{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "driving-spider",
   "metadata": {},
   "source": [
    "#### <center>KDD CUP 1999</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-objective",
   "metadata": {},
   "source": [
    "#### Load Training and Test Datta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "underlying-performer",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "training_data = pd.read_csv('train.txt', names = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\"dst_bytes\",\"land\",\n",
    "\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\"logged_in\",\n",
    "\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\"num_file_creations\",\n",
    "\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\"is_host_login\",\n",
    "\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\", \"srv_serror_rate\",\n",
    "\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\", \"diff_srv_rate\", \"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\"dst_host_same_srv_rate\",\n",
    "\"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",
    "test_data = pd.read_csv('test.txt', names = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\"dst_bytes\",\"land\",\n",
    "\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\"logged_in\",\n",
    "\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\"num_file_creations\",\n",
    "\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\"is_host_login\",\n",
    "\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\", \"srv_serror_rate\",\n",
    "\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\", \"diff_srv_rate\", \"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\"dst_host_same_srv_rate\",\n",
    "\"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\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "announced-munich",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_label(label):\n",
    "    if label == 'normal':\n",
    "        return 1;\n",
    "    else:\n",
    "        return -1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "junior-integrity",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = training_data['attack'].apply(get_label)\n",
    "test_y = test_data['attack'].apply(get_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "referenced-cleveland",
   "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>...</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack</th>\n",
       "      <th>last_flag</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>...</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>20</td>\n",
       "    </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>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>15</td>\n",
       "    </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>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neptune</td>\n",
       "      <td>19</td>\n",
       "    </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>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>normal</td>\n",
       "      <td>21</td>\n",
       "    </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>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\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  ...  dst_host_same_srv_rate  \\\n",
       "0               0       0    0  ...                    0.17   \n",
       "1               0       0    0  ...                    0.00   \n",
       "2               0       0    0  ...                    0.10   \n",
       "3               0       0    0  ...                    1.00   \n",
       "4               0       0    0  ...                    1.00   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                    0.03                         0.17   \n",
       "1                    0.60                         0.88   \n",
       "2                    0.05                         0.00   \n",
       "3                    0.00                         0.03   \n",
       "4                    0.00                         0.00   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                         0.00                  0.00   \n",
       "1                         0.00                  0.00   \n",
       "2                         0.00                  1.00   \n",
       "3                         0.04                  0.03   \n",
       "4                         0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                      0.00                  0.05                      0.00   \n",
       "1                      0.00                  0.00                      0.00   \n",
       "2                      1.00                  0.00                      0.00   \n",
       "3                      0.01                  0.00                      0.01   \n",
       "4                      0.00                  0.00                      0.00   \n",
       "\n",
       "    attack  last_flag  \n",
       "0   normal         20  \n",
       "1   normal         15  \n",
       "2  neptune         19  \n",
       "3   normal         21  \n",
       "4   normal         21  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "laden-inspiration",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fuzzy-complaint",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125973 entries, 0 to 125972\n",
      "Data columns (total 43 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   duration                     125973 non-null  int64  \n",
      " 1   protocol_type                125973 non-null  object \n",
      " 2   service                      125973 non-null  object \n",
      " 3   flag                         125973 non-null  object \n",
      " 4   src_bytes                    125973 non-null  int64  \n",
      " 5   dst_bytes                    125973 non-null  int64  \n",
      " 6   land                         125973 non-null  int64  \n",
      " 7   wrong_fragment               125973 non-null  int64  \n",
      " 8   urgent                       125973 non-null  int64  \n",
      " 9   hot                          125973 non-null  int64  \n",
      " 10  num_failed_logins            125973 non-null  int64  \n",
      " 11  logged_in                    125973 non-null  int64  \n",
      " 12  num_compromised              125973 non-null  int64  \n",
      " 13  root_shell                   125973 non-null  int64  \n",
      " 14  su_attempted                 125973 non-null  int64  \n",
      " 15  num_root                     125973 non-null  int64  \n",
      " 16  num_file_creations           125973 non-null  int64  \n",
      " 17  num_shells                   125973 non-null  int64  \n",
      " 18  num_access_files             125973 non-null  int64  \n",
      " 19  num_outbound_cmds            125973 non-null  int64  \n",
      " 20  is_host_login                125973 non-null  int64  \n",
      " 21  is_guest_login               125973 non-null  int64  \n",
      " 22  count                        125973 non-null  int64  \n",
      " 23  srv_count                    125973 non-null  int64  \n",
      " 24  serror_rate                  125973 non-null  float64\n",
      " 25  srv_serror_rate              125973 non-null  float64\n",
      " 26  rerror_rate                  125973 non-null  float64\n",
      " 27  srv_rerror_rate              125973 non-null  float64\n",
      " 28  same_srv_rate                125973 non-null  float64\n",
      " 29  diff_srv_rate                125973 non-null  float64\n",
      " 30  srv_diff_host_rate           125973 non-null  float64\n",
      " 31  dst_host_count               125973 non-null  int64  \n",
      " 32  dst_host_srv_count           125973 non-null  int64  \n",
      " 33  dst_host_same_srv_rate       125973 non-null  float64\n",
      " 34  dst_host_diff_srv_rate       125973 non-null  float64\n",
      " 35  dst_host_same_src_port_rate  125973 non-null  float64\n",
      " 36  dst_host_srv_diff_host_rate  125973 non-null  float64\n",
      " 37  dst_host_serror_rate         125973 non-null  float64\n",
      " 38  dst_host_srv_serror_rate     125973 non-null  float64\n",
      " 39  dst_host_rerror_rate         125973 non-null  float64\n",
      " 40  dst_host_srv_rerror_rate     125973 non-null  float64\n",
      " 41  attack                       125973 non-null  object \n",
      " 42  last_flag                    125973 non-null  int64  \n",
      "dtypes: float64(15), int64(24), object(4)\n",
      "memory usage: 41.3+ MB\n"
     ]
    }
   ],
   "source": [
    "training_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "independent-booking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>...</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>last_flag</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>...</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>...</td>\n",
       "      <td>115.653005</td>\n",
       "      <td>0.521242</td>\n",
       "      <td>0.082951</td>\n",
       "      <td>0.148379</td>\n",
       "      <td>0.032542</td>\n",
       "      <td>0.284452</td>\n",
       "      <td>0.278485</td>\n",
       "      <td>0.118832</td>\n",
       "      <td>0.120240</td>\n",
       "      <td>19.504060</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>...</td>\n",
       "      <td>110.702741</td>\n",
       "      <td>0.448949</td>\n",
       "      <td>0.188922</td>\n",
       "      <td>0.308997</td>\n",
       "      <td>0.112564</td>\n",
       "      <td>0.444784</td>\n",
       "      <td>0.445669</td>\n",
       "      <td>0.306557</td>\n",
       "      <td>0.319459</td>\n",
       "      <td>2.291503</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>...</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>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</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>...</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</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>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</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>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\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  ...  dst_host_srv_count  \\\n",
       "count  125973.000000    125973.000000  ...       125973.000000   \n",
       "mean        0.395736         0.279250  ...          115.653005   \n",
       "std         0.489010        23.942042  ...          110.702741   \n",
       "min         0.000000         0.000000  ...            0.000000   \n",
       "25%         0.000000         0.000000  ...           10.000000   \n",
       "50%         0.000000         0.000000  ...           63.000000   \n",
       "75%         1.000000         0.000000  ...          255.000000   \n",
       "max         1.000000      7479.000000  ...          255.000000   \n",
       "\n",
       "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "count           125973.000000           125973.000000   \n",
       "mean                 0.521242                0.082951   \n",
       "std                  0.448949                0.188922   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  0.050000                0.000000   \n",
       "50%                  0.510000                0.020000   \n",
       "75%                  1.000000                0.070000   \n",
       "max                  1.000000                1.000000   \n",
       "\n",
       "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "count                125973.000000                125973.000000   \n",
       "mean                      0.148379                     0.032542   \n",
       "std                       0.308997                     0.112564   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       0.000000                     0.000000   \n",
       "50%                       0.000000                     0.000000   \n",
       "75%                       0.060000                     0.020000   \n",
       "max                       1.000000                     1.000000   \n",
       "\n",
       "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "count         125973.000000             125973.000000         125973.000000   \n",
       "mean               0.284452                  0.278485              0.118832   \n",
       "std                0.444784                  0.445669              0.306557   \n",
       "min                0.000000                  0.000000              0.000000   \n",
       "25%                0.000000                  0.000000              0.000000   \n",
       "50%                0.000000                  0.000000              0.000000   \n",
       "75%                1.000000                  1.000000              0.000000   \n",
       "max                1.000000                  1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_rerror_rate      last_flag  \n",
       "count             125973.000000  125973.000000  \n",
       "mean                   0.120240      19.504060  \n",
       "std                    0.319459       2.291503  \n",
       "min                    0.000000       0.000000  \n",
       "25%                    0.000000      18.000000  \n",
       "50%                    0.000000      20.000000  \n",
       "75%                    0.000000      21.000000  \n",
       "max                    1.000000      21.000000  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "developing-greeting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'duration'}>,\n",
       "        <AxesSubplot:title={'center':'src_bytes'}>,\n",
       "        <AxesSubplot:title={'center':'dst_bytes'}>,\n",
       "        <AxesSubplot:title={'center':'land'}>,\n",
       "        <AxesSubplot:title={'center':'wrong_fragment'}>,\n",
       "        <AxesSubplot:title={'center':'urgent'}>],\n",
       "       [<AxesSubplot:title={'center':'hot'}>,\n",
       "        <AxesSubplot:title={'center':'num_failed_logins'}>,\n",
       "        <AxesSubplot:title={'center':'logged_in'}>,\n",
       "        <AxesSubplot:title={'center':'num_compromised'}>,\n",
       "        <AxesSubplot:title={'center':'root_shell'}>,\n",
       "        <AxesSubplot:title={'center':'su_attempted'}>],\n",
       "       [<AxesSubplot:title={'center':'num_root'}>,\n",
       "        <AxesSubplot:title={'center':'num_file_creations'}>,\n",
       "        <AxesSubplot:title={'center':'num_shells'}>,\n",
       "        <AxesSubplot:title={'center':'num_access_files'}>,\n",
       "        <AxesSubplot:title={'center':'num_outbound_cmds'}>,\n",
       "        <AxesSubplot:title={'center':'is_host_login'}>],\n",
       "       [<AxesSubplot:title={'center':'is_guest_login'}>,\n",
       "        <AxesSubplot:title={'center':'count'}>,\n",
       "        <AxesSubplot:title={'center':'srv_count'}>,\n",
       "        <AxesSubplot:title={'center':'serror_rate'}>,\n",
       "        <AxesSubplot:title={'center':'srv_serror_rate'}>,\n",
       "        <AxesSubplot:title={'center':'rerror_rate'}>],\n",
       "       [<AxesSubplot:title={'center':'srv_rerror_rate'}>,\n",
       "        <AxesSubplot:title={'center':'same_srv_rate'}>,\n",
       "        <AxesSubplot:title={'center':'diff_srv_rate'}>,\n",
       "        <AxesSubplot:title={'center':'srv_diff_host_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_count'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_srv_count'}>],\n",
       "       [<AxesSubplot:title={'center':'dst_host_same_srv_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_diff_srv_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_same_src_port_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_srv_diff_host_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_serror_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_srv_serror_rate'}>],\n",
       "       [<AxesSubplot:title={'center':'dst_host_rerror_rate'}>,\n",
       "        <AxesSubplot:title={'center':'dst_host_srv_rerror_rate'}>,\n",
       "        <AxesSubplot:title={'center':'last_flag'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_data.hist(bins = 50, figsize = (20, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "advisory-boost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration', 'protocol_type', 'service', 'flag', 'src_bytes',\n",
       "       'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
       "       'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell',\n",
       "       'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
       "       'num_access_files', 'num_outbound_cmds', 'is_host_login',\n",
       "       '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',\n",
       "       'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
       "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
       "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
       "       'dst_host_srv_serror_rate', 'dst_host_rerror_rate',\n",
       "       'dst_host_srv_rerror_rate', 'attack', 'last_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "informal-advertiser",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal             67343\n",
       "neptune            41214\n",
       "satan               3633\n",
       "ipsweep             3599\n",
       "portsweep           2931\n",
       "smurf               2646\n",
       "nmap                1493\n",
       "back                 956\n",
       "teardrop             892\n",
       "warezclient          890\n",
       "pod                  201\n",
       "guess_passwd          53\n",
       "buffer_overflow       30\n",
       "warezmaster           20\n",
       "land                  18\n",
       "imap                  11\n",
       "rootkit               10\n",
       "loadmodule             9\n",
       "ftp_write              8\n",
       "multihop               7\n",
       "phf                    4\n",
       "perl                   3\n",
       "spy                    2\n",
       "Name: attack, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data['attack'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outer-indicator",
   "metadata": {},
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "intellectual-picnic",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "minimal-deputy",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class Get_top_categories(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Create a class to keep the top categories, the rest categories are labeled as 'other'\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, top_num = 10): # no *args or **kargs\n",
    "        \"\"\"Create a class\n",
    "        \n",
    "        Arg:\n",
    "            top_num (int), the number of top categories kept, default number is 10\n",
    "        \"\"\"\n",
    "        self.top_num = top_num\n",
    "    \n",
    "    def fit(self, X, y = None):\n",
    "        \"\"\"Fit the class\n",
    "        \n",
    "        Arg:\n",
    "            X (Pandas.Series), a column of a Pandas.DataFrame\n",
    "            y (None), not used\n",
    "        \"\"\"\n",
    "        temp = X.value_counts()\n",
    "        self.columns = list(temp[:self.top_num].index)\n",
    "        return self\n",
    "    \n",
    "    def containe(self, s):\n",
    "        \"\"\"Process record\n",
    "        \n",
    "        Arg:\n",
    "            s (str), a recod in the categorical column\n",
    "            \n",
    "        Return:\n",
    "            str, return the same string is a recod in the top category list; otherwise, return 'other'\n",
    "        \"\"\"\n",
    "        if s in self.columns:\n",
    "            return s\n",
    "        else:\n",
    "            return 'other_category'\n",
    "        \n",
    "    def transform(self, X):\n",
    "        \"\"\"Convert a specific categorical column\n",
    "        \n",
    "        Arg:\n",
    "            X (Pandas.Series), a column of a Pandas.DataFrame\n",
    "            \n",
    "        Return:\n",
    "            Pandas.Series, processed column\n",
    "        \"\"\"\n",
    "        temp = X.apply(self.containe)\n",
    "        return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "saved-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DoNothing(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Do not change anything\"\"\"\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        temp = X.copy()\n",
    "        return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "toxic-certification",
   "metadata": {},
   "outputs": [],
   "source": [
    "# process numerical features\n",
    "num_pipeline = Pipeline([\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])\n",
    "\n",
    "num_pipeline_gaussian = Pipeline([\n",
    "    ('quantile', QuantileTransformer(output_distribution='normal', random_state=0)),\n",
    "    #('std_scaler', StandardScaler()),  \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "unable-image",
   "metadata": {},
   "outputs": [],
   "source": [
    "# process categorical features with bag of words\n",
    "cat_pipeline = Pipeline([\n",
    "    ('bag_of_words', CountVectorizer()),\n",
    "])\n",
    "\n",
    "cat_pipeline_five = Pipeline([\n",
    "    ('more_than_five', Get_top_categories(top_num=5)),\n",
    "    ('bag_of_words', CountVectorizer()),    \n",
    "])\n",
    "\n",
    "cat_pipeline_ten = Pipeline([\n",
    "    ('more_than_ten', Get_top_categories()),\n",
    "    ('bag_of_words', CountVectorizer()),    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "charged-applicant",
   "metadata": {},
   "outputs": [],
   "source": [
    "# do not change features\n",
    "do_nothing_pipeline = Pipeline([\n",
    "    ('do_nothing', DoNothing())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "straight-intake",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "preprocess_pipeline = ColumnTransformer([\n",
    "        (\"num_pipeline_guassion\", num_pipeline_gaussian, ['duration', 'src_bytes', 'dst_bytes', 'hot', 'num_compromised', 'num_root', 'num_file_creations', 'num_access_files', 'count', 'srv_count', 'dst_host_count', 'dst_host_srv_count']), # 3, pass a DataFrame to num_pipeline\n",
    "        (\"cat_pipeline_protocol_type\", cat_pipeline, 'protocol_type'), # 3, pass a Series to cat_pipeline\n",
    "        (\"cat_pipeline_service\", cat_pipeline_ten, 'service'), # 11, pass a Series to cat_pipeline_ten  \n",
    "        (\"cat_pipeline_flag\", cat_pipeline_five, 'flag'), # 6, pass a Series to cat_pipeline_ten\n",
    "        (\"do_nothing\", do_nothing_pipeline, ['land', 'wrong_fragment', 'urgent', 'num_failed_logins', 'logged_in', 'root_shell', 'su_attempted', 'num_shells', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate']) # 1, pass a DataFrame to num_pipeline\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fourth-texture",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_pipeline_gaussian, 12\n",
    "# cat_pipeline_protocol_type, 3\n",
    "# cat_pipeline_service, 11\n",
    "# cat_pipeline_flag, 6\n",
    "# do_nothing, 26\n",
    "train_x = preprocess_pipeline.fit_transform(training_data)\n",
    "column1 = ['duration', 'src_bytes', 'dst_bytes', 'hot', 'num_compromised', 'num_root', 'num_file_creations', 'num_access_files', 'count', 'srv_count', 'dst_host_count', 'dst_host_srv_count']\n",
    "column2 = ['p0', 'p1', 'p2']\n",
    "column3 = ['s0', 's1', 's2', 's3', 's4', 's5', 's6', 's7', 's8', 's9', 's10']\n",
    "column4 = ['f0', 'f1', 'f2', 'f3', 'f4', 'f5']\n",
    "column5 = ['land', 'wrong_fragment', 'urgent', 'num_failed_logins', 'logged_in', 'root_shell', 'su_attempted', 'num_shells', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate']\n",
    "columns = column1+column2+column3+column4+column5\n",
    "train_x = pd.DataFrame(train_x, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef46b640",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x = preprocess_pipeline.transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa1a184",
   "metadata": {},
   "source": [
    "#### Local Outlier Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "732195c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "lof = LocalOutlierFactor(novelty=True, n_neighbors = 200, algorithm = 'auto', metric = 'manhattan')\n",
    "lof.fit(train_x[train_y==1])\n",
    "predict = lof.predict(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9d7a768e",
   "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>Malicious_true</th>\n",
       "      <th>Normal_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Malicious_true</th>\n",
       "      <td>56147</td>\n",
       "      <td>2483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal_true</th>\n",
       "      <td>7328</td>\n",
       "      <td>60015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Malicious_true  Normal_true\n",
       "Malicious_true           56147         2483\n",
       "Normal_true               7328        60015"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cf_matrix = confusion_matrix(train_y, predict)\n",
    "pd.DataFrame(cf_matrix, index = ['Malicious_true', 'Normal_true'], columns = ['Malicious_true', 'Normal_true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bdedc3f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      normal       0.88      0.96      0.92     58630\n",
      "   malicious       0.96      0.89      0.92     67343\n",
      "\n",
      "    accuracy                           0.92    125973\n",
      "   macro avg       0.92      0.92      0.92    125973\n",
      "weighted avg       0.93      0.92      0.92    125973\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(train_y, predict, target_names=['normal', 'malicious']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2e4cdf74",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = lof.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4c8b024f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      normal       0.88      0.88      0.88     12833\n",
      "   malicious       0.84      0.84      0.84      9711\n",
      "\n",
      "    accuracy                           0.86     22544\n",
      "   macro avg       0.86      0.86      0.86     22544\n",
      "weighted avg       0.86      0.86      0.86     22544\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, test_pred, target_names=['normal', 'malicious']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outstanding-validity",
   "metadata": {},
   "source": [
    "#### Reference\n",
    "* <a href = \"https://www.kaggle.com/anushonkar/network-anamoly-detection\">Network Anomaly Detection</a>"
   ]
  }
 ],
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