_Classify_DDoS_Network_Attacks_Predicting_Network_Attacks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "@ Classify DDoS Network Attacks@ Predicting Network Attacks.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JosephBless/Deep-Learning-Colab/blob/main/_Classify_DDoS_Network_Attacks_Predicting_Network_Attacks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzJ8mgXup_wV"
      },
      "source": [
        "#Building a Predictive Model to Classify Network Attacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yb0-OE5hlusS"
      },
      "source": [
        "import os\n",
        "from collections import defaultdict\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w13Io0gelxMH"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLAyU020l03H"
      },
      "source": [
        "#PreProcessing\n",
        "Check out the dataset: https://www.unb.ca/cic/datasets/nsl.html\n",
        "\n",
        "Types of attack: http://kdd.ics.uci.edu/databases/kddcup99/training_attack_types\n",
        "\n",
        "Column names: http://kdd.ics.uci.edu/databases/kddcup99/kddcup.names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLq4pPXQly-k",
        "outputId": "f8df4f62-c105-4681-b6b4-0646a5760b45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive/My\\ Drive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n",
            "/gdrive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueVXbc61l56U"
      },
      "source": [
        "#os.listdir()\n",
        "dataset_root = os.path.join(os.getcwd(),'Datasets/NSL-KDD')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPauQXOAmGIB"
      },
      "source": [
        "train_file = os.path.join(dataset_root, 'KDDTrain+.txt')\n",
        "test_file = os.path.join(dataset_root, 'KDDTest+.txt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHDfemmsmcNg"
      },
      "source": [
        "header_names = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', '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', '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', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'attack_type', 'success_pred']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vU2FBTK2o_re"
      },
      "source": [
        "col_names = np.array(header_names)\n",
        "\n",
        "nominal_idx = [1, 2, 3]\n",
        "binary_idx = [6, 11, 13, 14, 20, 21]\n",
        "numeric_idx = list(set(range(41)).difference(nominal_idx).difference(binary_idx))\n",
        "\n",
        "nominal_cols = col_names[nominal_idx].tolist()\n",
        "binary_cols = col_names[binary_idx].tolist()\n",
        "numeric_cols = col_names[numeric_idx].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kh9cxuCYqDvc"
      },
      "source": [
        "category = defaultdict(list)\n",
        "category['benign'].append('normal')\n",
        "\n",
        "name = os.path.join(dataset_root,'name.txt')\n",
        "with open(name, 'r') as f:\n",
        "    for line in f.readlines():\n",
        "        #print(line)\n",
        "        attack, cat = line.strip().split(' ')\n",
        "        category[cat].append(attack)\n",
        "\n",
        "attack_mapping = dict((v,k) for k in category for v in category[k])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i62KQmLkqJHI",
        "outputId": "f9ad0b2e-6cd8-43b7-f73d-3be184da89c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        }
      },
      "source": [
        "attack_mapping"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'apache2': 'dos',\n",
              " 'back': 'dos',\n",
              " 'buffer_overflow': 'u2r',\n",
              " 'ftp_write': 'r2l',\n",
              " 'guess_passwd': 'r2l',\n",
              " 'httptunnel': 'r2l',\n",
              " 'imap': 'r2l',\n",
              " 'ipsweep': 'probe',\n",
              " 'land': 'dos',\n",
              " 'loadmodule': 'u2r',\n",
              " 'mailbomb': 'dos',\n",
              " 'mscan': 'probe',\n",
              " 'multihop': 'r2l',\n",
              " 'named': 'r2l',\n",
              " 'neptune': 'dos',\n",
              " 'nmap': 'probe',\n",
              " 'normal': 'benign',\n",
              " 'perl': 'u2r',\n",
              " 'phf': 'r2l',\n",
              " 'pod': 'dos',\n",
              " 'portsweep': 'probe',\n",
              " 'processtable': 'dos',\n",
              " 'ps': 'u2r',\n",
              " 'rootkit': 'u2r',\n",
              " 'saint': 'probe',\n",
              " 'satan': 'probe',\n",
              " 'sendmail': 'r2l',\n",
              " 'smurf': 'dos',\n",
              " 'snmpgetattack': 'r2l',\n",
              " 'snmpguess': 'r2l',\n",
              " 'spy': 'r2l',\n",
              " 'sqlattack': 'u2r',\n",
              " 'teardrop': 'dos',\n",
              " 'udpstorm': 'dos',\n",
              " 'warezclient': 'r2l',\n",
              " 'warezmaster': 'r2l',\n",
              " 'worm': 'dos',\n",
              " 'xlock': 'r2l',\n",
              " 'xsnoop': 'r2l',\n",
              " 'xterm': 'u2r'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6i9cGPsrPzP"
      },
      "source": [
        "train_df = pd.read_csv(train_file, names=header_names)\n",
        "train_df['attack_category'] = train_df['attack_type'].map(lambda x: attack_mapping[x])\n",
        "train_df.drop(['success_pred'], axis=1, inplace=True)\n",
        "    \n",
        "test_df = pd.read_csv(test_file, names=header_names)\n",
        "test_df['attack_category'] = test_df['attack_type'].map(lambda x: attack_mapping[x])\n",
        "test_df.drop(['success_pred'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "745IJDYfrd_A"
      },
      "source": [
        "train_attack_types = train_df['attack_type'].value_counts()\n",
        "train_attack_cats = train_df['attack_category'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ky3JjjmtLWb"
      },
      "source": [
        "test_attack_types = test_df['attack_type'].value_counts()\n",
        "test_attack_cats = test_df['attack_category'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDU08ZNy0SIS",
        "outputId": "370c7e63-fbf5-4b48-f14a-34ffa7f34af1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "train_attack_types.plot(kind='barh', figsize=(20,10), fontsize=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff723ff00b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABSMAAAJICAYAAACaIvGrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "peJ99axm0TsI",
        "outputId": "60b77f4b-44d4-4072-ecaa-3a76f9f9a9b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        }
      },
      "source": [
        "train_attack_cats.plot(kind='barh', figsize=(20,10), fontsize=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff723a67860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABP0AAAJSCAYAAAC4ObBsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-auNLpX0X5X",
        "outputId": "471fe78f-84d5-4e8b-a5df-164c3ceb1177",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        }
      },
      "source": [
        "test_attack_cats.plot(kind='barh', figsize=(20,10), fontsize=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff723f6de80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABPIAAAJSCAYAAABJMuvhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9p5yxo6x0hr4",
        "outputId": "6babb1e7-e485-4c39-8152-16ab9394cd9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "train_df[binary_cols].describe().transpose()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>land</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.014086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>logged_in</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>0.395736</td>\n",
              "      <td>0.489010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>root_shell</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.036603</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>su_attempted</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>0.001103</td>\n",
              "      <td>0.045154</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_host_login</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.002817</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_guest_login</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>0.009423</td>\n",
              "      <td>0.096612</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   count      mean       std  min  25%  50%  75%  max\n",
              "land            125973.0  0.000198  0.014086  0.0  0.0  0.0  0.0  1.0\n",
              "logged_in       125973.0  0.395736  0.489010  0.0  0.0  0.0  1.0  1.0\n",
              "root_shell      125973.0  0.001342  0.036603  0.0  0.0  0.0  0.0  1.0\n",
              "su_attempted    125973.0  0.001103  0.045154  0.0  0.0  0.0  0.0  2.0\n",
              "is_host_login   125973.0  0.000008  0.002817  0.0  0.0  0.0  0.0  1.0\n",
              "is_guest_login  125973.0  0.009423  0.096612  0.0  0.0  0.0  0.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ol8E1TzZ0oDt",
        "outputId": "220cfe14-89af-4407-c2af-2ba703aefe55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_df.groupby(['su_attempted']).size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "su_attempted\n",
              "0    125893\n",
              "1        21\n",
              "2        59\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvgOC_Z413oO",
        "outputId": "668e2878-b587-40b7-85d6-57eb47cae10c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "train_df['su_attempted'].replace(2, 0, inplace=True)\n",
        "test_df['su_attempted'].replace(2, 0, inplace=True)\n",
        "train_df.groupby(['su_attempted']).size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "su_attempted\n",
              "0    125952\n",
              "1        21\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdxhBdhR18v9",
        "outputId": "6032e938-e697-4093-8f10-c604c24cc77a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "train_df.groupby(['num_outbound_cmds']).size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num_outbound_cmds\n",
              "0    125973\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CQ2iYih2HCB"
      },
      "source": [
        "train_df.drop('num_outbound_cmds', axis = 1, inplace=True)\n",
        "test_df.drop('num_outbound_cmds', axis = 1, inplace=True)\n",
        "numeric_cols.remove('num_outbound_cmds')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilZbLRTV2JwQ"
      },
      "source": [
        "train_Y = train_df['attack_category']\n",
        "train_x_raw = train_df.drop(['attack_category','attack_type'], axis=1)\n",
        "test_Y = test_df['attack_category']\n",
        "test_x_raw = test_df.drop(['attack_category','attack_type'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnGYnchi2NY8"
      },
      "source": [
        "combined_df_raw = pd.concat([train_x_raw, test_x_raw])\n",
        "combined_df = pd.get_dummies(combined_df_raw, columns=nominal_cols, drop_first=True)\n",
        "\n",
        "train_x = combined_df[:len(train_x_raw)]\n",
        "test_x = combined_df[len(train_x_raw):]\n",
        "\n",
        "# Store dummy variable feature names\n",
        "dummy_variables = list(set(train_x)-set(combined_df_raw))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGHCvWRH2PAY",
        "outputId": "b2d080c6-c49d-4a5e-db7b-538803acb6d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "train_x.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>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>protocol_type_tcp</th>\n",
              "      <th>protocol_type_udp</th>\n",
              "      <th>service_X11</th>\n",
              "      <th>...</th>\n",
              "      <th>service_netstat</th>\n",
              "      <th>service_nnsp</th>\n",
              "      <th>service_nntp</th>\n",
              "      <th>service_ntp_u</th>\n",
              "      <th>service_other</th>\n",
              "      <th>service_pm_dump</th>\n",
              "      <th>service_pop_2</th>\n",
              "      <th>service_pop_3</th>\n",
              "      <th>service_printer</th>\n",
              "      <th>service_private</th>\n",
              "      <th>service_red_i</th>\n",
              "      <th>service_remote_job</th>\n",
              "      <th>service_rje</th>\n",
              "      <th>service_shell</th>\n",
              "      <th>service_smtp</th>\n",
              "      <th>service_sql_net</th>\n",
              "      <th>service_ssh</th>\n",
              "      <th>service_sunrpc</th>\n",
              "      <th>service_supdup</th>\n",
              "      <th>service_systat</th>\n",
              "      <th>service_telnet</th>\n",
              "      <th>service_tftp_u</th>\n",
              "      <th>service_tim_i</th>\n",
              "      <th>service_time</th>\n",
              "      <th>service_urh_i</th>\n",
              "      <th>service_urp_i</th>\n",
              "      <th>service_uucp</th>\n",
              "      <th>service_uucp_path</th>\n",
              "      <th>service_vmnet</th>\n",
              "      <th>service_whois</th>\n",
              "      <th>flag_REJ</th>\n",
              "      <th>flag_RSTO</th>\n",
              "      <th>flag_RSTOS0</th>\n",
              "      <th>flag_RSTR</th>\n",
              "      <th>flag_S0</th>\n",
              "      <th>flag_S1</th>\n",
              "      <th>flag_S2</th>\n",
              "      <th>flag_S3</th>\n",
              "      <th>flag_SF</th>\n",
              "      <th>flag_SH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>125973.00000</td>\n",
              "      <td>1.259730e+05</td>\n",
              "      <td>1.259730e+05</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.00000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.00000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>287.14465</td>\n",
              "      <td>4.556674e+04</td>\n",
              "      <td>1.977911e+04</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.022687</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.204409</td>\n",
              "      <td>0.001222</td>\n",
              "      <td>0.395736</td>\n",
              "      <td>0.279250</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.000167</td>\n",
              "      <td>0.302192</td>\n",
              "      <td>0.012669</td>\n",
              "      <td>0.000413</td>\n",
              "      <td>0.004096</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.009423</td>\n",
              "      <td>84.107555</td>\n",
              "      <td>27.737888</td>\n",
              "      <td>0.284485</td>\n",
              "      <td>0.282485</td>\n",
              "      <td>0.119958</td>\n",
              "      <td>0.121183</td>\n",
              "      <td>0.660928</td>\n",
              "      <td>0.063053</td>\n",
              "      <td>0.097322</td>\n",
              "      <td>182.148945</td>\n",
              "      <td>115.653005</td>\n",
              "      <td>0.521242</td>\n",
              "      <td>0.082951</td>\n",
              "      <td>0.148379</td>\n",
              "      <td>0.032542</td>\n",
              "      <td>0.284452</td>\n",
              "      <td>0.278485</td>\n",
              "      <td>0.118832</td>\n",
              "      <td>0.120240</td>\n",
              "      <td>0.815167</td>\n",
              "      <td>0.119018</td>\n",
              "      <td>0.000579</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002858</td>\n",
              "      <td>0.005001</td>\n",
              "      <td>0.002350</td>\n",
              "      <td>0.001334</td>\n",
              "      <td>0.034603</td>\n",
              "      <td>0.00004</td>\n",
              "      <td>0.000619</td>\n",
              "      <td>0.002096</td>\n",
              "      <td>0.000548</td>\n",
              "      <td>0.173474</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000619</td>\n",
              "      <td>0.000683</td>\n",
              "      <td>0.000516</td>\n",
              "      <td>0.058052</td>\n",
              "      <td>0.001945</td>\n",
              "      <td>0.002469</td>\n",
              "      <td>0.003024</td>\n",
              "      <td>0.004318</td>\n",
              "      <td>0.003787</td>\n",
              "      <td>0.018679</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.005192</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.004779</td>\n",
              "      <td>0.006192</td>\n",
              "      <td>0.005469</td>\n",
              "      <td>0.004898</td>\n",
              "      <td>0.005501</td>\n",
              "      <td>0.08917</td>\n",
              "      <td>0.012399</td>\n",
              "      <td>0.000818</td>\n",
              "      <td>0.019218</td>\n",
              "      <td>0.276655</td>\n",
              "      <td>0.002897</td>\n",
              "      <td>0.001008</td>\n",
              "      <td>0.000389</td>\n",
              "      <td>0.594929</td>\n",
              "      <td>0.002151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2604.51531</td>\n",
              "      <td>5.870331e+06</td>\n",
              "      <td>4.021269e+06</td>\n",
              "      <td>0.014086</td>\n",
              "      <td>0.253530</td>\n",
              "      <td>0.014366</td>\n",
              "      <td>2.149968</td>\n",
              "      <td>0.045239</td>\n",
              "      <td>0.489010</td>\n",
              "      <td>23.942042</td>\n",
              "      <td>0.036603</td>\n",
              "      <td>0.012910</td>\n",
              "      <td>24.399618</td>\n",
              "      <td>0.483935</td>\n",
              "      <td>0.022181</td>\n",
              "      <td>0.099370</td>\n",
              "      <td>0.002817</td>\n",
              "      <td>0.096612</td>\n",
              "      <td>114.508607</td>\n",
              "      <td>72.635840</td>\n",
              "      <td>0.446456</td>\n",
              "      <td>0.447022</td>\n",
              "      <td>0.320436</td>\n",
              "      <td>0.323647</td>\n",
              "      <td>0.439623</td>\n",
              "      <td>0.180314</td>\n",
              "      <td>0.259830</td>\n",
              "      <td>99.206213</td>\n",
              "      <td>110.702741</td>\n",
              "      <td>0.448949</td>\n",
              "      <td>0.188922</td>\n",
              "      <td>0.308997</td>\n",
              "      <td>0.112564</td>\n",
              "      <td>0.444784</td>\n",
              "      <td>0.445669</td>\n",
              "      <td>0.306557</td>\n",
              "      <td>0.319459</td>\n",
              "      <td>0.388164</td>\n",
              "      <td>0.323810</td>\n",
              "      <td>0.024066</td>\n",
              "      <td>...</td>\n",
              "      <td>0.053382</td>\n",
              "      <td>0.070541</td>\n",
              "      <td>0.048417</td>\n",
              "      <td>0.036495</td>\n",
              "      <td>0.182772</td>\n",
              "      <td>0.00630</td>\n",
              "      <td>0.024876</td>\n",
              "      <td>0.045731</td>\n",
              "      <td>0.023397</td>\n",
              "      <td>0.378658</td>\n",
              "      <td>0.007969</td>\n",
              "      <td>0.024876</td>\n",
              "      <td>0.026119</td>\n",
              "      <td>0.022710</td>\n",
              "      <td>0.233843</td>\n",
              "      <td>0.044058</td>\n",
              "      <td>0.049626</td>\n",
              "      <td>0.054912</td>\n",
              "      <td>0.065573</td>\n",
              "      <td>0.061418</td>\n",
              "      <td>0.135388</td>\n",
              "      <td>0.004880</td>\n",
              "      <td>0.007969</td>\n",
              "      <td>0.071866</td>\n",
              "      <td>0.008909</td>\n",
              "      <td>0.068964</td>\n",
              "      <td>0.078444</td>\n",
              "      <td>0.073753</td>\n",
              "      <td>0.069813</td>\n",
              "      <td>0.073966</td>\n",
              "      <td>0.28499</td>\n",
              "      <td>0.110661</td>\n",
              "      <td>0.028583</td>\n",
              "      <td>0.137292</td>\n",
              "      <td>0.447346</td>\n",
              "      <td>0.053750</td>\n",
              "      <td>0.031736</td>\n",
              "      <td>0.019719</td>\n",
              "      <td>0.490908</td>\n",
              "      <td>0.046332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</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>0.000000</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",
              "      <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>...</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>0.00000</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>0.000000</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>0.00000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.00000</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>0.000000</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>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>82.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.050000</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>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</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>0.00000</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>0.000000</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>0.00000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>4.400000e+01</td>\n",
              "      <td>0.000000e+00</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>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>63.000000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.020000</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>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</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>0.00000</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>0.000000</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>0.00000</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>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.760000e+02</td>\n",
              "      <td>5.160000e+02</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>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>143.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070000</td>\n",
              "      <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>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</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>0.00000</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>0.000000</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>0.00000</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>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42908.00000</td>\n",
              "      <td>1.379964e+09</td>\n",
              "      <td>1.309937e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7479.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7468.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>511.000000</td>\n",
              "      <td>511.000000</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>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>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</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>1.00000</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>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>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>1.00000</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>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 118 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           duration     src_bytes  ...        flag_SF        flag_SH\n",
              "count  125973.00000  1.259730e+05  ...  125973.000000  125973.000000\n",
              "mean      287.14465  4.556674e+04  ...       0.594929       0.002151\n",
              "std      2604.51531  5.870331e+06  ...       0.490908       0.046332\n",
              "min         0.00000  0.000000e+00  ...       0.000000       0.000000\n",
              "25%         0.00000  0.000000e+00  ...       0.000000       0.000000\n",
              "50%         0.00000  4.400000e+01  ...       1.000000       0.000000\n",
              "75%         0.00000  2.760000e+02  ...       1.000000       0.000000\n",
              "max     42908.00000  1.379964e+09  ...       1.000000       1.000000\n",
              "\n",
              "[8 rows x 118 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3khTsJe2rRS",
        "outputId": "b15858d3-5c86-4dff-f498-a8589665e064",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "durations = train_x['duration'].values.reshape(-1, 1)\n",
        "standard_scaler = StandardScaler().fit(durations)\n",
        "scaled_durations = standard_scaler.transform(durations)\n",
        "pd.Series(scaled_durations.flatten()).describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.259730e+05\n",
              "mean     3.916911e-16\n",
              "std      1.000004e+00\n",
              "min     -1.102492e-01\n",
              "25%     -1.102492e-01\n",
              "50%     -1.102492e-01\n",
              "75%     -1.102492e-01\n",
              "max      1.636428e+01\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFT8Zqwf2sVa"
      },
      "source": [
        "train_Y_bin = train_Y.apply(lambda x: 0 if x is 'benign' else 1)\n",
        "test_Y_bin = test_Y.apply(lambda x: 0 if x is 'benign' else 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFHQ55Ke21MO",
        "outputId": "d81a49b8-dc05-4c60-a64a-7f1bcaaf91d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "source": [
        "#had tried improving imbalancing, no significant change in result\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix, zero_one_loss, accuracy_score\n",
        "\n",
        "classifier = DecisionTreeClassifier(random_state=17)\n",
        "classifier.fit(train_x, train_Y)\n",
        "\n",
        "pred_y = classifier.predict(test_x)\n",
        "#print(pred_y)\n",
        "\n",
        "results = confusion_matrix(test_Y, pred_y)\n",
        "error = zero_one_loss(test_Y, pred_y)\n",
        "\n",
        "print(results)\n",
        "print(error)\n",
        "print(accuracy_score(pred_y,test_Y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[9365   56  289    1    0]\n",
            " [1365 5998   97    0    0]\n",
            " [ 673  220 1528    0    0]\n",
            " [2587    1   16  277    4]\n",
            " [  48    0    3    5   11]]\n",
            "0.23797906316536555\n",
            "0.7620209368346345\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wBI57b_215v",
        "outputId": "c00f8322-8a11-4d6a-8015-8d34fc68e155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus\n",
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "dot_data = export_graphviz(classifier, feature_names=list(train_x.columns),class_names=['benign', 'probe','dos','u2r','r2l'],rounded=True,filled=True)\n",
        "graph = pydotplus.graph_from_dot_data(dot_data)\n",
        "\n",
        "graph.write_png(\"viz_tree.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssWZeVUZ5dPx"
      },
      "source": [
        "from google.colab import files\n",
        "files.download( \"viz_tree.png\" )  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LABt8su-8H5"
      },
      "source": [
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=1VQd0oyR3aWqH6kJvUOPOi0NW7tMz3IgB'/>\n",
        "  </center>\n",
        "  <center><figcaption><b> Decision Tree</b></figcaption></center>\n",
        "</figure>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlgJORx8CJE0"
      },
      "source": [
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=1btrMRU7V0GrifvNwxWM2HjbfmRBZtUph'/>\n",
        "  </center>\n",
        "  <center><figcaption><b>Sample View of a part</b></figcaption></center>\n",
        "</figure>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NlLrAo3HKl5",
        "outputId": "8b261ac5-3567-43f0-f525-b297e0d69d6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "#variants can be added here, the kernel trick one should work better\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "classifier = LinearSVC()\n",
        "classifier.fit(train_x, train_Y)\n",
        "\n",
        "pred_y = classifier.predict(test_x)\n",
        "\n",
        "results = confusion_matrix(test_Y, pred_y)\n",
        "error = zero_one_loss(test_Y, pred_y)\n",
        "\n",
        "print(results)\n",
        "print(error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[8498  387  307  519    0]\n",
            " [ 995 5287   41 1137    0]\n",
            " [ 677  235 1509    0    0]\n",
            " [2453    1    6  425    0]\n",
            " [  33    0    2   32    0]]\n",
            "0.3027413058907026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S62hRuAhG3f9",
        "outputId": "22f36cdd-f034-4b54-9018-f8425e72db81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(accuracy_score(pred_y,test_Y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6972586941092974\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cj4d3SayHzJ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "6b22da55-02c4-4b90-af90-1ac9e93166b0"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(verbose=True,max_iter=1000)\n",
        "\n",
        "model.fit(train_x, train_Y)\n",
        "pred_y = model.predict(test_x)\n",
        "\n",
        "results = confusion_matrix(test_Y, pred_y)\n",
        "error = zero_one_loss(test_Y, pred_y)\n",
        "\n",
        "print(results)\n",
        "print(error)\n",
        "print(accuracy_score(pred_y,test_Y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[LibSVM][[9487   71  152    1    0]\n",
            " [2249 5055  156    0    0]\n",
            " [ 874  136 1411    0    0]\n",
            " [2685    1  197    2    0]\n",
            " [  66    0    0    0    1]]\n",
            "0.2922285308729595\n",
            "0.7077714691270405\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Chxl7fYW2l-m"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfpQKb4xAa2K"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}