model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# type: ignore\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import imblearn\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial settings\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "np.set_printoptions(precision=3)\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load NSL_KDD train and test dataset\n",
    "\n",
    "datacolumns = [\n",
    "    \"duration\", \"protocol_type\", \"service\", \"flag\", \"src_bytes\",\n",
    "    \"dst_bytes\", \"land\", \"wrong_fragment\", \"urgent\", \"hot\", \"num_failed_logins\",\n",
    "    \"logged_in\", \"num_compromised\", \"root_shell\", \"su_attempted\", \"num_root\",\n",
    "    \"num_file_creations\", \"num_shells\", \"num_access_files\", \"num_outbound_cmds\",\n",
    "    \"is_host_login\", \"is_guest_login\", \"count\", \"srv_count\", \"serror_rate\",\n",
    "    \"srv_serror_rate\", \"rerror_rate\", \"srv_rerror_rate\", \"same_srv_rate\",\n",
    "    \"diff_srv_rate\", \"srv_diff_host_rate\", \"dst_host_count\", \"dst_host_srv_count\",\n",
    "    \"dst_host_same_srv_rate\", \"dst_host_diff_srv_rate\", \"dst_host_same_src_port_rate\",\n",
    "    \"dst_host_srv_diff_host_rate\", \"dst_host_serror_rate\", \"dst_host_srv_serror_rate\",\n",
    "    \"dst_host_rerror_rate\", \"dst_host_srv_rerror_rate\", \"attack\", \"last_flag\"\n",
    "]\n",
    "\n",
    "train_data = pd.read_table(\"./dataset/KDDTrain.txt\", sep=\",\", names=datacolumns)\n",
    "train_data = train_data.iloc[:, :-1]\n",
    "\n",
    "test_data = pd.read_table(\"./dataset/KDDTest.txt\", sep=\",\", names=datacolumns)\n",
    "test_data = test_data.iloc[:, :-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
      "0         0           tcp  ftp_data   SF        491          0     0   \n",
      "1         0           udp     other   SF        146          0     0   \n",
      "2         0           tcp   private   S0          0          0     0   \n",
      "\n",
      "   wrong_fragment  urgent  hot  num_failed_logins  logged_in  num_compromised  \\\n",
      "0               0       0    0                  0          0                0   \n",
      "1               0       0    0                  0          0                0   \n",
      "2               0       0    0                  0          0                0   \n",
      "\n",
      "   root_shell  su_attempted  num_root  num_file_creations  num_shells  \\\n",
      "0           0             0         0                   0           0   \n",
      "1           0             0         0                   0           0   \n",
      "2           0             0         0                   0           0   \n",
      "\n",
      "   num_access_files  num_outbound_cmds  is_host_login  is_guest_login  count  \\\n",
      "0                 0                  0              0               0      2   \n",
      "1                 0                  0              0               0     13   \n",
      "2                 0                  0              0               0    123   \n",
      "\n",
      "   srv_count  serror_rate  srv_serror_rate  rerror_rate  srv_rerror_rate  \\\n",
      "0          2          0.0              0.0          0.0              0.0   \n",
      "1          1          0.0              0.0          0.0              0.0   \n",
      "2          6          1.0              1.0          0.0              0.0   \n",
      "\n",
      "   same_srv_rate  diff_srv_rate  srv_diff_host_rate  dst_host_count  \\\n",
      "0           1.00           0.00                 0.0             150   \n",
      "1           0.08           0.15                 0.0             255   \n",
      "2           0.05           0.07                 0.0             255   \n",
      "\n",
      "   dst_host_srv_count  dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
      "0                  25                    0.17                    0.03   \n",
      "1                   1                    0.00                    0.60   \n",
      "2                  26                    0.10                    0.05   \n",
      "\n",
      "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
      "0                         0.17                          0.0   \n",
      "1                         0.88                          0.0   \n",
      "2                         0.00                          0.0   \n",
      "\n",
      "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
      "0                   0.0                       0.0                  0.05   \n",
      "1                   0.0                       0.0                  0.00   \n",
      "2                   1.0                       1.0                  0.00   \n",
      "\n",
      "   dst_host_srv_rerror_rate   attack  \n",
      "0                       0.0   normal  \n",
      "1                       0.0   normal  \n",
      "2                       0.0  neptune  \n",
      "\n",
      "Train set dimension: 125973 rows, 42 columns\n"
     ]
    }
   ],
   "source": [
    "print(train_data.head(3))\n",
    "print(f'\\nTrain set dimension: {train_data.shape[0]} rows, {train_data.shape[1]} columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print First 3 column from the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
      "0         0           tcp   private  REJ          0          0     0   \n",
      "1         0           tcp   private  REJ          0          0     0   \n",
      "2         2           tcp  ftp_data   SF      12983          0     0   \n",
      "\n",
      "   wrong_fragment  urgent  hot  num_failed_logins  logged_in  num_compromised  \\\n",
      "0               0       0    0                  0          0                0   \n",
      "1               0       0    0                  0          0                0   \n",
      "2               0       0    0                  0          0                0   \n",
      "\n",
      "   root_shell  su_attempted  num_root  num_file_creations  num_shells  \\\n",
      "0           0             0         0                   0           0   \n",
      "1           0             0         0                   0           0   \n",
      "2           0             0         0                   0           0   \n",
      "\n",
      "   num_access_files  num_outbound_cmds  is_host_login  is_guest_login  count  \\\n",
      "0                 0                  0              0               0    229   \n",
      "1                 0                  0              0               0    136   \n",
      "2                 0                  0              0               0      1   \n",
      "\n",
      "   srv_count  serror_rate  srv_serror_rate  rerror_rate  srv_rerror_rate  \\\n",
      "0         10          0.0              0.0          1.0              1.0   \n",
      "1          1          0.0              0.0          1.0              1.0   \n",
      "2          1          0.0              0.0          0.0              0.0   \n",
      "\n",
      "   same_srv_rate  diff_srv_rate  srv_diff_host_rate  dst_host_count  \\\n",
      "0           0.04           0.06                 0.0             255   \n",
      "1           0.01           0.06                 0.0             255   \n",
      "2           1.00           0.00                 0.0             134   \n",
      "\n",
      "   dst_host_srv_count  dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
      "0                  10                    0.04                    0.06   \n",
      "1                   1                    0.00                    0.06   \n",
      "2                  86                    0.61                    0.04   \n",
      "\n",
      "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
      "0                         0.00                         0.00   \n",
      "1                         0.00                         0.00   \n",
      "2                         0.61                         0.02   \n",
      "\n",
      "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
      "0                   0.0                       0.0                   1.0   \n",
      "1                   0.0                       0.0                   1.0   \n",
      "2                   0.0                       0.0                   0.0   \n",
      "\n",
      "   dst_host_srv_rerror_rate   attack  \n",
      "0                       1.0  neptune  \n",
      "1                       1.0  neptune  \n",
      "2                       0.0   normal  \n",
      "\n",
      "Test set dimension: 22544 rows, 42 columns\n"
     ]
    }
   ],
   "source": [
    "print(test_data.head(3))\n",
    "print(f'\\nTest set dimension: {test_data.shape[0]} rows, {test_data.shape[1]} columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Processing for NSL-KDD Dataset\n",
    "\n",
    "NSL-KDD dataset has 42 attributes for each connection record including class label containing attack types. The attack types are categorized into four attack classes as described by Mahbod Tavallaee et al. in [_A Detailed analysis of the KDD CUP 99 Data Set_](http://www.ee.ryerson.ca/~bagheri/papers/cisda.pdf) as:\n",
    "1. **Denial of Service (DoS)**: is an attack in which an adversary directed a deluge of traffic requests to a system in order to make the computing or memory resource too busy or too full to handle legitimate requests and in the process, denies legitimate users access to a machine.\n",
    "2. **Probing Attack (Probe)**: probing network of computers to gather information to be used to compromise its security controls. \n",
    "3. **User to Root Attack (U2R)**: a class of exploit in which the adversary starts out with access to a normal user account on the system (gained either by sniffing passwords, a dictionary attack, or social engineering) and is able to exploit some vulnerability to gain root access to the system. \n",
    "4. **Remote to Local Attack (R2L)**: occurs when an attacker who has the ability to send packets to a machine over a network but who does not have an account on that machine exploits some vulnerability to gain local access as a user of that machine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'ipsweep': 'Probe','satan': 'Probe','nmap': 'Probe','portsweep': 'Probe','saint': 'Probe','mscan': 'Probe',\n",
    "        'teardrop': 'DoS','pod': 'DoS','land': 'DoS','back': 'DoS','neptune': 'DoS','smurf': 'DoS','mailbomb': 'DoS',\n",
    "        'udpstorm': 'DoS','apache2': 'DoS','processtable': 'DoS',\n",
    "        'perl': 'U2R','loadmodule': 'U2R','rootkit': 'U2R','buffer_overflow': 'U2R','xterm': 'U2R','ps': 'U2R',\n",
    "        'sqlattack': 'U2R','httptunnel': 'U2R',\n",
    "        'ftp_write': 'R2L','phf': 'R2L','guess_passwd': 'R2L','warezmaster': 'R2L','warezclient': 'R2L','imap': 'R2L',\n",
    "        'spy': 'R2L','multihop': 'R2L','named': 'R2L','snmpguess': 'R2L','worm': 'R2L','snmpgetattack': 'R2L',\n",
    "        'xsnoop': 'R2L','xlock': 'R2L','sendmail': 'R2L',\n",
    "        'normal': 'Normal'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply attack class mappings to the dataset\n",
    "train_data['attack_class'] = train_data['attack'].apply(lambda v: mapping[v])\n",
    "test_data['attack_class'] = test_data['attack'].apply(lambda v: mapping[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop attack field from both train and test data\n",
    "train_data.drop(['attack'], axis=1, inplace=True)\n",
    "test_data.drop(['attack'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>num_root</th>\n",
       "      <th>num_file_creations</th>\n",
       "      <th>num_shells</th>\n",
       "      <th>num_access_files</th>\n",
       "      <th>num_outbound_cmds</th>\n",
       "      <th>is_host_login</th>\n",
       "      <th>is_guest_login</th>\n",
       "      <th>count</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>srv_rerror_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>DoS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp  ftp_data   SF        491          0     0   \n",
       "1         0           udp     other   SF        146          0     0   \n",
       "2         0           tcp   private   S0          0          0     0   \n",
       "3         0           tcp      http   SF        232       8153     0   \n",
       "4         0           tcp      http   SF        199        420     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  num_failed_logins  logged_in  num_compromised  \\\n",
       "0               0       0    0                  0          0                0   \n",
       "1               0       0    0                  0          0                0   \n",
       "2               0       0    0                  0          0                0   \n",
       "3               0       0    0                  0          1                0   \n",
       "4               0       0    0                  0          1                0   \n",
       "\n",
       "   root_shell  su_attempted  num_root  num_file_creations  num_shells  \\\n",
       "0           0             0         0                   0           0   \n",
       "1           0             0         0                   0           0   \n",
       "2           0             0         0                   0           0   \n",
       "3           0             0         0                   0           0   \n",
       "4           0             0         0                   0           0   \n",
       "\n",
       "   num_access_files  num_outbound_cmds  is_host_login  is_guest_login  count  \\\n",
       "0                 0                  0              0               0      2   \n",
       "1                 0                  0              0               0     13   \n",
       "2                 0                  0              0               0    123   \n",
       "3                 0                  0              0               0      5   \n",
       "4                 0                  0              0               0     30   \n",
       "\n",
       "   srv_count  serror_rate  srv_serror_rate  rerror_rate  srv_rerror_rate  \\\n",
       "0          2          0.0              0.0          0.0              0.0   \n",
       "1          1          0.0              0.0          0.0              0.0   \n",
       "2          6          1.0              1.0          0.0              0.0   \n",
       "3          5          0.2              0.2          0.0              0.0   \n",
       "4         32          0.0              0.0          0.0              0.0   \n",
       "\n",
       "   same_srv_rate  diff_srv_rate  srv_diff_host_rate  dst_host_count  \\\n",
       "0           1.00           0.00                0.00             150   \n",
       "1           0.08           0.15                0.00             255   \n",
       "2           0.05           0.07                0.00             255   \n",
       "3           1.00           0.00                0.00              30   \n",
       "4           1.00           0.00                0.09             255   \n",
       "\n",
       "   dst_host_srv_count  dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                  25                    0.17                    0.03   \n",
       "1                   1                    0.00                    0.60   \n",
       "2                  26                    0.10                    0.05   \n",
       "3                 255                    1.00                    0.00   \n",
       "4                 255                    1.00                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.17                         0.00   \n",
       "1                         0.88                         0.00   \n",
       "2                         0.00                         0.00   \n",
       "3                         0.03                         0.04   \n",
       "4                         0.00                         0.00   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                  0.00                      0.00                  0.05   \n",
       "1                  0.00                      0.00                  0.00   \n",
       "2                  1.00                      1.00                  0.00   \n",
       "3                  0.03                      0.01                  0.00   \n",
       "4                  0.00                      0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_rerror_rate attack_class  \n",
       "0                      0.00       Normal  \n",
       "1                      0.00       Normal  \n",
       "2                      0.00          DoS  \n",
       "3                      0.01       Normal  \n",
       "4                      0.00       Normal  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View top 3 train data \n",
    "train_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>duration</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>num_root</th>\n",
       "      <th>num_file_creations</th>\n",
       "      <th>num_shells</th>\n",
       "      <th>num_access_files</th>\n",
       "      <th>num_outbound_cmds</th>\n",
       "      <th>is_host_login</th>\n",
       "      <th>is_guest_login</th>\n",
       "      <th>count</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>srv_rerror_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "    </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>125973.0</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",
       "    </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>0.001103</td>\n",
       "      <td>0.302192</td>\n",
       "      <td>0.012669</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.004096</td>\n",
       "      <td>0.0</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",
       "    </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>0.045154</td>\n",
       "      <td>24.399618</td>\n",
       "      <td>0.483935</td>\n",
       "      <td>0.022181</td>\n",
       "      <td>0.099370</td>\n",
       "      <td>0.0</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",
       "    </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>0.0</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",
       "    </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>0.0</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",
       "    </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>0.0</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",
       "    </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>0.0</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",
       "    </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>2.000000</td>\n",
       "      <td>7468.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           duration     src_bytes     dst_bytes           land  \\\n",
       "count  125973.00000  1.259730e+05  1.259730e+05  125973.000000   \n",
       "mean      287.14465  4.556674e+04  1.977911e+04       0.000198   \n",
       "std      2604.51531  5.870331e+06  4.021269e+06       0.014086   \n",
       "min         0.00000  0.000000e+00  0.000000e+00       0.000000   \n",
       "25%         0.00000  0.000000e+00  0.000000e+00       0.000000   \n",
       "50%         0.00000  4.400000e+01  0.000000e+00       0.000000   \n",
       "75%         0.00000  2.760000e+02  5.160000e+02       0.000000   \n",
       "max     42908.00000  1.379964e+09  1.309937e+09       1.000000   \n",
       "\n",
       "       wrong_fragment         urgent            hot  num_failed_logins  \\\n",
       "count   125973.000000  125973.000000  125973.000000      125973.000000   \n",
       "mean         0.022687       0.000111       0.204409           0.001222   \n",
       "std          0.253530       0.014366       2.149968           0.045239   \n",
       "min          0.000000       0.000000       0.000000           0.000000   \n",
       "25%          0.000000       0.000000       0.000000           0.000000   \n",
       "50%          0.000000       0.000000       0.000000           0.000000   \n",
       "75%          0.000000       0.000000       0.000000           0.000000   \n",
       "max          3.000000       3.000000      77.000000           5.000000   \n",
       "\n",
       "           logged_in  num_compromised     root_shell   su_attempted  \\\n",
       "count  125973.000000    125973.000000  125973.000000  125973.000000   \n",
       "mean        0.395736         0.279250       0.001342       0.001103   \n",
       "std         0.489010        23.942042       0.036603       0.045154   \n",
       "min         0.000000         0.000000       0.000000       0.000000   \n",
       "25%         0.000000         0.000000       0.000000       0.000000   \n",
       "50%         0.000000         0.000000       0.000000       0.000000   \n",
       "75%         1.000000         0.000000       0.000000       0.000000   \n",
       "max         1.000000      7479.000000       1.000000       2.000000   \n",
       "\n",
       "            num_root  num_file_creations     num_shells  num_access_files  \\\n",
       "count  125973.000000       125973.000000  125973.000000     125973.000000   \n",
       "mean        0.302192            0.012669       0.000413          0.004096   \n",
       "std        24.399618            0.483935       0.022181          0.099370   \n",
       "min         0.000000            0.000000       0.000000          0.000000   \n",
       "25%         0.000000            0.000000       0.000000          0.000000   \n",
       "50%         0.000000            0.000000       0.000000          0.000000   \n",
       "75%         0.000000            0.000000       0.000000          0.000000   \n",
       "max      7468.000000           43.000000       2.000000          9.000000   \n",
       "\n",
       "       num_outbound_cmds  is_host_login  is_guest_login          count  \\\n",
       "count           125973.0  125973.000000   125973.000000  125973.000000   \n",
       "mean                 0.0       0.000008        0.009423      84.107555   \n",
       "std                  0.0       0.002817        0.096612     114.508607   \n",
       "min                  0.0       0.000000        0.000000       0.000000   \n",
       "25%                  0.0       0.000000        0.000000       2.000000   \n",
       "50%                  0.0       0.000000        0.000000      14.000000   \n",
       "75%                  0.0       0.000000        0.000000     143.000000   \n",
       "max                  0.0       1.000000        1.000000     511.000000   \n",
       "\n",
       "           srv_count    serror_rate  srv_serror_rate    rerror_rate  \\\n",
       "count  125973.000000  125973.000000    125973.000000  125973.000000   \n",
       "mean       27.737888       0.284485         0.282485       0.119958   \n",
       "std        72.635840       0.446456         0.447022       0.320436   \n",
       "min         0.000000       0.000000         0.000000       0.000000   \n",
       "25%         2.000000       0.000000         0.000000       0.000000   \n",
       "50%         8.000000       0.000000         0.000000       0.000000   \n",
       "75%        18.000000       1.000000         1.000000       0.000000   \n",
       "max       511.000000       1.000000         1.000000       1.000000   \n",
       "\n",
       "       srv_rerror_rate  same_srv_rate  diff_srv_rate  srv_diff_host_rate  \\\n",
       "count    125973.000000  125973.000000  125973.000000       125973.000000   \n",
       "mean          0.121183       0.660928       0.063053            0.097322   \n",
       "std           0.323647       0.439623       0.180314            0.259830   \n",
       "min           0.000000       0.000000       0.000000            0.000000   \n",
       "25%           0.000000       0.090000       0.000000            0.000000   \n",
       "50%           0.000000       1.000000       0.000000            0.000000   \n",
       "75%           0.000000       1.000000       0.060000            0.000000   \n",
       "max           1.000000       1.000000       1.000000            1.000000   \n",
       "\n",
       "       dst_host_count  dst_host_srv_count  dst_host_same_srv_rate  \\\n",
       "count   125973.000000       125973.000000           125973.000000   \n",
       "mean       182.148945          115.653005                0.521242   \n",
       "std         99.206213          110.702741                0.448949   \n",
       "min          0.000000            0.000000                0.000000   \n",
       "25%         82.000000           10.000000                0.050000   \n",
       "50%        255.000000           63.000000                0.510000   \n",
       "75%        255.000000          255.000000                1.000000   \n",
       "max        255.000000          255.000000                1.000000   \n",
       "\n",
       "       dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "count           125973.000000                125973.000000   \n",
       "mean                 0.082951                     0.148379   \n",
       "std                  0.188922                     0.308997   \n",
       "min                  0.000000                     0.000000   \n",
       "25%                  0.000000                     0.000000   \n",
       "50%                  0.020000                     0.000000   \n",
       "75%                  0.070000                     0.060000   \n",
       "max                  1.000000                     1.000000   \n",
       "\n",
       "       dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "count                125973.000000         125973.000000   \n",
       "mean                      0.032542              0.284452   \n",
       "std                       0.112564              0.444784   \n",
       "min                       0.000000              0.000000   \n",
       "25%                       0.000000              0.000000   \n",
       "50%                       0.000000              0.000000   \n",
       "75%                       0.020000              1.000000   \n",
       "max                       1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "count             125973.000000         125973.000000   \n",
       "mean                   0.278485              0.118832   \n",
       "std                    0.445669              0.306557   \n",
       "min                    0.000000              0.000000   \n",
       "25%                    0.000000              0.000000   \n",
       "50%                    0.000000              0.000000   \n",
       "75%                    1.000000              0.000000   \n",
       "max                    1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_rerror_rate  \n",
       "count             125973.000000  \n",
       "mean                   0.120240  \n",
       "std                    0.319459  \n",
       "min                    0.000000  \n",
       "25%                    0.000000  \n",
       "50%                    0.000000  \n",
       "75%                    0.000000  \n",
       "max                    1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_outbound_cmds\n",
       "0    22544\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['num_outbound_cmds'].value_counts()\n",
    "test_data['num_outbound_cmds'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_outbound_cmds' field has all 0 values. Hence, it will be removed from both train and test dataset since it is a redundant field.\n",
    "train_data.drop(['num_outbound_cmds'], axis=1, inplace=True)\n",
    "test_data.drop(['num_outbound_cmds'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>attack_class</th>\n",
       "      <th>frequency_percent_train</th>\n",
       "      <th>attack_class</th>\n",
       "      <th>frequency_percent_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attack_class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>67343</td>\n",
       "      <td>53.46</td>\n",
       "      <td>9711</td>\n",
       "      <td>43.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DoS</th>\n",
       "      <td>45927</td>\n",
       "      <td>36.46</td>\n",
       "      <td>7458</td>\n",
       "      <td>33.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Probe</th>\n",
       "      <td>11656</td>\n",
       "      <td>9.25</td>\n",
       "      <td>2421</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2L</th>\n",
       "      <td>995</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2754</td>\n",
       "      <td>12.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U2R</th>\n",
       "      <td>52</td>\n",
       "      <td>0.04</td>\n",
       "      <td>200</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              attack_class  frequency_percent_train  attack_class  \\\n",
       "attack_class                                                        \n",
       "Normal               67343                    53.46          9711   \n",
       "DoS                  45927                    36.46          7458   \n",
       "Probe                11656                     9.25          2421   \n",
       "R2L                    995                     0.79          2754   \n",
       "U2R                     52                     0.04           200   \n",
       "\n",
       "              frequency_percent_test  \n",
       "attack_class                          \n",
       "Normal                         43.08  \n",
       "DoS                            33.08  \n",
       "Probe                          10.74  \n",
       "R2L                            12.22  \n",
       "U2R                             0.89  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attack Class Distribution\n",
    "attack_class_freq_train = train_data[['attack_class']].apply(lambda x: x.value_counts())\n",
    "attack_class_freq_test = test_data[['attack_class']].apply(lambda x: x.value_counts())\n",
    "attack_class_freq_train['frequency_percent_train'] = round((100 * attack_class_freq_train / attack_class_freq_train.sum()),2)\n",
    "attack_class_freq_test['frequency_percent_test'] = round((100 * attack_class_freq_test / attack_class_freq_test.sum()),2)\n",
    "\n",
    "attack_class_dist = pd.concat([attack_class_freq_train,attack_class_freq_test], axis=1) \n",
    "attack_class_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attack class bar plot\n",
    "plot = attack_class_dist[['frequency_percent_train', 'frequency_percent_test']].plot(kind=\"bar\");\n",
    "plot.set_title(\"Attack Class Distribution\", fontsize=20);\n",
    "plot.grid(color='lightgray', alpha=0.5);"
   ]
  },
  {
   "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>num_root</th>\n",
       "      <th>num_file_creations</th>\n",
       "      <th>num_shells</th>\n",
       "      <th>num_access_files</th>\n",
       "      <th>is_host_login</th>\n",
       "      <th>is_guest_login</th>\n",
       "      <th>count</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>srv_rerror_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>DoS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp  ftp_data   SF        491          0     0   \n",
       "1         0           udp     other   SF        146          0     0   \n",
       "2         0           tcp   private   S0          0          0     0   \n",
       "3         0           tcp      http   SF        232       8153     0   \n",
       "4         0           tcp      http   SF        199        420     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  num_failed_logins  logged_in  num_compromised  \\\n",
       "0               0       0    0                  0          0                0   \n",
       "1               0       0    0                  0          0                0   \n",
       "2               0       0    0                  0          0                0   \n",
       "3               0       0    0                  0          1                0   \n",
       "4               0       0    0                  0          1                0   \n",
       "\n",
       "   root_shell  su_attempted  num_root  num_file_creations  num_shells  \\\n",
       "0           0             0         0                   0           0   \n",
       "1           0             0         0                   0           0   \n",
       "2           0             0         0                   0           0   \n",
       "3           0             0         0                   0           0   \n",
       "4           0             0         0                   0           0   \n",
       "\n",
       "   num_access_files  is_host_login  is_guest_login  count  srv_count  \\\n",
       "0                 0              0               0      2          2   \n",
       "1                 0              0               0     13          1   \n",
       "2                 0              0               0    123          6   \n",
       "3                 0              0               0      5          5   \n",
       "4                 0              0               0     30         32   \n",
       "\n",
       "   serror_rate  srv_serror_rate  rerror_rate  srv_rerror_rate  same_srv_rate  \\\n",
       "0          0.0              0.0          0.0              0.0           1.00   \n",
       "1          0.0              0.0          0.0              0.0           0.08   \n",
       "2          1.0              1.0          0.0              0.0           0.05   \n",
       "3          0.2              0.2          0.0              0.0           1.00   \n",
       "4          0.0              0.0          0.0              0.0           1.00   \n",
       "\n",
       "   diff_srv_rate  srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
       "0           0.00                0.00             150                  25   \n",
       "1           0.15                0.00             255                   1   \n",
       "2           0.07                0.00             255                  26   \n",
       "3           0.00                0.00              30                 255   \n",
       "4           0.00                0.09             255                 255   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                    0.17                    0.03   \n",
       "1                    0.00                    0.60   \n",
       "2                    0.10                    0.05   \n",
       "3                    1.00                    0.00   \n",
       "4                    1.00                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.17                         0.00   \n",
       "1                         0.88                         0.00   \n",
       "2                         0.00                         0.00   \n",
       "3                         0.03                         0.04   \n",
       "4                         0.00                         0.00   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                  0.00                      0.00                  0.05   \n",
       "1                  0.00                      0.00                  0.00   \n",
       "2                  1.00                      1.00                  0.00   \n",
       "3                  0.03                      0.01                  0.00   \n",
       "4                  0.00                      0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_rerror_rate attack_class  \n",
       "0                      0.00       Normal  \n",
       "1                      0.00       Normal  \n",
       "2                      0.00          DoS  \n",
       "3                      0.01       Normal  \n",
       "4                      0.00       Normal  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling Numerical Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# extract numerical attributes and scale it to have zero mean and unit variance  \n",
    "columns = train_data.select_dtypes(include=['float64','int64']).columns\n",
    "scale_train_data = scaler.fit_transform(train_data.select_dtypes(include=['float64','int64']))\n",
    "scale_test_data = scaler.fit_transform(test_data.select_dtypes(include=['float64','int64']))\n",
    "\n",
    "# turn the result back to a dataframe\n",
    "sc_traindf = pd.DataFrame(scale_train_data, columns = columns)\n",
    "sc_testdf = pd.DataFrame(scale_test_data, columns = columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding of Categorical Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# extract categorical attributes from both training and test sets \n",
    "train_data_category = train_data.select_dtypes(include=['object']).copy()\n",
    "test_data_category = test_data.select_dtypes(include=['object']).copy()\n",
    "\n",
    "# encode the categorical attributes\n",
    "train_category = train_data_category.apply(encoder.fit_transform)\n",
    "test_category = test_data_category.apply(encoder.fit_transform)\n",
    "\n",
    "# separate target column from encoded data \n",
    "encoded_train_data = train_category.drop(['attack_class'], axis=1)\n",
    "encoded_test_data = test_category.drop(['attack_class'], axis=1)\n",
    "\n",
    "category_Ytrain = train_category[['attack_class']].copy()\n",
    "category_Ytest = test_category[['attack_class']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Sampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({np.int64(1): 67343, np.int64(0): 45927, np.int64(2): 11656, np.int64(3): 995, np.int64(4): 52})\n",
      "Resampled dataset shape Counter({np.int64(1): 67343, np.int64(0): 67343, np.int64(3): 67343, np.int64(2): 67343, np.int64(4): 67343})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler \n",
    "from collections import Counter\n",
    "\n",
    "# define columns and extract encoded train set for sampling \n",
    "sc_traindf = train_data.select_dtypes(include=['float64','int64'])\n",
    "refclasscol = pd.concat([sc_traindf, encoded_train_data], axis=1).columns\n",
    "refclass = np.concatenate((scale_train_data, encoded_train_data.values), axis=1)\n",
    "X = refclass\n",
    "\n",
    "# reshape target column to 1D array shape  \n",
    "c, r = category_Ytest.values.shape\n",
    "y_test = category_Ytest.values.reshape(c,)\n",
    "\n",
    "c, r = category_Ytrain.values.shape\n",
    "y = category_Ytrain.values.reshape(c,)\n",
    "\n",
    "# apply the random over-sampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_res, y_res = ros.fit_resample(X, y)\n",
    "print('Original dataset shape {}'.format(Counter(y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier();\n",
    "\n",
    "# fit random forest classifier on the training set\n",
    "rfc.fit(X_res, y_res);\n",
    "\n",
    "\n",
    "# extract important features\n",
    "score = np.round(rfc.feature_importances_,3)\n",
    "importances = pd.DataFrame({'feature':refclasscol,'importance':score})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "\n",
    "\n",
    "# plot importances\n",
    "plt.rcParams['figure.figsize'] = (11, 4)\n",
    "importances.plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "import itertools\n",
    "rfc = RandomForestClassifier()\n",
    "\n",
    "# create the RFE model and select 10 attributes\n",
    "rfe = RFE(rfc, n_features_to_select=10)\n",
    "rfe = rfe.fit(X_res, y_res)\n",
    "\n",
    "# summarize the selection of the attributes\n",
    "feature_map = [(i, v) for i, v in itertools.zip_longest(rfe.get_support(), refclasscol)]\n",
    "selected_features = [v for i, v in feature_map if i==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['src_bytes',\n",
       " 'dst_bytes',\n",
       " 'logged_in',\n",
       " 'count',\n",
       " 'srv_count',\n",
       " 'dst_host_srv_count',\n",
       " 'dst_host_diff_srv_rate',\n",
       " 'dst_host_same_src_port_rate',\n",
       " 'dst_host_serror_rate',\n",
       " 'service']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(336715, 41)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define columns to new dataframe\n",
    "newcol = list(refclasscol)\n",
    "newcol.append('attack_class')\n",
    "\n",
    "# add a dimension to target\n",
    "new_y_res = y_res[:, np.newaxis]\n",
    "\n",
    "# create a dataframe from sampled data\n",
    "res_arr = np.concatenate((X_res, new_y_res), axis=1)\n",
    "res_df = pd.DataFrame(res_arr, columns = newcol) \n",
    "\n",
    "# create test dataframe\n",
    "reftest = pd.concat([sc_testdf, test_category], axis=1)\n",
    "reftest['attack_class'] = reftest['attack_class'].astype(np.float64)\n",
    "reftest['protocol_type'] = reftest['protocol_type'].astype(np.float64)\n",
    "reftest['flag'] = reftest['flag'].astype(np.float64)\n",
    "reftest['service'] = reftest['service'].astype(np.float64)\n",
    "\n",
    "res_df.shape\n",
    "reftest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "classdict = defaultdict(list)\n",
    "\n",
    "# create two-target classes (normal class and an attack class)  \n",
    "attacklist = [('DoS', 0.0), ('Probe', 2.0), ('R2L', 3.0), ('U2R', 4.0)]\n",
    "normalclass = [('Normal', 1.0)]\n",
    "\n",
    "def create_classdict():\n",
    "    '''This function subdivides train and test dataset into two-class attack labels''' \n",
    "    for j, k in normalclass: \n",
    "        for i, v in attacklist: \n",
    "            restrain_set = res_df.loc[(res_df['attack_class'] == k) | (res_df['attack_class'] == v)]\n",
    "            classdict[j +'_' + i].append(restrain_set)\n",
    "            reftest_set = reftest.loc[(reftest['attack_class'] == k) | (reftest['attack_class'] == v)]\n",
    "            classdict[j +'_' + i].append(reftest_set)\n",
    "        \n",
    "create_classdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in classdict.items():\n",
    "    k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrain = classdict['Normal_DoS'][0]\n",
    "pretest = classdict['Normal_DoS'][1]\n",
    "grpclass = 'Normal_DoS'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalize data Processing for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "enc = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "Xresdf = pretrain\n",
    "newtest = pretest\n",
    "\n",
    "Xresdfnew = Xresdf[selected_features]\n",
    "Xresdfnum = Xresdfnew.drop(['service'], axis=1)\n",
    "Xresdfcat = Xresdfnew[['service']].copy()\n",
    "\n",
    "Xtest_features = newtest[selected_features]\n",
    "Xtestdfnum = Xtest_features.drop(['service'], axis=1)\n",
    "Xtestcat = Xtest_features[['service']].copy()\n",
    "\n",
    "# Fit train data\n",
    "enc.fit(Xresdfcat)\n",
    "\n",
    "# Transform train data\n",
    "X_train_1hotenc = enc.transform(Xresdfcat).toarray()\n",
    "\n",
    "# Transform test data\n",
    "X_test_1hotenc = enc.transform(Xtestcat).toarray()\n",
    "\n",
    "X_train = np.concatenate((Xresdfnum.values, X_train_1hotenc), axis=1)\n",
    "X_test = np.concatenate((Xtestdfnum.values, X_test_1hotenc), axis=1)\n",
    "\n",
    "y_train = Xresdf[['attack_class']].copy()\n",
    "c, r = y_train.values.shape\n",
    "Y_train = y_train.values.reshape(c,)\n",
    "\n",
    "y_test = newtest[['attack_class']].copy()\n",
    "c, r = y_test.values.shape\n",
    "Y_test = y_test.values.reshape(c,)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "from sklearn.naive_bayes import BernoulliNB \n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Train KNeighborsClassifier Model\n",
    "KNN_Classifier = KNeighborsClassifier(n_jobs=-1)\n",
    "KNN_Classifier.fit(X_train, Y_train); \n",
    "\n",
    "# Train LogisticRegression Model\n",
    "LGR_Classifier = LogisticRegression(n_jobs=-1, random_state=0)\n",
    "LGR_Classifier.fit(X_train, Y_train);\n",
    "\n",
    "# Train Gaussian Naive Baye Model\n",
    "BNB_Classifier = BernoulliNB()\n",
    "BNB_Classifier.fit(X_train, Y_train)\n",
    "            \n",
    "# Train Decision Tree Model\n",
    "DTC_Classifier = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "DTC_Classifier.fit(X_train, Y_train);\n",
    "\n",
    "# Train RandomForestClassifier Model\n",
    "RF_Classifier = RandomForestClassifier(criterion='entropy', n_jobs=-1, random_state=0)\n",
    "RF_Classifier.fit(X_train, Y_train);  \n",
    "\n",
    "# Train SVM Model\n",
    "SVC_Classifier = SVC(random_state=0)\n",
    "SVC_Classifier.fit(X_train, Y_train)\n",
    "\n",
    "# Train Ensemble Model (This method combines all the individual models above except RandomForest)\n",
    "combined_model = [('Naive Baye Classifier', BNB_Classifier), \n",
    "                 ('Decision Tree Classifier', DTC_Classifier), \n",
    "                 ('KNeighborsClassifier', KNN_Classifier), \n",
    "                 ('LogisticRegression', LGR_Classifier)\n",
    "                ]\n",
    "VotingClassifier =  VotingClassifier(estimators = combined_model,voting = 'soft', n_jobs=-1)\n",
    "VotingClassifier.fit(X_train, Y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================== Normal_DoS SVM Classifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.9885066028376592\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9891376980532498\n",
      "\n",
      "Confusion matrix:\n",
      " [[66345   998]\n",
      " [  465 66878]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     67343\n",
      "         1.0       0.99      0.99      0.99     67343\n",
      "\n",
      "    accuracy                           0.99    134686\n",
      "   macro avg       0.99      0.99      0.99    134686\n",
      "weighted avg       0.99      0.99      0.99    134686\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS Naive Baye Classifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.9737760413571536\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9737686173767133\n",
      "\n",
      "Confusion matrix:\n",
      " [[65346  1997]\n",
      " [ 1536 65807]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.97      0.97     67343\n",
      "         1.0       0.97      0.98      0.97     67343\n",
      "\n",
      "    accuracy                           0.97    134686\n",
      "   macro avg       0.97      0.97      0.97    134686\n",
      "weighted avg       0.97      0.97      0.97    134686\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS Decision Tree Classifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.9997698368976862\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9999480272634127\n",
      "\n",
      "Confusion matrix:\n",
      " [[67343     0]\n",
      " [    7 67336]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     67343\n",
      "         1.0       1.00      1.00      1.00     67343\n",
      "\n",
      "    accuracy                           1.00    134686\n",
      "   macro avg       1.00      1.00      1.00    134686\n",
      "weighted avg       1.00      1.00      1.00    134686\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS RandomForest Classifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.9998143858397031\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9999480272634127\n",
      "\n",
      "Confusion matrix:\n",
      " [[67342     1]\n",
      " [    6 67337]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     67343\n",
      "         1.0       1.00      1.00      1.00     67343\n",
      "\n",
      "    accuracy                           1.00    134686\n",
      "   macro avg       1.00      1.00      1.00    134686\n",
      "weighted avg       1.00      1.00      1.00    134686\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS KNeighborsClassifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.99656981084704\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9977577476500898\n",
      "\n",
      "Confusion matrix:\n",
      " [[67287    56]\n",
      " [  246 67097]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     67343\n",
      "         1.0       1.00      1.00      1.00     67343\n",
      "\n",
      "    accuracy                           1.00    134686\n",
      "   macro avg       1.00      1.00      1.00    134686\n",
      "weighted avg       1.00      1.00      1.00    134686\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS LogisticRegression Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.9808517575575264\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9810522251755937\n",
      "\n",
      "Confusion matrix:\n",
      " [[65560  1783]\n",
      " [  769 66574]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.97      0.98     67343\n",
      "         1.0       0.97      0.99      0.98     67343\n",
      "\n",
      "    accuracy                           0.98    134686\n",
      "   macro avg       0.98      0.98      0.98    134686\n",
      "weighted avg       0.98      0.98      0.98    134686\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS VotingClassifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.996651479313518\n",
      "\n",
      "Model Accuracy:\n",
      " 0.9976538021769152\n",
      "\n",
      "Confusion matrix:\n",
      " [[67096   247]\n",
      " [   69 67274]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     67343\n",
      "         1.0       1.00      1.00      1.00     67343\n",
      "\n",
      "    accuracy                           1.00    134686\n",
      "   macro avg       1.00      1.00      1.00    134686\n",
      "weighted avg       1.00      1.00      1.00    134686\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "models = []\n",
    "models.append(('SVM Classifier', SVC_Classifier))\n",
    "models.append(('Naive Baye Classifier', BNB_Classifier))\n",
    "models.append(('Decision Tree Classifier', DTC_Classifier))\n",
    "models.append(('RandomForest Classifier', RF_Classifier))\n",
    "models.append(('KNeighborsClassifier', KNN_Classifier))\n",
    "models.append(('LogisticRegression', LGR_Classifier))\n",
    "models.append(('VotingClassifier', VotingClassifier))\n",
    "\n",
    "for i, v in models:\n",
    "    scores = cross_val_score(v, X_train, Y_train, cv=10)\n",
    "    accuracy = metrics.accuracy_score(Y_train, v.predict(X_train))\n",
    "    confusion_matrix = metrics.confusion_matrix(Y_train, v.predict(X_train))\n",
    "    classification = metrics.classification_report(Y_train, v.predict(X_train))\n",
    "    print()\n",
    "    print('============================== {} {} Model Evaluation =============================='.format(grpclass, i))\n",
    "    print()\n",
    "    print (\"Cross Validation Mean Score:\" \"\\n\", scores.mean())\n",
    "    print()\n",
    "    print (\"Model Accuracy:\" \"\\n\", accuracy)\n",
    "    print()\n",
    "    print(\"Confusion matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification report:\" \"\\n\", classification) \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================== Normal_DoS SVM Classifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8366241481740346\n",
      "\n",
      "Confusion matrix:\n",
      " [[5272 2186]\n",
      " [ 619 9092]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.89      0.71      0.79      7458\n",
      "         1.0       0.81      0.94      0.87      9711\n",
      "\n",
      "    accuracy                           0.84     17169\n",
      "   macro avg       0.85      0.82      0.83     17169\n",
      "weighted avg       0.84      0.84      0.83     17169\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS Naive Baye Classifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8336536781408352\n",
      "\n",
      "Confusion matrix:\n",
      " [[5487 1971]\n",
      " [ 885 8826]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.86      0.74      0.79      7458\n",
      "         1.0       0.82      0.91      0.86      9711\n",
      "\n",
      "    accuracy                           0.83     17169\n",
      "   macro avg       0.84      0.82      0.83     17169\n",
      "weighted avg       0.84      0.83      0.83     17169\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS Decision Tree Classifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8165880365775525\n",
      "\n",
      "Confusion matrix:\n",
      " [[5591 1867]\n",
      " [1282 8429]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.81      0.75      0.78      7458\n",
      "         1.0       0.82      0.87      0.84      9711\n",
      "\n",
      "    accuracy                           0.82     17169\n",
      "   macro avg       0.82      0.81      0.81     17169\n",
      "weighted avg       0.82      0.82      0.82     17169\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS RandomForest Classifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.829518317898538\n",
      "\n",
      "Confusion matrix:\n",
      " [[5489 1969]\n",
      " [ 958 8753]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.85      0.74      0.79      7458\n",
      "         1.0       0.82      0.90      0.86      9711\n",
      "\n",
      "    accuracy                           0.83     17169\n",
      "   macro avg       0.83      0.82      0.82     17169\n",
      "weighted avg       0.83      0.83      0.83     17169\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS KNeighborsClassifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8666200710583027\n",
      "\n",
      "Confusion matrix:\n",
      " [[5787 1671]\n",
      " [ 619 9092]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.90      0.78      0.83      7458\n",
      "         1.0       0.84      0.94      0.89      9711\n",
      "\n",
      "    accuracy                           0.87     17169\n",
      "   macro avg       0.87      0.86      0.86     17169\n",
      "weighted avg       0.87      0.87      0.86     17169\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS LogisticRegression Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8409924864581513\n",
      "\n",
      "Confusion matrix:\n",
      " [[5874 1584]\n",
      " [1146 8565]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.79      0.81      7458\n",
      "         1.0       0.84      0.88      0.86      9711\n",
      "\n",
      "    accuracy                           0.84     17169\n",
      "   macro avg       0.84      0.83      0.84     17169\n",
      "weighted avg       0.84      0.84      0.84     17169\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS VotingClassifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8424485992195235\n",
      "\n",
      "Confusion matrix:\n",
      " [[5655 1803]\n",
      " [ 902 8809]]\n",
      "\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.86      0.76      0.81      7458\n",
      "         1.0       0.83      0.91      0.87      9711\n",
      "\n",
      "    accuracy                           0.84     17169\n",
      "   macro avg       0.85      0.83      0.84     17169\n",
      "weighted avg       0.84      0.84      0.84     17169\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, v in models:\n",
    "    accuracy = metrics.accuracy_score(Y_test, v.predict(X_test))\n",
    "    confusion_matrix = metrics.confusion_matrix(Y_test, v.predict(X_test))\n",
    "    classification = metrics.classification_report(Y_test, v.predict(X_test))\n",
    "    print()\n",
    "    print('============================== {} {} Model Test Results =============================='.format(grpclass, i))\n",
    "    print()\n",
    "    print (\"Model Accuracy:\" \"\\n\", accuracy)\n",
    "    print()\n",
    "    print(\"Confusion matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification report:\" \"\\n\", classification) \n",
    "    print()        \n"
   ]
  }
 ],
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