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "m3FpoOnvvHq5",
        "outputId": "895eb4bc-dbbd-4c95-8a94-12a20a9d0071"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration protocol_type  service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           udp    other   SF        146          0     0   \n",
              "1         0           tcp  private   S0          0          0     0   \n",
              "2         0           tcp     http   SF        232       8153     0   \n",
              "3         0           tcp     http   SF        199        420     0   \n",
              "4         0           tcp  private  REJ          0          0     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0               0       0    0  ...                   1   \n",
              "1               0       0    0  ...                  26   \n",
              "2               0       0    0  ...                 255   \n",
              "3               0       0    0  ...                 255   \n",
              "4               0       0    0  ...                  19   \n",
              "\n",
              "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                    0.00                    0.60   \n",
              "1                    0.10                    0.05   \n",
              "2                    1.00                    0.00   \n",
              "3                    1.00                    0.00   \n",
              "4                    0.07                    0.07   \n",
              "\n",
              "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                         0.88                         0.00   \n",
              "1                         0.00                         0.00   \n",
              "2                         0.03                         0.04   \n",
              "3                         0.00                         0.00   \n",
              "4                         0.00                         0.00   \n",
              "\n",
              "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                  0.00                      0.00                   0.0   \n",
              "1                  1.00                      1.00                   0.0   \n",
              "2                  0.03                      0.01                   0.0   \n",
              "3                  0.00                      0.00                   0.0   \n",
              "4                  0.00                      0.00                   1.0   \n",
              "\n",
              "   dst_host_srv_rerror_rate   attack  \n",
              "0                      0.00   normal  \n",
              "1                      0.00  neptune  \n",
              "2                      0.01   normal  \n",
              "3                      0.00   normal  \n",
              "4                      1.00  neptune  \n",
              "\n",
              "[5 rows x 42 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-473a84db-5aa0-4f48-bc80-1317bdddf0a2\" 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>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>26</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>19</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 42 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-473a84db-5aa0-4f48-bc80-1317bdddf0a2')\"\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-473a84db-5aa0-4f48-bc80-1317bdddf0a2 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-473a84db-5aa0-4f48-bc80-1317bdddf0a2');\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-f02c55e8-ec17-4959-b115-bbf4d98087cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f02c55e8-ec17-4959-b115-bbf4d98087cd')\"\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-f02c55e8-ec17-4959-b115-bbf4d98087cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# changing attack labels to their respective attack class\n",
        "def change_label(df):\n",
        "    df.attack.replace(['apache2','back','land','neptune','mailbomb','pod','processtable','smurf','teardrop','udpstorm','worm'],'Dos',inplace=True)\n",
        "    df.attack.replace(['ftp_write','guess_passwd','httptunnel','imap','multihop','named','phf','sendmail','snmpgetattack','snmpguess','spy','warezclient','warezmaster','xlock','xsnoop'],'R2L',inplace=True)\n",
        "    df.attack.replace(['ipsweep','mscan','nmap','portsweep','saint','satan'],'Probe',inplace=True)\n",
        "    df.attack.replace(['buffer_overflow','loadmodule','perl','ps','rootkit','sqlattack','xterm'],'U2R',inplace=True)"
      ],
      "metadata": {
        "id": "mHr61RlHvjzz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "change_label(df)\n",
        "df.attack.unique()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "ij-29AmAvRF9",
        "outputId": "d4259408-cc4c-44b4-c88c-0e4757990f56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration protocol_type  service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           udp    other   SF        146          0     0   \n",
              "1         0           tcp  private   S0          0          0     0   \n",
              "2         0           tcp     http   SF        232       8153     0   \n",
              "3         0           tcp     http   SF        199        420     0   \n",
              "4         0           tcp  private  REJ          0          0     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0               0       0    0  ...                   1   \n",
              "1               0       0    0  ...                  26   \n",
              "2               0       0    0  ...                 255   \n",
              "3               0       0    0  ...                 255   \n",
              "4               0       0    0  ...                  19   \n",
              "\n",
              "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                    0.00                    0.60   \n",
              "1                    0.10                    0.05   \n",
              "2                    1.00                    0.00   \n",
              "3                    1.00                    0.00   \n",
              "4                    0.07                    0.07   \n",
              "\n",
              "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                         0.88                         0.00   \n",
              "1                         0.00                         0.00   \n",
              "2                         0.03                         0.04   \n",
              "3                         0.00                         0.00   \n",
              "4                         0.00                         0.00   \n",
              "\n",
              "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                  0.00                      0.00                   0.0   \n",
              "1                  1.00                      1.00                   0.0   \n",
              "2                  0.03                      0.01                   0.0   \n",
              "3                  0.00                      0.00                   0.0   \n",
              "4                  0.00                      0.00                   1.0   \n",
              "\n",
              "   dst_host_srv_rerror_rate  attack  \n",
              "0                      0.00  normal  \n",
              "1                      0.00     Dos  \n",
              "2                      0.01  normal  \n",
              "3                      0.00  normal  \n",
              "4                      1.00     Dos  \n",
              "\n",
              "[5 rows x 42 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1f9be58-3cc7-4c02-8654-d06a19ac100a\" 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>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>26</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Dos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>19</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Dos</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 42 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-b1f9be58-3cc7-4c02-8654-d06a19ac100a')\"\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-b1f9be58-3cc7-4c02-8654-d06a19ac100a 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-b1f9be58-3cc7-4c02-8654-d06a19ac100a');\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-ff0f525b-b371-47fc-a0a2-6be300de7bc8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff0f525b-b371-47fc-a0a2-6be300de7bc8')\"\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-ff0f525b-b371-47fc-a0a2-6be300de7bc8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le1 = LabelEncoder()\n",
        "le2 = LabelEncoder()\n",
        "le3 = LabelEncoder()\n",
        "le4 = LabelEncoder()\n",
        "\n",
        "le_protocol_type = le1.fit(df['protocol_type'])\n",
        "le_service = le2.fit(df['service'])\n",
        "le_flag = le3.fit(df['flag'])\n",
        "le_attack = le4.fit(df['attack'])\n",
        "\n",
        "df['protocol_type'] = le_protocol_type.transform(df['protocol_type'])\n",
        "df['service'] = le_service.transform(df['service'])\n",
        "df['flag'] = le_flag.transform(df['flag'])\n",
        "df['attack'] = le_attack.transform(df['attack'])"
      ],
      "metadata": {
        "id": "sM60NshqvTJD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train= df[['attack']]\n",
        "X_train= df.drop(labels=['attack'], axis=1)\n",
        "print('X_train has shape:',X_train.shape,'\\ny_train has shape:',y_train.shape)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_train,y_train, test_size=0.20, random_state=42)\n",
        "target_names = ['normal', 'Dos', 'Probe', 'R2L', 'U2R']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnTmogKuvUTx",
        "outputId": "fd95ce72-a6a5-4384-b321-6afb47406b35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train has shape: (125972, 41) \n",
            "y_train has shape: (125972, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xg = XGBClassifier(n_estimators=100, learning_rate=1, objective='multi:softmax', random_state=42)\n",
        "xg = xg.fit(X_train, y_train)\n",
        "xg_pred = xg.predict(X_test)\n",
        "print(xg_pred)\n",
        "xg_acc = accuracy_score(y_test, xg_pred)\n",
        "print(xg_acc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTKWGAwyvXau",
        "outputId": "df4f4f77-9181-41a9-be7d-910dc6b3f547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 4 ... 0 0 0]\n",
            "0.9991665012899384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xg_cm = confusion_matrix(y_test, xg_pred)\n",
        "ConfusionMatrixDisplay(xg_cm,display_labels=target_names).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "e4X6GRcevZC5",
        "outputId": "297e309d-50a6-42e4-85f5-dcdd955c7f63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbcf2a4a740>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'best_model.sav'\n",
        "pickle.dump(xg, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "mS_c3aYXvaVi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('https://raw.githubusercontent.com/stepka123/basic-NL-SA/stepka123-KDD_Dataset/KDDTrain%2B_20Percent.txt')\n",
        "\n",
        "columns = (['duration','protocol_type','service','flag','src_bytes','dst_bytes','land','wrong_fragment','urgent','hot'\n",
        ",'num_failed_logins','logged_in','num_compromised','root_shell','su_attempted','num_root','num_file_creations'\n",
        ",'num_shells','num_access_files','num_outbound_cmds','is_host_login','is_guest_login','count','srv_count','serror_rate'\n",
        ",'srv_serror_rate','rerror_rate','srv_rerror_rate','same_srv_rate','diff_srv_rate','srv_diff_host_rate','dst_host_count','dst_host_srv_count'\n",
        ",'dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate','dst_host_srv_diff_host_rate','dst_host_serror_rate'\n",
        ",'dst_host_srv_serror_rate','dst_host_rerror_rate','dst_host_srv_rerror_rate','attack','level'])\n",
        "\n",
        "df_test.columns = columns\n",
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "ichGnhVfvbsR",
        "outputId": "25980a71-d968-49cf-a74a-76db8da9d7a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration protocol_type   service  flag  src_bytes  dst_bytes  land  \\\n",
              "0             0           udp     other    SF        146          0     0   \n",
              "1             0           tcp   private    S0          0          0     0   \n",
              "2             0           tcp      http    SF        232       8153     0   \n",
              "3             0           tcp      http    SF        199        420     0   \n",
              "4             0           tcp   private   REJ          0          0     0   \n",
              "...         ...           ...       ...   ...        ...        ...   ...   \n",
              "25186         0           tcp      exec  RSTO          0          0     0   \n",
              "25187         0           tcp  ftp_data    SF        334          0     0   \n",
              "25188         0           tcp   private   REJ          0          0     0   \n",
              "25189         0           tcp      nnsp    S0          0          0     0   \n",
              "25190         0           tcp    finger    S0          0          0     0   \n",
              "\n",
              "       wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
              "0                   0       0    0  ...                    0.00   \n",
              "1                   0       0    0  ...                    0.10   \n",
              "2                   0       0    0  ...                    1.00   \n",
              "3                   0       0    0  ...                    1.00   \n",
              "4                   0       0    0  ...                    0.07   \n",
              "...               ...     ...  ...  ...                     ...   \n",
              "25186               0       0    0  ...                    0.03   \n",
              "25187               0       0    0  ...                    1.00   \n",
              "25188               0       0    0  ...                    0.05   \n",
              "25189               0       0    0  ...                    0.08   \n",
              "25190               0       0    0  ...                    0.19   \n",
              "\n",
              "       dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
              "0                        0.60                         0.88   \n",
              "1                        0.05                         0.00   \n",
              "2                        0.00                         0.03   \n",
              "3                        0.00                         0.00   \n",
              "4                        0.07                         0.00   \n",
              "...                       ...                          ...   \n",
              "25186                    0.06                         0.00   \n",
              "25187                    0.00                         1.00   \n",
              "25188                    0.07                         0.00   \n",
              "25189                    0.06                         0.00   \n",
              "25190                    0.03                         0.01   \n",
              "\n",
              "       dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
              "0                             0.00                  0.00   \n",
              "1                             0.00                  1.00   \n",
              "2                             0.04                  0.03   \n",
              "3                             0.00                  0.00   \n",
              "4                             0.00                  0.00   \n",
              "...                            ...                   ...   \n",
              "25186                         0.00                  0.00   \n",
              "25187                         0.18                  0.00   \n",
              "25188                         0.00                  0.00   \n",
              "25189                         0.00                  1.00   \n",
              "25190                         0.00                  1.00   \n",
              "\n",
              "       dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                          0.00                   0.0   \n",
              "1                          1.00                   0.0   \n",
              "2                          0.01                   0.0   \n",
              "3                          0.00                   0.0   \n",
              "4                          0.00                   1.0   \n",
              "...                         ...                   ...   \n",
              "25186                      0.00                   1.0   \n",
              "25187                      0.00                   0.0   \n",
              "25188                      0.00                   1.0   \n",
              "25189                      1.00                   0.0   \n",
              "25190                      1.00                   0.0   \n",
              "\n",
              "       dst_host_srv_rerror_rate       attack  level  \n",
              "0                          0.00       normal     15  \n",
              "1                          0.00      neptune     19  \n",
              "2                          0.01       normal     21  \n",
              "3                          0.00       normal     21  \n",
              "4                          1.00      neptune     21  \n",
              "...                         ...          ...    ...  \n",
              "25186                      1.00      neptune     19  \n",
              "25187                      0.00  warezclient     12  \n",
              "25188                      1.00      neptune     21  \n",
              "25189                      0.00      neptune     20  \n",
              "25190                      0.00      neptune     18  \n",
              "\n",
              "[25191 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8407ceb5-ff99-444c-bd66-98f0741c95b5\" 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>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack</th>\n",
              "      <th>level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</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>25186</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>exec</td>\n",
              "      <td>RSTO</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25187</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>334</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>warezclient</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25188</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25189</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>nnsp</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25190</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>finger</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>25191 rows × 43 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-8407ceb5-ff99-444c-bd66-98f0741c95b5')\"\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-8407ceb5-ff99-444c-bd66-98f0741c95b5 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-8407ceb5-ff99-444c-bd66-98f0741c95b5');\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-338fb737-a093-4b33-8f61-7e8da8912b27\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-338fb737-a093-4b33-8f61-7e8da8912b27')\"\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-338fb737-a093-4b33-8f61-7e8da8912b27 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_928bd60a-3ff5-45d6-8716-d683b909704f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_928bd60a-3ff5-45d6-8716-d683b909704f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = df_test.drop(['attack', 'level'], axis=1)"
      ],
      "metadata": {
        "id": "tOTqtE3Rvwfs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = pickle.load(open(filename, 'rb'))"
      ],
      "metadata": {
        "id": "PpsmRbagvyXN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def intrusion_check(packet):\n",
        "    packet[1] = le_protocol_type.transform([packet[1]])[0]\n",
        "    packet[2] = le_service.transform([packet[2]])[0]\n",
        "    packet[3] = le_flag.transform([packet[3]])[0]\n",
        "    pred = loaded_model.predict(np.reshape(packet, (1, 41)))\n",
        "    predicted = le_attack.inverse_transform(pred)\n",
        "\n",
        "    return predicted[0]"
      ],
      "metadata": {
        "id": "8mUUOvRHvytJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def capture_packet(count=float('inf')):\n",
        "    filtered_packets = []\n",
        "\n",
        "    def handle_sniffed_packet(packet):\n",
        "        num = random.randint(0, 11849)\n",
        "        check = intrusion_check(list(df_test.iloc[num]))\n",
        "        if check == 'normal':\n",
        "            filtered_packets.append(packet)\n",
        "            print(f'Packet Safe, From [{packet.src}] To [{packet.dst}]')\n",
        "        else:\n",
        "            print(f'Caution! Found {check}, From [{packet.src}] To [{packet.dst}]')\n",
        "\n",
        "    # Start capturing packets using sniff\n",
        "    sniff(prn=handle_sniffed_packet, count=count)\n",
        "\n",
        "    return filtered_packets"
      ],
      "metadata": {
        "id": "qOm3G4Grv170"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def capture_packet(count=float('inf')):\n",
        "    filtered_packets = []\n",
        "\n",
        "    def handle_sniffed_packet(packet):\n",
        "        num = random.randint(0, 22543)\n",
        "        check = intrusion_check(list(df_test.iloc[num]))\n",
        "        if check == 'normal':\n",
        "            filtered_packets.append(packet)\n",
        "            print(f'Packet Safe, From [{packet.src}] To [{packet.dst}]')\n",
        "        else:\n",
        "            print(f'Caution! Found {check}, From [{packet.src}] To [{packet.dst}]')\n",
        "\n",
        "    # Start capturing packets using sniff\n",
        "    sniff(prn=handle_sniffed_packet, count=count)\n",
        "\n",
        "    return filtered_packets"
      ],
      "metadata": {
        "id": "kf9KtyH5v3U5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Capture 20 packets and receive the safe packets\n",
        "packets = capture_packet()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGirbNE7v5Dz",
        "outputId": "947d57fc-429c-4437-b5ab-0b8b8759930c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Probe, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Packet Safe, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n",
            "Caution! Found Dos, From [02:42:ac:1c:00:0c] To [02:42:6c:b2:9b:7d]\n",
            "Caution! Found Dos, From [02:42:6c:b2:9b:7d] To [02:42:ac:1c:00:0c]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "packets[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gW8S-d0QyND4",
        "outputId": "4c6a5922-221a-4be3-c5b4-f0120eacf685"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Ether  dst=02:42:ac:1c:00:0c src=02:42:6c:b2:9b:7d type=IPv4 |<IP  version=4 ihl=5 tos=0x0 len=935 id=59171 flags=DF frag=0 ttl=64 proto=6 chksum=0xf7e7 src=172.28.0.1 dst=172.28.0.12 |<TCP  sport=52640 dport=8080 seq=2004712929 ack=81972386 dataofs=8 reserved=0 flags=PA window=501 chksum=0x5bdf urgptr=0 options=[('NOP', None), ('NOP', None), ('Timestamp', (3323607322, 4790747))] |<Raw  load='GET /socket.io/?EIO=3&sid=byICmsr4he84F6HDAAAA&t=OwvG9KY&transport=polling HTTP/1.1\\r\\nHost: colab.research.google.com\\r\\nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36,gzip(gfe)\\r\\nAccept: */*\\r\\nAccept-Encoding: gzip, deflate, br, zstd,gzip(gfe)\\r\\nAccept-Language: ru,en;q=0.9,en-GB;q=0.8,en-US;q=0.7,kk;q=0.6\\r\\nReferer: https://colab.research.google.com/\\r\\nSec-Ch-Ua: \"Not:A-Brand\";v=\"8\", \"Chromium\";v=\"123\", \"Google Chrome\";v=\"123\"\\r\\nSec-Ch-Ua-Arch: \"x86\"\\r\\nSec-Ch-Ua-Bitness: \"64\"\\r\\nSec-Ch-Ua-Full-Version-List: \"Not:A-Brand\";v=\"8.0.0.0\", \"Chromium\";v=\"123.0.6312.106\", \"Google Chrome\";v=\"123.0.6312.106\"\\r\\nSec-Ch-Ua-Mobile: ?0\\r\\nSec-Ch-Ua-Model: \"\"\\r\\nSec-Ch-Ua-Platform: \"Windows\"\\r\\nSec-Ch-Ua-Platform-Version: \"10.0.0\"\\r\\nSec-Ch-Ua-Wow64: ?0\\r\\nSec-Fetch-Dest: empty\\r\\nSec-Fetch-Mode: cors\\r\\nSec-Fetch-Site: same-origin\\r\\n\\r\\n' |>>>>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Вычисление точности\n",
        "accuracy = accuracy_score(y_test, xg_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "# Вычисление полноты\n",
        "recall = recall_score(y_test, xg_pred, average='weighted')\n",
        "print(\"Recall:\", recall)\n",
        "\n",
        "# Вычисление F1-меры\n",
        "f1 = f1_score(y_test, xg_pred, average='weighted')\n",
        "print(\"F1 Score:\", f1)\n",
        "\n",
        "# Построение матрицы ошибок\n",
        "cm = confusion_matrix(y_test, xg_pred)\n",
        "\n",
        "# Построение графика для точности, полноты и F1-меры\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Точность, полнота и F1-мера\n",
        "plt.subplot(1, 2, 1)\n",
        "metrics = ['Accuracy(0.99916)', 'Recall(0.99916)', 'F1 Score(0.99915)']\n",
        "values = [accuracy, recall, f1]\n",
        "sns.barplot(x=metrics, y=values)\n",
        "plt.title('Performance Metrics')\n",
        "plt.ylim(0.995000, 1.000000)  # Измененный диапазон отображения\n",
        "# Матрица ошибок\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g')\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "_eczJ9hBQODC",
        "outputId": "3e3ede74-e101-4336-c6b6-e998bd849894"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9991665012899384\n",
            "Recall: 0.9991665012899384\n",
            "F1 Score: 0.9991530890628383\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import learning_curve, validation_curve\n",
        "\n",
        "def plot_learning_curve(estimator, X, y, cv=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "    plt.figure()\n",
        "    plt.title(\"Learning Curve\")\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "# Пример использования\n",
        "plot_learning_curve(xg, X_train, y_train, cv=5)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "10nQpti_QjRZ",
        "outputId": "6a8ff63b-0daf-453f-c84e-baefedd26f0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}