model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>dur</th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>...</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>ct_srv_dst</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "      <th>attack_cat</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>496</td>\n",
       "      <td>0</td>\n",
       "      <td>90909.0902</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1762</td>\n",
       "      <td>0</td>\n",
       "      <td>125000.0003</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1068</td>\n",
       "      <td>0</td>\n",
       "      <td>200000.0051</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>166666.6608</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2126</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0025</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       dur proto service state  spkts  dpkts  sbytes  dbytes  \\\n",
       "0   1  0.000011   udp       -   INT      2      0     496       0   \n",
       "1   2  0.000008   udp       -   INT      2      0    1762       0   \n",
       "2   3  0.000005   udp       -   INT      2      0    1068       0   \n",
       "3   4  0.000006   udp       -   INT      2      0     900       0   \n",
       "4   5  0.000010   udp       -   INT      2      0    2126       0   \n",
       "\n",
       "          rate  ...  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login  \\\n",
       "0   90909.0902  ...                 1               2             0   \n",
       "1  125000.0003  ...                 1               2             0   \n",
       "2  200000.0051  ...                 1               3             0   \n",
       "3  166666.6608  ...                 1               3             0   \n",
       "4  100000.0025  ...                 1               3             0   \n",
       "\n",
       "   ct_ftp_cmd  ct_flw_http_mthd  ct_src_ltm  ct_srv_dst  is_sm_ips_ports  \\\n",
       "0           0                 0           1           2                0   \n",
       "1           0                 0           1           2                0   \n",
       "2           0                 0           1           3                0   \n",
       "3           0                 0           2           3                0   \n",
       "4           0                 0           2           3                0   \n",
       "\n",
       "   attack_cat  label  \n",
       "0      Normal      0  \n",
       "1      Normal      0  \n",
       "2      Normal      0  \n",
       "3      Normal      0  \n",
       "4      Normal      0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read dataset\n",
    "\n",
    "df=pd.read_csv('dataset/UNSW_NB15_training-set.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dur</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>...</th>\n",
       "      <th>ct_src_dport_ltm</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>ct_srv_dst</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>1.006756</td>\n",
       "      <td>18.666472</td>\n",
       "      <td>17.545936</td>\n",
       "      <td>7.993908e+03</td>\n",
       "      <td>1.323379e+04</td>\n",
       "      <td>8.241089e+04</td>\n",
       "      <td>180.967667</td>\n",
       "      <td>95.713003</td>\n",
       "      <td>6.454902e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>4.928898</td>\n",
       "      <td>3.663011</td>\n",
       "      <td>7.456360</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.129743</td>\n",
       "      <td>6.468360</td>\n",
       "      <td>9.164262</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.550600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23767.345519</td>\n",
       "      <td>4.710444</td>\n",
       "      <td>133.916353</td>\n",
       "      <td>115.574086</td>\n",
       "      <td>1.716423e+05</td>\n",
       "      <td>1.514715e+05</td>\n",
       "      <td>1.486204e+05</td>\n",
       "      <td>101.513358</td>\n",
       "      <td>116.667722</td>\n",
       "      <td>1.798618e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>8.389545</td>\n",
       "      <td>5.915386</td>\n",
       "      <td>11.415191</td>\n",
       "      <td>0.091171</td>\n",
       "      <td>0.092485</td>\n",
       "      <td>0.638683</td>\n",
       "      <td>8.543927</td>\n",
       "      <td>11.121413</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>0.497436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000e+01</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>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20583.750000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.860611e+01</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.120247e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>0.014138</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.340000e+02</td>\n",
       "      <td>1.780000e+02</td>\n",
       "      <td>2.650177e+03</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.770032e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61749.250000</td>\n",
       "      <td>0.719360</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.280000e+03</td>\n",
       "      <td>9.560000e+02</td>\n",
       "      <td>1.111111e+05</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>6.514286e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>59.999989</td>\n",
       "      <td>10646.000000</td>\n",
       "      <td>11018.000000</td>\n",
       "      <td>1.435577e+07</td>\n",
       "      <td>1.465753e+07</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>5.268000e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id           dur         spkts         dpkts        sbytes  \\\n",
       "count  82332.000000  82332.000000  82332.000000  82332.000000  8.233200e+04   \n",
       "mean   41166.500000      1.006756     18.666472     17.545936  7.993908e+03   \n",
       "std    23767.345519      4.710444    133.916353    115.574086  1.716423e+05   \n",
       "min        1.000000      0.000000      1.000000      0.000000  2.400000e+01   \n",
       "25%    20583.750000      0.000008      2.000000      0.000000  1.140000e+02   \n",
       "50%    41166.500000      0.014138      6.000000      2.000000  5.340000e+02   \n",
       "75%    61749.250000      0.719360     12.000000     10.000000  1.280000e+03   \n",
       "max    82332.000000     59.999989  10646.000000  11018.000000  1.435577e+07   \n",
       "\n",
       "             dbytes          rate          sttl          dttl         sload  \\\n",
       "count  8.233200e+04  8.233200e+04  82332.000000  82332.000000  8.233200e+04   \n",
       "mean   1.323379e+04  8.241089e+04    180.967667     95.713003  6.454902e+07   \n",
       "std    1.514715e+05  1.486204e+05    101.513358    116.667722  1.798618e+08   \n",
       "min    0.000000e+00  0.000000e+00      0.000000      0.000000  0.000000e+00   \n",
       "25%    0.000000e+00  2.860611e+01     62.000000      0.000000  1.120247e+04   \n",
       "50%    1.780000e+02  2.650177e+03    254.000000     29.000000  5.770032e+05   \n",
       "75%    9.560000e+02  1.111111e+05    254.000000    252.000000  6.514286e+07   \n",
       "max    1.465753e+07  1.000000e+06    255.000000    253.000000  5.268000e+09   \n",
       "\n",
       "       ...  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login  \\\n",
       "count  ...      82332.000000      82332.000000    82332.000000  82332.000000   \n",
       "mean   ...          4.928898          3.663011        7.456360      0.008284   \n",
       "std    ...          8.389545          5.915386       11.415191      0.091171   \n",
       "min    ...          1.000000          1.000000        1.000000      0.000000   \n",
       "25%    ...          1.000000          1.000000        1.000000      0.000000   \n",
       "50%    ...          1.000000          1.000000        3.000000      0.000000   \n",
       "75%    ...          4.000000          3.000000        6.000000      0.000000   \n",
       "max    ...         59.000000         38.000000       63.000000      2.000000   \n",
       "\n",
       "         ct_ftp_cmd  ct_flw_http_mthd    ct_src_ltm    ct_srv_dst  \\\n",
       "count  82332.000000      82332.000000  82332.000000  82332.000000   \n",
       "mean       0.008381          0.129743      6.468360      9.164262   \n",
       "std        0.092485          0.638683      8.543927     11.121413   \n",
       "min        0.000000          0.000000      1.000000      1.000000   \n",
       "25%        0.000000          0.000000      1.000000      2.000000   \n",
       "50%        0.000000          0.000000      3.000000      5.000000   \n",
       "75%        0.000000          0.000000      7.000000     11.000000   \n",
       "max        2.000000         16.000000     60.000000     62.000000   \n",
       "\n",
       "       is_sm_ips_ports         label  \n",
       "count     82332.000000  82332.000000  \n",
       "mean          0.011126      0.550600  \n",
       "std           0.104891      0.497436  \n",
       "min           0.000000      0.000000  \n",
       "25%           0.000000      0.000000  \n",
       "50%           0.000000      1.000000  \n",
       "75%           0.000000      1.000000  \n",
       "max           1.000000      1.000000  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82332, 45)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()\n",
    "#false but observe service variable which has more - values which indicates the null values\n",
    "#so we have to replace it with other labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-           47153\n",
       "dns         21367\n",
       "http         8287\n",
       "smtp         1851\n",
       "ftp          1552\n",
       "ftp-data     1396\n",
       "pop3          423\n",
       "ssh           204\n",
       "ssl            30\n",
       "snmp           29\n",
       "dhcp           26\n",
       "radius          9\n",
       "irc             5\n",
       "Name: service, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets plot the types of value in service attribute\n",
    "df['service'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown     47153\n",
       "dns         21367\n",
       "http         8287\n",
       "smtp         1851\n",
       "ftp          1552\n",
       "ftp-data     1396\n",
       "pop3          423\n",
       "ssh           204\n",
       "ssl            30\n",
       "snmp           29\n",
       "dhcp           26\n",
       "radius          9\n",
       "irc             5\n",
       "Name: service, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets changed the - with 'unknown' value \n",
    "df['service']=df['service'].replace('-','unknown')\n",
    "df['service'].value_counts()\n",
    "#good to go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1296 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram to see the data distribution\n",
    "df.hist(figsize=(15,18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2303c69ea20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(df.corr(),linewidths=.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2303b5916a0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count the label\n",
    "#0 indicates normal\n",
    "#1 indicates attacked\n",
    "sns.countplot(df['label'])\n",
    "\n",
    "#our dataset contains the equal data length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets check categorical data plot\n",
    "#service,attack_cat,proto etc\n",
    "ax=plt.figure(figsize=(12,3))\n",
    "sns.countplot(df['attack_cat'])\n",
    "plt.show()\n",
    "ax=plt.figure(figsize=(12,6))\n",
    "plt.subplot(2,2,1)\n",
    "sns.countplot(df['service'],palette='Set2')\n",
    "plt.subplot(2,2,2)\n",
    "sns.countplot(df['state'],palette='Set1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(columns=['label','id'])\n",
    "y=df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>attack_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>117</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>117</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   proto  service  state  attack_cat\n",
       "0    117       12      4           6\n",
       "1    117       12      4           6\n",
       "2    117       12      4           6\n",
       "3    117       12      4           6\n",
       "4    117       12      4           6"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "#train dataset\n",
    "lbl=LabelEncoder()\n",
    "\n",
    "#fetch all coumns with object data type\n",
    "feature_mask=X.dtypes==object\n",
    "cat_cols=X.columns[feature_mask].tolist()\n",
    "\n",
    "#convert each label to a unique integer\n",
    "X[cat_cols]=X[cat_cols].apply(lambda col:lbl.fit_transform(col))\n",
    "X[cat_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((57632, 43), (57632,), (24700, 43), (24700,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#split the data into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=5)\n",
    "X_train.shape,y_train.shape,X_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mr.Khan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurcy 0.7579352226720648\n",
      "ROC AUC  0.7678668518190391\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "       Actual  Predicted\n",
       "8737        1          1\n",
       "72286       0          1\n",
       "78850       0          1\n",
       "42816       0          0\n",
       "67500       0          0\n",
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       "51958       1          1\n",
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      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score\n",
    "\n",
    "clf=LogisticRegression(C=0.5)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred=clf.predict(X_test)\n",
    "print(\"Accurcy \" + str(accuracy_score(y_test,y_pred)))\n",
    "print(\"ROC AUC  \" + str(roc_auc_score(y_test,y_pred)))\n",
    "pd.DataFrame(data={'Actual':y_test,'Predicted':y_pred}).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurcy 0.999919028340081\n",
      "ROC AUC  0.9999260847069258\n"
     ]
    },
    {
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       "</div>"
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      "text/plain": [
       "       Actual  Predicted\n",
       "8737        1          1\n",
       "72286       0          0\n",
       "78850       0          0\n",
       "42816       0          0\n",
       "67500       0          0\n",
       "19671       1          1\n",
       "61863       1          1\n",
       "51958       1          1\n",
       "69597       0          0\n",
       "73793       0          0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=20)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred=clf.predict(X_test)\n",
    "print(\"Accurcy \" + str(accuracy_score(y_test,y_pred)))\n",
    "print(\"ROC AUC  \" + str(roc_auc_score(y_test,y_pred)))\n",
    "pd.DataFrame(data={'Actual':y_test,'Predicted':y_pred}).head(10)\n",
    "\n",
    "#wow...both accuracy and roc auc curve is 99%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11171,     0],\n",
       "       [    2, 13527]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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