2_ACS.ipynb

{
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  "metadata": {
    "colab": {
      "name": "2-ACS.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNb5djItKXl5967gKSFlzd+",
      "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/laoktaviana/ACS-UAS/blob/main/2_ACS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Install python libraries"
      ],
      "metadata": {
        "id": "6_MKg9I0aEoD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "Mr17FT0T3jid"
      },
      "outputs": [],
      "source": [
        "!pip install requests sklearn pandas numpy matplotlib seaborn > /dev/nul"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Load libraries and declare vars"
      ],
      "metadata": {
        "id": "p3jteV7Cbucm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import requests\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree\n",
        "\n",
        "#File retrieved from the UNB/UCI site \n",
        "kdd99_names_url = 'http://kdd.ics.uci.edu/databases/kddcup99/kddcup.names'\n",
        "training_attack_types ='http://kdd.ics.uci.edu/databases/kddcup99/training_attack_types'\n",
        "kdd99_20p_filename = 'KDDTrain+_20Percent.csv'\n",
        "kdd99_20p_url = \"https://gist.githubusercontent.com/sblack4/3a34ebf5a8f02629f3f5ab8547c2f85f/raw/1985460fe1b036c28c0235871c6e770409be4e5b/KDDTrain+_20Percent.csv\""
      ],
      "metadata": {
        "id": "vyCxgsMg3rJ0"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Download remaning file"
      ],
      "metadata": {
        "id": "7ACvSOlicY8m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Download file containing the column names and map of the attacks also their type\n",
        "def dl_file(url):\n",
        "  filename = os.path.basename(url)\n",
        "  with open(filename, 'wb') as fh:\n",
        "    r = requests.get(url)\n",
        "    fh.write(r.content)\n",
        "  return filename\n",
        "\n",
        "files_to_dl = [kdd99_names_url, training_attack_types, kdd99_20p_url]\n",
        "\n",
        "list(map(dl_file, files_to_dl))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWiWSnw_35PU",
        "outputId": "8e18f061-f5bc-4075-8d90-488b29041775"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['kddcup.names', 'training_attack_types', 'KDDTrain+_20Percent.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. View file contents\n",
        "\n"
      ],
      "metadata": {
        "id": "WPd6ntw6c4z6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!head KDDTrain+_20Percent.txt\n",
        "#The head command is used to quickly get these first few from through the shell. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icRVScC3387w",
        "outputId": "92b7ce67-b2cd-471d-e691-1fc8ed745e87"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "head: cannot open 'KDDTrain+_20Percent.txt' for reading: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head kddcup.names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uJnnOYI3_Tl",
        "outputId": "a9385654-562b-4bf8-a1c8-1819f55de3fb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "back,buffer_overflow,ftp_write,guess_passwd,imap,ipsweep,land,loadmodule,multihop,neptune,nmap,normal,perl,phf,pod,portsweep,rootkit,satan,smurf,spy,teardrop,warezclient,warezmaster.\n",
            "duration: continuous.\n",
            "protocol_type: symbolic.\n",
            "service: symbolic.\n",
            "flag: symbolic.\n",
            "src_bytes: continuous.\n",
            "dst_bytes: continuous.\n",
            "land: symbolic.\n",
            "wrong_fragment: continuous.\n",
            "urgent: continuous.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head training_attack_types"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMRioM1Y4CCN",
        "outputId": "91f15095-81fe-45ef-ee89-53872a5fbcd6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "back dos\n",
            "buffer_overflow u2r\n",
            "ftp_write r2l\n",
            "guess_passwd r2l\n",
            "imap r2l\n",
            "ipsweep probe\n",
            "land dos\n",
            "loadmodule u2r\n",
            "multihop r2l\n",
            "neptune dos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Load labels and data"
      ],
      "metadata": {
        "id": "Q3rCYItqdkja"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The first file\n",
        "with open('kddcup.names', 'r') as fh:\n",
        "  fh.readline() # get rid of the first line\n",
        "  lines = fh.readlines()\n",
        "\n",
        "col_names = []\n",
        "for line in lines:\n",
        "  name = line.split(':')[0]\n",
        "  col_names.append(name)\n",
        "\n",
        "# the last one is the target, attack_type\n",
        "col_names.append('attack_type')\n",
        "col_names.append('successfulPredictions')"
      ],
      "metadata": {
        "id": "hyD0HW1j4S1f"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fig1. Column Names\n",
        "for nm in col_names: print('  - ' + nm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OJ8iwwX4VsY",
        "outputId": "04ea911e-5e00-4b64-93d6-dd77b20428c3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  - duration\n",
            "  - protocol_type\n",
            "  - service\n",
            "  - flag\n",
            "  - src_bytes\n",
            "  - dst_bytes\n",
            "  - land\n",
            "  - wrong_fragment\n",
            "  - urgent\n",
            "  - hot\n",
            "  - num_failed_logins\n",
            "  - logged_in\n",
            "  - num_compromised\n",
            "  - root_shell\n",
            "  - su_attempted\n",
            "  - num_root\n",
            "  - num_file_creations\n",
            "  - num_shells\n",
            "  - num_access_files\n",
            "  - num_outbound_cmds\n",
            "  - is_host_login\n",
            "  - is_guest_login\n",
            "  - count\n",
            "  - srv_count\n",
            "  - serror_rate\n",
            "  - srv_serror_rate\n",
            "  - rerror_rate\n",
            "  - srv_rerror_rate\n",
            "  - same_srv_rate\n",
            "  - diff_srv_rate\n",
            "  - srv_diff_host_rate\n",
            "  - dst_host_count\n",
            "  - dst_host_srv_count\n",
            "  - dst_host_same_srv_rate\n",
            "  - dst_host_diff_srv_rate\n",
            "  - dst_host_same_src_port_rate\n",
            "  - dst_host_srv_diff_host_rate\n",
            "  - dst_host_serror_rate\n",
            "  - dst_host_srv_serror_rate\n",
            "  - dst_host_rerror_rate\n",
            "  - dst_host_srv_rerror_rate\n",
            "  - attack_type\n",
            "  - successfulPredictions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The second file\n",
        "#This mapping reduce the 23 attacks to 4 types of attacks. \n",
        "with open('training_attack_types', 'r') as fh:\n",
        "  lines = fh.readlines()\n",
        "\n",
        "print('-'*24)\n",
        "print(f'{\"Attack \".rjust(16, \" \")}-> type')\n",
        "print('-'*24)\n",
        "attack_types = {}\n",
        "for line in lines:\n",
        "  line = line.replace('\\n', '')\n",
        "  if not line: continue\n",
        "  typ = line.split(' ')[0]\n",
        "  cls = line.split(' ')[1]\n",
        "  attack_types[typ] = cls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5eXMuqR4ZoM",
        "outputId": "ede11621-ddc5-4c1e-c40d-3b5a6b078c77"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------\n",
            "         Attack -> type\n",
            "------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Not just records of attacks but also records of a normal state for system\n",
        "for typ, cls in attack_types.items(): print(f'{typ.ljust(16, \" \")}-> {cls}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBLJQ-4F4bzn",
        "outputId": "7a12c42e-1792-432c-c093-de5cea2f4c4d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "back            -> dos\n",
            "buffer_overflow -> u2r\n",
            "ftp_write       -> r2l\n",
            "guess_passwd    -> r2l\n",
            "imap            -> r2l\n",
            "ipsweep         -> probe\n",
            "land            -> dos\n",
            "loadmodule      -> u2r\n",
            "multihop        -> r2l\n",
            "neptune         -> dos\n",
            "nmap            -> probe\n",
            "perl            -> u2r\n",
            "phf             -> r2l\n",
            "pod             -> dos\n",
            "portsweep       -> probe\n",
            "rootkit         -> u2r\n",
            "satan           -> probe\n",
            "smurf           -> dos\n",
            "spy             -> r2l\n",
            "teardrop        -> dos\n",
            "warezclient     -> r2l\n",
            "warezmaster     -> r2l\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. Creat DataFrame from labels and data"
      ],
      "metadata": {
        "id": "GrOEvOj8f_4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#load data file\n",
        "#pandas library used to read file into a DataFrame with the column names that was list above. \n",
        "#attack_type is appended as the attack type\n",
        "kdd99_20p_df = pd.read_csv(kdd99_20p_filename, names=col_names, index_col=None)\n",
        "\n",
        "kdd99_20p_df['attack_class'] = kdd99_20p_df.apply(\n",
        "    lambda x: 'normal' if x['attack_type'] == 'normal' else attack_types[x['attack_type']], \n",
        "    axis=1)"
      ],
      "metadata": {
        "id": "o2kFXJlX4fR_"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The first five lines at the head of the DataFrame\n",
        "kdd99_20p_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "AaNsosTq4hNv",
        "outputId": "6dd1dc33-077e-4dad-abf5-7479c20bae2f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-41540ab4-efd2-42c8-aad9-1c323f46b74a\">\n",
              "    <div 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>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</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_type</th>\n",
              "      <th>successfulPredictions</th>\n",
              "      <th>attack_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>491</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>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>150</td>\n",
              "      <td>25</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>20</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</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>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</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>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>15</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</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>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</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>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "      <td>dos</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>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>30</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>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</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>0</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>32</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>255</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>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41540ab4-efd2-42c8-aad9-1c323f46b74a')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-41540ab4-efd2-42c8-aad9-1c323f46b74a 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-41540ab4-efd2-42c8-aad9-1c323f46b74a');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   duration protocol_type  ... successfulPredictions attack_class\n",
              "0         0           tcp  ...                    20       normal\n",
              "1         0           udp  ...                    15       normal\n",
              "2         0           tcp  ...                    19          dos\n",
              "3         0           tcp  ...                    21       normal\n",
              "4         0           tcp  ...                    21       normal\n",
              "\n",
              "[5 rows x 44 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------------------------"
      ],
      "metadata": {
        "id": "IENUTKsrhGDR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Data statistic\n",
        "\n"
      ],
      "metadata": {
        "id": "O_U_hOsKhJMK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#General shape of the sataset by rows, cols\n",
        "kdd99_20p_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkfDJ29A4wWD",
        "outputId": "0ba3f062-db73-4f80-ea74-06258d171d4e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25192, 44)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Descriptive statistics for all numerical columns in dataset\n",
        "kdd99_20p_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "76jCvV-V4yWb",
        "outputId": "db2b72d1-d6f1-4b2b-b38c-74efeef0f3d4"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e595f45a-5ba0-41a4-862a-789d4b483537\">\n",
              "    <div 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>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>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</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>successfulPredictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25192.000000</td>\n",
              "      <td>2.519200e+04</td>\n",
              "      <td>2.519200e+04</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.00000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.0</td>\n",
              "      <td>25192.0</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>305.054104</td>\n",
              "      <td>2.433063e+04</td>\n",
              "      <td>3.491847e+03</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.023738</td>\n",
              "      <td>0.00004</td>\n",
              "      <td>0.198039</td>\n",
              "      <td>0.001191</td>\n",
              "      <td>0.394768</td>\n",
              "      <td>0.227850</td>\n",
              "      <td>0.001548</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.249841</td>\n",
              "      <td>0.014727</td>\n",
              "      <td>0.000357</td>\n",
              "      <td>0.004327</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009130</td>\n",
              "      <td>84.591180</td>\n",
              "      <td>27.698754</td>\n",
              "      <td>0.286338</td>\n",
              "      <td>0.283762</td>\n",
              "      <td>0.118630</td>\n",
              "      <td>0.120260</td>\n",
              "      <td>0.660559</td>\n",
              "      <td>0.062363</td>\n",
              "      <td>0.095931</td>\n",
              "      <td>182.532074</td>\n",
              "      <td>115.063036</td>\n",
              "      <td>0.519791</td>\n",
              "      <td>0.082539</td>\n",
              "      <td>0.147453</td>\n",
              "      <td>0.031844</td>\n",
              "      <td>0.285800</td>\n",
              "      <td>0.279846</td>\n",
              "      <td>0.117800</td>\n",
              "      <td>0.118769</td>\n",
              "      <td>19.487695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2686.555640</td>\n",
              "      <td>2.410805e+06</td>\n",
              "      <td>8.883072e+04</td>\n",
              "      <td>0.008910</td>\n",
              "      <td>0.260221</td>\n",
              "      <td>0.00630</td>\n",
              "      <td>2.154202</td>\n",
              "      <td>0.045418</td>\n",
              "      <td>0.488811</td>\n",
              "      <td>10.417352</td>\n",
              "      <td>0.039316</td>\n",
              "      <td>0.048785</td>\n",
              "      <td>11.500842</td>\n",
              "      <td>0.529602</td>\n",
              "      <td>0.018898</td>\n",
              "      <td>0.098524</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.095115</td>\n",
              "      <td>114.673451</td>\n",
              "      <td>72.468242</td>\n",
              "      <td>0.447312</td>\n",
              "      <td>0.447599</td>\n",
              "      <td>0.318745</td>\n",
              "      <td>0.322335</td>\n",
              "      <td>0.439637</td>\n",
              "      <td>0.178550</td>\n",
              "      <td>0.256583</td>\n",
              "      <td>98.993895</td>\n",
              "      <td>110.646850</td>\n",
              "      <td>0.448944</td>\n",
              "      <td>0.187191</td>\n",
              "      <td>0.308367</td>\n",
              "      <td>0.110575</td>\n",
              "      <td>0.445316</td>\n",
              "      <td>0.446075</td>\n",
              "      <td>0.305869</td>\n",
              "      <td>0.317333</td>\n",
              "      <td>2.328541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.050000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.790000e+02</td>\n",
              "      <td>5.302500e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>18.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.060000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42862.000000</td>\n",
              "      <td>3.817091e+08</td>\n",
              "      <td>5.151385e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.00000</td>\n",
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              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>884.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>511.000000</td>\n",
              "      <td>511.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
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              "  "
            ],
            "text/plain": [
              "           duration  ...  successfulPredictions\n",
              "count  25192.000000  ...           25192.000000\n",
              "mean     305.054104  ...              19.487695\n",
              "std     2686.555640  ...               2.328541\n",
              "min        0.000000  ...               0.000000\n",
              "25%        0.000000  ...              18.000000\n",
              "50%        0.000000  ...              20.000000\n",
              "75%        0.000000  ...              21.000000\n",
              "max    42862.000000  ...              21.000000\n",
              "\n",
              "[8 rows x 39 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 48
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Description of categorical variables\n",
        "#Refreshing chart showing some of the variables are investigated further with two charts showing the distributions for the attack types \n",
        "kdd99_20p_df.describe(include=['object'])"
      ],
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        {
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          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>attack_type</th>\n",
              "      <th>attack_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3</td>\n",
              "      <td>66</td>\n",
              "      <td>11</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>20526</td>\n",
              "      <td>8003</td>\n",
              "      <td>14973</td>\n",
              "      <td>13449</td>\n",
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            ],
            "text/plain": [
              "       protocol_type service   flag attack_type attack_class\n",
              "count          25192   25192  25192       25192        25192\n",
              "unique             3      66     11          22            5\n",
              "top              tcp    http     SF      normal       normal\n",
              "freq           20526    8003  14973       13449        13449"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bar graph of attack type\n",
        "#The largest class= normal\n",
        "#The attack type= neptune\n",
        "fig = plt.figure()\n",
        "fig = kdd99_20p_df['attack_type'].value_counts().plot(figsize=(20, 15), kind=\"barh\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "zL1LRNBL43A3",
        "outputId": "f5a53b81-e050-4ee8-b81a-90970f128bf0"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bar attack-class classification\n",
        "plt.figure(figsize=(15, 10))\n",
        "kdd99_20p_df['attack_class'].value_counts().plot(kind=\"barh\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "QJfAbMJB47Ho",
        "outputId": "63510d64-f21c-499f-e14c-7858b633a9bc"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pairwise correlation for float point type values\n",
        "#Convariance between numerical features. \n",
        "corr = kdd99_20p_df.loc[:,kdd99_20p_df.dtypes == 'float64'].corr()\n",
        "\n",
        "plt.figure(figsize = (15,10))\n",
        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, \n",
        "            cmap=sns.diverging_palette(220, 10, as_cmap=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "APV0YGsS49nM",
        "outputId": "4b2eb8d2-dd79-4b15-ca1b-b7328b795a55"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd9c0f2fb90>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Engineering Data"
      ],
      "metadata": {
        "id": "CUz1IBXDlDRd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Binary classification of dataset\n",
        "#Dataset was further classified into binary types of attack or not attack (normal). This column was appeded to DataFrame\n",
        "kdd99_20p_df['attack_biclass'] = kdd99_20p_df.apply(\n",
        "    lambda x: 'normal' if x['attack_type'] == 'normal' else 'attack', \n",
        "    axis=1)"
      ],
      "metadata": {
        "id": "blpTJgSo5EoL"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Remove highly correlated features because make unctable decisions trees.\n",
        "kdd99_20p_dropped = kdd99_20p_df.drop(['srv_serror_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate'], axis=1)\n",
        "kdd99_20p_dropped = kdd99_20p_dropped.drop(['attack_type', 'attack_biclass', 'successfulPredictions'], axis=1)"
      ],
      "metadata": {
        "id": "AqcgizaS5I4X"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Data sampling"
      ],
      "metadata": {
        "id": "c-pqEZuAqkU_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Three sample: Random Sample, Uniform Distribution, Tree Sample\n",
        "#Each sample contains 400 elements\n",
        "n_sample = 400\n",
        "random_sample = kdd99_20p_dropped.sample(n_sample)\n",
        "uniform_sample = kdd99_20p_dropped.groupby('attack_class', group_keys=False)\\\n",
        "                                  .apply(lambda x: x.sample(int(n_sample/4), replace=True))\n",
        "test_sample = kdd99_20p_dropped.sample(n_sample)"
      ],
      "metadata": {
        "id": "FLNLMO1F5K3H"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Randome sample attack classes\n",
        "random_sample.attack_class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DbN3Hfpl5OdM",
        "outputId": "04d04c7a-37e5-46f4-879d-79fcaed5ed81"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "normal    220\n",
              "dos       153\n",
              "probe      25\n",
              "r2l         2\n",
              "Name: attack_class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Uniform sample attack classes\n",
        "uniform_sample.attack_class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inbgohB_5QGH",
        "outputId": "f22574d2-bf46-40dd-a73c-3283c70eadbc"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "probe     100\n",
              "r2l       100\n",
              "dos       100\n",
              "normal    100\n",
              "u2r       100\n",
              "Name: attack_class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Test sample attack classes\n",
        "test_sample.attack_class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGNcHrT_5R7-",
        "outputId": "06d782c9-b2a6-49dd-a28c-37aa3e6fe390"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "normal    232\n",
              "dos       123\n",
              "probe      44\n",
              "r2l         1\n",
              "Name: attack_class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Data analysis and results"
      ],
      "metadata": {
        "id": "8T4R8uPzrFUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating decision trees\n",
        "#function was two dataframes and compute decision tree information. \n",
        "\n",
        "from sklearn import metrics #Used to generate and graph decisionn tree\n",
        "\n",
        "def make_tree(train_df, test_df, export_depth=2):\n",
        "  ''' create, plot, score, and retrun tree '''\n",
        "\n",
        "  y_train = random_sample['attack_class']\n",
        "  x_train = random_sample.select_dtypes(exclude='object')\n",
        "\n",
        "  y_test = test_df['attack_class']\n",
        "  x_test = test_df.select_dtypes(exclude='object')\n",
        "\n",
        "  dtc = DecisionTreeClassifier(criterion='entropy', random_state=0, max_depth=16)\n",
        "  dtc.fit(x_train, y_train)\n",
        "\n",
        "  #Confusion matrix printed below the graphical representation of the decision tree\n",
        "  #Confussion matrix allows assessment of our predictor on a class-level\n",
        "  print(f'The test score is {dtc.score(x_test, y_test)}') \n",
        "  plt.figure(figsize=(15,10))\n",
        "  plot_tree(dtc, class_names=dtc.classes_, feature_names=x_train.columns, filled=True, \n",
        "            fontsize=10, max_depth=2, precision=1, label=None, impurity=False)\n",
        "  \n",
        "  cm = metrics.plot_confusion_matrix(dtc, x_test, y_test)\n",
        "  cm.figure_.suptitle(\"Confusion Matrix\")\n",
        "\n",
        "  plt.show()\n",
        "  return export_text(dtc, feature_names=list(x_train.columns), max_depth=export_depth)"
      ],
      "metadata": {
        "id": "-daEaZNC5Vzp"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Decision tree for random sample with confusion matrix\n",
        "random_txt = make_tree(random_sample, test_sample)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "id": "J0db3ZkUZdUR",
        "outputId": "61222b52-9423-43be-e7dc-1e2bef94c64a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The test score is 0.9475\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Decision tree for uniform sample with confusion matrix\n",
        "uni_txt = make_tree(uniform_sample, test_sample)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "id": "tKpkK5MoZg63",
        "outputId": "13de1548-815b-4c90-c1c0-f88e8bbd8e94"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The test score is 0.9475\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------------------\n"
      ],
      "metadata": {
        "id": "x8miVjVqsynE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Conclusion\n",
        "\n",
        "Table show are not the full from the trees but here is a tree truncated to 2 nodes past root. These two trees are very much alike, identically, and hight-level. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "BGnDZ1SBs1KJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Side-by-side comparison of decision trees\n",
        "import difflib #Create side-by-side comparison for two tree\n",
        "from IPython.core.display import display, HTML\n",
        "d = difflib.HtmlDiff().make_table(random_txt.split('\\n'), uni_txt.split('\\n'),\n",
        "                                   fromdesc='Tree1', todesc='Tree2')\n",
        "HTML(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "gPg3P5bPZoGH",
        "outputId": "f5f4749a-881a-4265-997d-964bf027cde3"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "    <table class=\"diff\" id=\"difflib_chg_to1__top\"\n",
              "           cellspacing=\"0\" cellpadding=\"0\" rules=\"groups\" >\n",
              "        <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>\n",
              "        <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>\n",
              "        <thead><tr><th class=\"diff_next\"><br /></th><th colspan=\"2\" class=\"diff_header\">Tree1</th><th class=\"diff_next\"><br /></th><th colspan=\"2\" class=\"diff_header\">Tree2</th></tr></thead>\n",
              "        <tbody>\n",
              "            <tr><td class=\"diff_next\"><a href=\"#difflib_chg_to1__top\">t</a></td><td class=\"diff_header\" id=\"from1_1\">1</td><td nowrap=\"nowrap\">|---&nbsp;src_bytes&nbsp;&lt;=&nbsp;29.00</td><td class=\"diff_next\"><a href=\"#difflib_chg_to1__top\">t</a></td><td class=\"diff_header\" id=\"to1_1\">1</td><td nowrap=\"nowrap\">|---&nbsp;src_bytes&nbsp;&lt;=&nbsp;29.00</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_2\">2</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;count&nbsp;&lt;=&nbsp;10.50</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_2\">2</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;count&nbsp;&lt;=&nbsp;10.50</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_3\">3</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_same_src_port_rate&nbsp;&lt;=&nbsp;0.88</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_3\">3</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_same_src_port_rate&nbsp;&lt;=&nbsp;0.88</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_4\">4</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;4</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_4\">4</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;4</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_5\">5</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_same_src_port_rate&nbsp;&gt;&nbsp;&nbsp;0.88</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_5\">5</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_same_src_port_rate&nbsp;&gt;&nbsp;&nbsp;0.88</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_6\">6</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;3</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_6\">6</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;3</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_7\">7</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;count&nbsp;&gt;&nbsp;&nbsp;10.50</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_7\">7</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;count&nbsp;&gt;&nbsp;&nbsp;10.50</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_8\">8</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_diff_srv_rate&nbsp;&lt;=&nbsp;0.11</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_8\">8</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_diff_srv_rate&nbsp;&lt;=&nbsp;0.11</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_9\">9</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;2</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_9\">9</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;2</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_10\">10</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_diff_srv_rate&nbsp;&gt;&nbsp;&nbsp;0.11</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_10\">10</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_diff_srv_rate&nbsp;&gt;&nbsp;&nbsp;0.11</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_11\">11</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;2</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_11\">11</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;2</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_12\">12</td><td nowrap=\"nowrap\">|---&nbsp;src_bytes&nbsp;&gt;&nbsp;&nbsp;29.00</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_12\">12</td><td nowrap=\"nowrap\">|---&nbsp;src_bytes&nbsp;&gt;&nbsp;&nbsp;29.00</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_13\">13</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;src_bytes&nbsp;&lt;=&nbsp;518.00</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_13\">13</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;src_bytes&nbsp;&lt;=&nbsp;518.00</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_14\">14</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_count&nbsp;&lt;=&nbsp;21.50</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_14\">14</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_count&nbsp;&lt;=&nbsp;21.50</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_15\">15</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;4</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_15\">15</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;4</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_16\">16</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_count&nbsp;&gt;&nbsp;&nbsp;21.50</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_16\">16</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_count&nbsp;&gt;&nbsp;&nbsp;21.50</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_17\">17</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;class:&nbsp;normal</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_17\">17</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;class:&nbsp;normal</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_18\">18</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;src_bytes&nbsp;&gt;&nbsp;&nbsp;518.00</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_18\">18</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|---&nbsp;src_bytes&nbsp;&gt;&nbsp;&nbsp;518.00</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_19\">19</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_diff_host_rate&nbsp;&lt;=&nbsp;0.00</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_19\">19</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_diff_host_rate&nbsp;&lt;=&nbsp;0.00</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_20\">20</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;3</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_20\">20</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;truncated&nbsp;branch&nbsp;of&nbsp;depth&nbsp;3</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_21\">21</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_diff_host_rate&nbsp;&gt;&nbsp;&nbsp;0.00</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_21\">21</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;dst_host_srv_diff_host_rate&nbsp;&gt;&nbsp;&nbsp;0.00</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_22\">22</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;class:&nbsp;normal</td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_22\">22</td><td nowrap=\"nowrap\">|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;|---&nbsp;class:&nbsp;normal</td></tr>\n",
              "            <tr><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"from1_23\">23</td><td nowrap=\"nowrap\"></td><td class=\"diff_next\"></td><td class=\"diff_header\" id=\"to1_23\">23</td><td nowrap=\"nowrap\"></td></tr>\n",
              "        </tbody>\n",
              "    </table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    }
  ]
}