One_day_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline \n",
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({-1: 149915, 1: 67713})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "data = pd.read_csv(\"20151219.txt\", sep = \"\\t\", header = None)\n",
    "data.columns = [\"conn len\", \"service\", \"src bytes\", \"dst bytes\", \"conn numb\", \"Same srv rate\", \"Serror rate\", \"Srv serror rate\", \"Dst host count\", \"Dst host srv count\", \"Dst host same src port rate\", \"Dst host serror rate\", \"Dst host srv serror rate\", \"Conn state\", \"IDS detection\", \"Malware detection\", \"Ashula detection\", \"attack_flag\", \"src IP\", \"src port\", \"dst IP\", \"dst port\", \"start time\", \"proto\"]\n",
    "\n",
    "print(Counter(data[\"attack_flag\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform to binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 149915, 0: 67713})\n"
     ]
    }
   ],
   "source": [
    "data.loc[:, \"attack_flag\"] = data.loc[:,\"attack_flag\"].replace(-2, -1)\n",
    "data.loc[:, \"attack_flag\"] = data.loc[:, \"attack_flag\"] * -1\n",
    "data.loc[:, \"attack_flag\"] = data.loc[:,\"attack_flag\"].replace(-1, 0)\n",
    "\n",
    "print(Counter(data[\"attack_flag\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(217628, 24)\n",
      "(217628, 21)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    149915\n",
       "0     67713\n",
       "Name: attack_flag, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice dataset, drop additional features\n",
    "print(data.shape)\n",
    "data = data.drop(['start time', 'dst IP', 'src IP'], axis=1)\n",
    "print(data.shape)\n",
    "\n",
    "# drop software markers\n",
    "data = data.drop([\"IDS detection\", \"Malware detection\", \"Ashula detection\"], axis=1)\n",
    "data[\"attack_flag\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217628 entries, 0 to 217627\n",
      "Data columns (total 18 columns):\n",
      "conn len                       217628 non-null float64\n",
      "service                        217628 non-null object\n",
      "src bytes                      217628 non-null int64\n",
      "dst bytes                      217628 non-null int64\n",
      "conn numb                      217628 non-null int64\n",
      "Same srv rate                  217628 non-null float64\n",
      "Serror rate                    217628 non-null float64\n",
      "Srv serror rate                217628 non-null float64\n",
      "Dst host count                 217628 non-null int64\n",
      "Dst host srv count             217628 non-null int64\n",
      "Dst host same src port rate    217628 non-null float64\n",
      "Dst host serror rate           217628 non-null float64\n",
      "Dst host srv serror rate       217628 non-null float64\n",
      "Conn state                     217628 non-null object\n",
      "attack_flag                    217628 non-null int64\n",
      "src port                       217628 non-null int64\n",
      "dst port                       217628 non-null int64\n",
      "proto                          217628 non-null object\n",
      "dtypes: float64(7), int64(8), object(3)\n",
      "memory usage: 29.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conn len</th>\n",
       "      <th>service</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
       "      <th>Serror rate</th>\n",
       "      <th>Srv serror rate</th>\n",
       "      <th>Dst host count</th>\n",
       "      <th>Dst host srv count</th>\n",
       "      <th>Dst host same src port rate</th>\n",
       "      <th>Dst host serror rate</th>\n",
       "      <th>Dst host srv serror rate</th>\n",
       "      <th>Conn state</th>\n",
       "      <th>attack_flag</th>\n",
       "      <th>src port</th>\n",
       "      <th>dst port</th>\n",
       "      <th>proto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217618</th>\n",
       "      <td>0.410043</td>\n",
       "      <td>other</td>\n",
       "      <td>150</td>\n",
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       "      <td>6338</td>\n",
       "      <td>17989</td>\n",
       "      <td>udp</td>\n",
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       "    <tr>\n",
       "      <th>217619</th>\n",
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       "      <td>other</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>OTH</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217620</th>\n",
       "      <td>1.215994</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>14015</td>\n",
       "      <td>17989</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217621</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>dns</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>44074</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217622</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>49263</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217623</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>58398</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217624</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>55373</td>\n",
       "      <td>23</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217625</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
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       "      <td>1</td>\n",
       "      <td>33244</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217626</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>47476</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217627</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>10106</td>\n",
       "      <td>17989</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        conn len service  src bytes  dst bytes  conn numb  Same srv rate  \\\n",
       "217618  0.410043   other        150          0          0            0.0   \n",
       "217619  0.000000   other          0          0          0            0.0   \n",
       "217620  1.215994   other         60          0          0            0.0   \n",
       "217621  0.000000     dns          0          0          0            0.0   \n",
       "217622  0.000000   other          0          0          0            0.0   \n",
       "217623  0.000000   other          0          0          1            1.0   \n",
       "217624  0.000000   other          0          0          0            0.0   \n",
       "217625  0.000000   other          0          0          0            0.0   \n",
       "217626  0.000000   other          0          0          1            1.0   \n",
       "217627  0.000000   other          0          0          0            0.0   \n",
       "\n",
       "        Serror rate  Srv serror rate  Dst host count  Dst host srv count  \\\n",
       "217618          0.0              1.0               0                   0   \n",
       "217619          0.0              1.0               0                   0   \n",
       "217620          0.0              0.0               0                   0   \n",
       "217621          0.0              0.0               0                   0   \n",
       "217622          0.0              1.0               0                   0   \n",
       "217623          1.0              1.0               0                   1   \n",
       "217624          0.0              1.0               2                   2   \n",
       "217625          0.0              1.0               0                   0   \n",
       "217626          1.0              1.0               0                   1   \n",
       "217627          0.0              1.0               0                   0   \n",
       "\n",
       "        Dst host same src port rate  Dst host serror rate  \\\n",
       "217618                          0.0                   0.0   \n",
       "217619                          0.0                   0.0   \n",
       "217620                          0.0                   0.0   \n",
       "217621                          0.0                   0.0   \n",
       "217622                          0.0                   0.0   \n",
       "217623                          0.0                   0.0   \n",
       "217624                          1.0                   1.0   \n",
       "217625                          0.0                   0.0   \n",
       "217626                          0.0                   0.0   \n",
       "217627                          0.0                   0.0   \n",
       "\n",
       "        Dst host srv serror rate Conn state  attack_flag  src port  dst port  \\\n",
       "217618                       0.0         S0            1      6338     17989   \n",
       "217619                       0.0        OTH            1        11         0   \n",
       "217620                       0.0         S0            1     14015     17989   \n",
       "217621                       0.0         S0            1     44074        53   \n",
       "217622                       0.0         S0            1     49263        25   \n",
       "217623                       1.0         S0            1     58398        25   \n",
       "217624                       1.0         S0            1     55373        23   \n",
       "217625                       0.0         S0            1     33244        25   \n",
       "217626                       1.0         S0            1     47476        25   \n",
       "217627                       0.0         S0            1     10106     17989   \n",
       "\n",
       "       proto  \n",
       "217618   udp  \n",
       "217619  icmp  \n",
       "217620   udp  \n",
       "217621   udp  \n",
       "217622   tcp  \n",
       "217623   tcp  \n",
       "217624   tcp  \n",
       "217625   tcp  \n",
       "217626   tcp  \n",
       "217627   udp  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conn len</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
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       "      <th>Dst host srv count</th>\n",
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       "      <th>Dst host serror rate</th>\n",
       "      <th>Dst host srv serror rate</th>\n",
       "      <th>attack_flag</th>\n",
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       "      <th>dst port</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
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       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>0.855259</td>\n",
       "      <td>56.899333</td>\n",
       "      <td>122.064555</td>\n",
       "      <td>2.952263</td>\n",
       "      <td>0.417481</td>\n",
       "      <td>0.089564</td>\n",
       "      <td>0.542215</td>\n",
       "      <td>29.491720</td>\n",
       "      <td>31.876730</td>\n",
       "      <td>0.071694</td>\n",
       "      <td>0.066464</td>\n",
       "      <td>0.146028</td>\n",
       "      <td>0.688859</td>\n",
       "      <td>34292.130006</td>\n",
       "      <td>3550.084736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.085410</td>\n",
       "      <td>519.846915</td>\n",
       "      <td>748.123612</td>\n",
       "      <td>5.519918</td>\n",
       "      <td>0.491905</td>\n",
       "      <td>0.284547</td>\n",
       "      <td>0.467622</td>\n",
       "      <td>44.351499</td>\n",
       "      <td>43.928101</td>\n",
       "      <td>0.256081</td>\n",
       "      <td>0.248495</td>\n",
       "      <td>0.350052</td>\n",
       "      <td>0.462961</td>\n",
       "      <td>19708.835701</td>\n",
       "      <td>10869.634486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>12375.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39682.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.013333</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50473.000000</td>\n",
       "      <td>1433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>563.012764</td>\n",
       "      <td>88066.000000</td>\n",
       "      <td>157723.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65535.000000</td>\n",
       "      <td>65498.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            conn len      src bytes      dst bytes      conn numb  \\\n",
       "count  217628.000000  217628.000000  217628.000000  217628.000000   \n",
       "mean        0.855259      56.899333     122.064555       2.952263   \n",
       "std         3.085410     519.846915     748.123612       5.519918   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000290       0.000000       0.000000       0.000000   \n",
       "75%         0.013333      45.000000      61.000000       5.000000   \n",
       "max       563.012764   88066.000000  157723.000000      84.000000   \n",
       "\n",
       "       Same srv rate    Serror rate  Srv serror rate  Dst host count  \\\n",
       "count  217628.000000  217628.000000    217628.000000   217628.000000   \n",
       "mean        0.417481       0.089564         0.542215       29.491720   \n",
       "std         0.491905       0.284547         0.467622       44.351499   \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         0.750000        0.000000   \n",
       "75%         1.000000       0.000000         1.000000       96.000000   \n",
       "max         1.000000       1.000000         1.000000      100.000000   \n",
       "\n",
       "       Dst host srv count  Dst host same src port rate  Dst host serror rate  \\\n",
       "count       217628.000000                217628.000000         217628.000000   \n",
       "mean            31.876730                     0.071694              0.066464   \n",
       "std             43.928101                     0.256081              0.248495   \n",
       "min              0.000000                     0.000000              0.000000   \n",
       "25%              0.000000                     0.000000              0.000000   \n",
       "50%              1.000000                     0.000000              0.000000   \n",
       "75%             96.000000                     0.000000              0.000000   \n",
       "max            100.000000                     1.000000              1.000000   \n",
       "\n",
       "       Dst host srv serror rate    attack_flag       src port       dst port  \n",
       "count             217628.000000  217628.000000  217628.000000  217628.000000  \n",
       "mean                   0.146028       0.688859   34292.130006    3550.084736  \n",
       "std                    0.350052       0.462961   19708.835701   10869.634486  \n",
       "min                    0.000000       0.000000       0.000000       0.000000  \n",
       "25%                    0.000000       0.000000   12375.000000      25.000000  \n",
       "50%                    0.000000       1.000000   39682.000000      53.000000  \n",
       "75%                    0.000000       1.000000   50473.000000    1433.000000  \n",
       "max                    1.000000       1.000000   65535.000000   65498.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217628, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing NaNs\n",
    "data = data.dropna()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S0        127464\n",
       "SF         59944\n",
       "RSTO       11476\n",
       "OTH         8640\n",
       "REJ         7998\n",
       "RSTRH        733\n",
       "RSTOS0       634\n",
       "SHR          381\n",
       "RSTR         304\n",
       "S1            52\n",
       "SH             2\n",
       "Name: Conn state, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Conn state\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other    126080\n",
       "dns       61990\n",
       "sip       16519\n",
       "ssh       11518\n",
       "snmp        665\n",
       "smtp        495\n",
       "rdp         328\n",
       "dhcp         20\n",
       "http         11\n",
       "ssl           2\n",
       "Name: service, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"service\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tcp     119162\n",
       "udp      93548\n",
       "icmp      4918\n",
       "Name: proto, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"proto\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a bit of feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## port numbers classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = 1023  # well-known port numbers\n",
    "t2 = 49151 # registered ports\n",
    "t3 = 65535 # client ports\n",
    "\n",
    "def wk(data_row):\n",
    "    if (data_row[\"src port\"] <= t1): \n",
    "        value = 1\n",
    "    elif ((data_row[\"src port\"] > t1) and (data_row[\"src port\"] <= t2)):\n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t2) and (data_row[\"src port\"] <= t3)):\n",
    "        value = 0\n",
    "    return value\n",
    "\n",
    "def reg(data_row):\n",
    "    if (data_row[\"src port\"] <= t1): \n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t1) and (data_row[\"src port\"] <= t2)):\n",
    "        value = 1\n",
    "    elif ((data_row[\"src port\"] > t2) and (data_row[\"src port\"] <= t3)):\n",
    "        value = 0\n",
    "    return value\n",
    "\n",
    "def cli(data_row):\n",
    "    if (data_row[\"src port\"] <= t1): \n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t1) and (data_row[\"src port\"] <= t2)):\n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t2) and (data_row[\"src port\"] <= t3)):\n",
    "        value = 1\n",
    "    return value\n",
    "\n",
    "data[\"well-known src pool\"] = data.apply(wk, axis=1)\n",
    "data[\"registered src pool\"] = data.apply(reg, axis=1)\n",
    "data[\"cli src pool\"] = data.apply(cli, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 211216, 1: 6412})\n",
      "Counter({1: 150534, 0: 67094})\n",
      "Counter({0: 156946, 1: 60682})\n"
     ]
    }
   ],
   "source": [
    "print(Counter(data[\"well-known src pool\"]))\n",
    "print(Counter(data[\"registered src pool\"]))\n",
    "print(Counter(data[\"cli src pool\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 217628 entries, 0 to 217627\n",
      "Data columns (total 21 columns):\n",
      "conn len                       217628 non-null float64\n",
      "service                        217628 non-null object\n",
      "src bytes                      217628 non-null int64\n",
      "dst bytes                      217628 non-null int64\n",
      "conn numb                      217628 non-null int64\n",
      "Same srv rate                  217628 non-null float64\n",
      "Serror rate                    217628 non-null float64\n",
      "Srv serror rate                217628 non-null float64\n",
      "Dst host count                 217628 non-null int64\n",
      "Dst host srv count             217628 non-null int64\n",
      "Dst host same src port rate    217628 non-null float64\n",
      "Dst host serror rate           217628 non-null float64\n",
      "Dst host srv serror rate       217628 non-null float64\n",
      "Conn state                     217628 non-null object\n",
      "attack_flag                    217628 non-null int64\n",
      "src port                       217628 non-null int64\n",
      "dst port                       217628 non-null int64\n",
      "proto                          217628 non-null object\n",
      "well-known src pool            217628 non-null int64\n",
      "registered src pool            217628 non-null int64\n",
      "cli src pool                   217628 non-null int64\n",
      "dtypes: float64(7), int64(11), object(3)\n",
      "memory usage: 36.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>src port</th>\n",
       "      <th>dst port</th>\n",
       "      <th>well-known src pool</th>\n",
       "      <th>registered src pool</th>\n",
       "      <th>cli src pool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40061</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53488</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35051</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1408</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55244</td>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44544</td>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50678</td>\n",
       "      <td>5900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35051</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50678</td>\n",
       "      <td>5900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50678</td>\n",
       "      <td>5900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>59625</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42626</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2194</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7432</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7206</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55133</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60169</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>63030</td>\n",
       "      <td>2222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31905</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>59625</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36939</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7432</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25385</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36877</td>\n",
       "      <td>2332</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7206</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56346</td>\n",
       "      <td>8888</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37854</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>56248</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>38273</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3026</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    src port  dst port  well-known src pool  registered src pool  cli src pool\n",
       "0      40061       111                    0                    1             0\n",
       "1      53488        80                    0                    0             1\n",
       "2      35051        23                    0                    1             0\n",
       "3       1408       445                    0                    1             0\n",
       "4      55244       995                    0                    0             1\n",
       "5      44544       995                    0                    1             0\n",
       "6      50678      5900                    0                    0             1\n",
       "7      35051        23                    0                    1             0\n",
       "8      50678      5900                    0                    0             1\n",
       "9      50678      5900                    0                    0             1\n",
       "10     59625        21                    0                    0             1\n",
       "11     42626       111                    0                    1             0\n",
       "12      2194       445                    0                    1             0\n",
       "13      7432        22                    0                    1             0\n",
       "14      7206        80                    0                    1             0\n",
       "15     55133        53                    0                    0             1\n",
       "16     60169        80                    0                    0             1\n",
       "17     63030      2222                    0                    0             1\n",
       "18     31905       445                    0                    1             0\n",
       "19     59625        21                    0                    0             1\n",
       "20     36939       111                    0                    1             0\n",
       "21      7432        22                    0                    1             0\n",
       "22     25385       445                    0                    1             0\n",
       "23     36877      2332                    0                    1             0\n",
       "24      7206        80                    0                    1             0\n",
       "25     56346      8888                    0                    0             1\n",
       "26     37854       445                    0                    1             0\n",
       "27     56248       445                    0                    0             1\n",
       "28     38273      1080                    0                    1             0\n",
       "29      3026       445                    0                    1             0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.loc[:, [\"src port\", \"dst port\", \"well-known src pool\", \"registered src pool\", \"cli src pool\", \"well-known dst pool\", \"registered dst pool\", \"cli dst pool\"]].head(30)\n",
    "data.loc[:, [\"src port\", \"dst port\", \"well-known src pool\", \"registered src pool\", \"cli src pool\"]].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change port number info to categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"src port\"] = data[\"src port\"].apply(str)\n",
    "data[\"dst port\"] = data[\"dst port\"].apply(str)\n",
    "data['src port'].nunique()\n",
    "data['dst port'].nunique()\n",
    "\n",
    "thresh1 = 125\n",
    "thresh2 = 125\n",
    "counts1 = data['src port'].value_counts()\n",
    "counts2 = data['dst port'].value_counts()\n",
    "\n",
    "repl1 = counts1[counts1 <= thresh1].index\n",
    "repl2 = counts2[counts2 <= thresh2].index\n",
    "\n",
    "#hot one encoding for src port numbers\n",
    "data = pd.concat([data, pd.get_dummies(data[\"src port\"].replace(repl1, 'other_src'), prefix=\"is_src\")], axis=1)\n",
    "\n",
    "#hot one encoding for dst port numbers\n",
    "data = pd.concat([data, pd.get_dummies(data[\"dst port\"].replace(repl2, 'other_dst'), prefix=\"is_dst\")], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217628, 214)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hot one encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>conn len</th>\n",
       "      <th>service</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
       "      <th>Serror rate</th>\n",
       "      <th>Srv serror rate</th>\n",
       "      <th>Dst host count</th>\n",
       "      <th>Dst host srv count</th>\n",
       "      <th>...</th>\n",
       "      <th>state_REJ</th>\n",
       "      <th>state_RSTO</th>\n",
       "      <th>state_RSTOS0</th>\n",
       "      <th>state_RSTR</th>\n",
       "      <th>state_RSTRH</th>\n",
       "      <th>state_S0</th>\n",
       "      <th>state_S1</th>\n",
       "      <th>state_SF</th>\n",
       "      <th>state_SH</th>\n",
       "      <th>state_SHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217618</th>\n",
       "      <td>0.410043</td>\n",
       "      <td>other</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217619</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217620</th>\n",
       "      <td>1.215994</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217621</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>dns</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217622</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217623</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217624</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217625</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217626</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217627</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 238 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        conn len service  src bytes  dst bytes  conn numb  Same srv rate  \\\n",
       "217618  0.410043   other        150          0          0            0.0   \n",
       "217619  0.000000   other          0          0          0            0.0   \n",
       "217620  1.215994   other         60          0          0            0.0   \n",
       "217621  0.000000     dns          0          0          0            0.0   \n",
       "217622  0.000000   other          0          0          0            0.0   \n",
       "217623  0.000000   other          0          0          1            1.0   \n",
       "217624  0.000000   other          0          0          0            0.0   \n",
       "217625  0.000000   other          0          0          0            0.0   \n",
       "217626  0.000000   other          0          0          1            1.0   \n",
       "217627  0.000000   other          0          0          0            0.0   \n",
       "\n",
       "        Serror rate  Srv serror rate  Dst host count  Dst host srv count  \\\n",
       "217618          0.0              1.0               0                   0   \n",
       "217619          0.0              1.0               0                   0   \n",
       "217620          0.0              0.0               0                   0   \n",
       "217621          0.0              0.0               0                   0   \n",
       "217622          0.0              1.0               0                   0   \n",
       "217623          1.0              1.0               0                   1   \n",
       "217624          0.0              1.0               2                   2   \n",
       "217625          0.0              1.0               0                   0   \n",
       "217626          1.0              1.0               0                   1   \n",
       "217627          0.0              1.0               0                   0   \n",
       "\n",
       "          ...      state_REJ  state_RSTO  state_RSTOS0 state_RSTR  \\\n",
       "217618    ...              0           0             0          0   \n",
       "217619    ...              0           0             0          0   \n",
       "217620    ...              0           0             0          0   \n",
       "217621    ...              0           0             0          0   \n",
       "217622    ...              0           0             0          0   \n",
       "217623    ...              0           0             0          0   \n",
       "217624    ...              0           0             0          0   \n",
       "217625    ...              0           0             0          0   \n",
       "217626    ...              0           0             0          0   \n",
       "217627    ...              0           0             0          0   \n",
       "\n",
       "        state_RSTRH state_S0 state_S1 state_SF  state_SH  state_SHR  \n",
       "217618            0        1        0        0         0          0  \n",
       "217619            0        0        0        0         0          0  \n",
       "217620            0        1        0        0         0          0  \n",
       "217621            0        1        0        0         0          0  \n",
       "217622            0        1        0        0         0          0  \n",
       "217623            0        1        0        0         0          0  \n",
       "217624            0        1        0        0         0          0  \n",
       "217625            0        1        0        0         0          0  \n",
       "217626            0        1        0        0         0          0  \n",
       "217627            0        1        0        0         0          0  \n",
       "\n",
       "[10 rows x 238 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### hot one encoding ###\n",
    "\n",
    "# hot one encoding for service\n",
    "data = pd.concat([data, pd.get_dummies(data[\"service\"], prefix=\"is\")], axis=1)\n",
    "data.tail(10)\n",
    "\n",
    "#hot one encoding for proto - commented due to reduced column number\n",
    "data = pd.concat([data, pd.get_dummies(data[\"proto\"], prefix=\"is\")], axis=1)\n",
    "data.tail(10)\n",
    "\n",
    "# hot one encoding for conn state\n",
    "data = pd.concat([data, pd.get_dummies(data[\"Conn state\"], prefix=\"state\")], axis=1)\n",
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 217628 entries, 0 to 217627\n",
      "Columns: 238 entries, conn len to state_SHR\n",
      "dtypes: float64(7), int64(9), object(5), uint8(217)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot function definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting method definition\n",
    "\n",
    "def plot_stats(stat, name, width, rot):\n",
    "    fig, ax = plt.subplots(figsize=(width, 4))    \n",
    "    ax.bar(range(len(stat)), list(stat.values()), align='center')\n",
    "    plt.xticks(range(len(stat)), list(stat.keys()), rotation=rot)\n",
    "    for a,b in enumerate(stat.items()):\n",
    "        ax.text(a, b[1] + max(stat.values())/60 , str(round(b[1],5)), horizontalalignment ='center', verticalalignment='baseline')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    plt.title(name + \" of tested algorithms\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 14952, 1: 7548})\n",
      "Counter({1: 14607, 0: 7893})\n",
      "Counter({1: 86179, 0: 18821})\n"
     ]
    }
   ],
   "source": [
    "data[\"src port\"] = data[\"src port\"].apply(str)\n",
    "data[\"dst port\"] = data[\"dst port\"].apply(str)\n",
    "\n",
    "data.rename(columns={'Dst host count': 'Dst_host_count', \"Dst host srv count\" : \"Dst_host_srv_count\",\n",
    "                    \"Dst host same src port rate\" : \"Dst_host_same_src_port_rate\",\n",
    "                    \"Dst host serror rate\" : \"Dst_host_serror_rate\",\n",
    "                    \"Dst host srv serror rate\" : \"Dst_host_srv_serror_rate\",\n",
    "                    \"Same srv rate\" : \"Same_srv_rate\",\n",
    "                    \"Srv serror rate\" : \"Srv_serror_rate\",\n",
    "                    \"conn len\" : \"conn_len\",\n",
    "                    \"src bytes\" : \"src_bytes\",\n",
    "                    \"dst bytes\" : \"dst_bytes\",\n",
    "                    \"conn numb\" : \"conn_numb\",\n",
    "                    \"Serror rate\" : \"Serror_rate\",\n",
    "                    \"cli src pool\" : \"cli_src_pool\",\n",
    "                    \"registered src pool\" : \"registered_src_pool\",\n",
    "                    \"well-known src pool\" : \"well_known_src_pool\",\n",
    "                    \"dst port\" : \"dst_port\",\n",
    "                    \"src port\" : \"src_port\",\n",
    "                     \"Conn state\" : \"Conn_state\" \n",
    "                    }, inplace=True)\n",
    "\n",
    "sampled_data = data.loc[range(150000), :]\n",
    "\n",
    "train_alg, validate_alg, test_alg = np.split(sampled_data, [int(.7*len(sampled_data)), int(.85*len(sampled_data))])\n",
    "\n",
    "train_alg = train_alg.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "validate_alg = validate_alg.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "test_alg = test_alg.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "\n",
    "train_alg = train_alg.sample(frac=1)\n",
    "validate_alg = validate_alg.sample(frac=1)\n",
    "test_alg = test_alg.sample(frac=1)\n",
    "\n",
    "y_train_alg = train_alg.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_train_alg = train_alg.drop([\"attack_flag\"], axis=1).astype(np.float32)\n",
    "y_validate_alg = validate_alg.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_validate_alg = validate_alg.drop([\"attack_flag\"], axis=1).astype(np.float32)\n",
    "y_test_alg = test_alg.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_test_alg = test_alg.drop([\"attack_flag\"], axis=1).astype(np.float32)\n",
    "\n",
    "print(Counter(y_test_alg))\n",
    "print(Counter(y_validate_alg))\n",
    "print(Counter(y_train_alg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 30946, 0: 1699})\n",
      "Counter({0: 22937, 1: 9707})\n",
      "Counter({1: 109262, 0: 43077})\n"
     ]
    }
   ],
   "source": [
    "data['src_port'] = data['src_port'].apply(lambda x: \"src_\" + x)\n",
    "data['dst_port'] = data['dst_port'].apply(lambda x: \"dst_\" + x)\n",
    "\n",
    "train, validate, test = np.split(data, [int(.7*len(data)), int(.85*len(data))])\n",
    "\n",
    "\n",
    "train_ex = train\n",
    "validate_ex = validate\n",
    "test_ex = test\n",
    "\n",
    "train = train.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "validate = validate.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "test = test.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "\n",
    "train = train.sample(frac=1)\n",
    "validate = validate.sample(frac=1)\n",
    "test = test.sample(frac=1)\n",
    "\n",
    "y_train = train.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_train = train.drop([\"attack_flag\"], axis=1).astype(np.float32)\n",
    "y_validate = validate.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_validate = validate.drop([\"attack_flag\"], axis=1).astype(np.float32)\n",
    "y_test = test.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_test = test.drop([\"attack_flag\"], axis=1).astype(np.float32)\n",
    "\n",
    "print(Counter(y_test))\n",
    "print(Counter(y_validate))\n",
    "print(Counter(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algoritms performance comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 30946, 0: 1699})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "print(Counter(y_test))\n",
    "\n",
    "Acc = {}\n",
    "F1S = {}\n",
    "Prec = {}\n",
    "Rec = {}\n",
    "FPR = {}\n",
    "FNR = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: baseline     Accuracy: 0.94796, F1-score: 0.97328, Precision: 0.94796, Recall: 1.00000, FPR: 1.00000, FNR: 0.00000\n",
      "TN:   0; FP: 1699; FN:    0; TP: 30946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Metrics of baseline method\n",
    "from sklearn import metrics\n",
    "\n",
    "name = \"baseline\"\n",
    "\n",
    "if(y_test.mean() > (1-y_test.mean())):\n",
    "    y_pred = np.ones(len(y_test)).ravel() \n",
    "else:\n",
    "    y_pred = np.zeros(len(y_test)).ravel() \n",
    "    \n",
    "Acc[name] = metrics.accuracy_score(y_test, y_pred)\n",
    "F1S[name] = metrics.f1_score(y_test, y_pred)\n",
    "Prec[name] = metrics.precision_score(y_test, y_pred)\n",
    "Rec[name] = metrics.recall_score(y_test, y_pred)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test, y_pred).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: LogisticReg  Accuracy: 0.97326, F1-score: 0.98587, Precision: 0.98780, Recall: 0.98394, FPR: 0.22131, FNR: 0.01606\n",
      "TN: 1323; FP: 376; FN:  497; TP: 30449\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "clf = LogisticRegression(solver = 'newton-cg')\n",
    "\n",
    "model = clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "name = \"LogisticReg\"\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(y_test, y_pred)\n",
    "F1S[name] = metrics.f1_score(y_test, y_pred)\n",
    "Prec[name] = metrics.precision_score(y_test, y_pred)\n",
    "Rec[name] = metrics.recall_score(y_test, y_pred)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test, y_pred).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: AdaBoost     Accuracy: 0.98726, F1-score: 0.99326, Precision: 0.99642, Recall: 0.99011, FPR: 0.06474, FNR: 0.00989\n",
      "TN: 1589; FP: 110; FN:  306; TP: 30640\n",
      "\n",
      "Algorithm: DecisionTree Accuracy: 0.98759, F1-score: 0.99343, Precision: 0.99707, Recall: 0.98982, FPR: 0.05297, FNR: 0.01018\n",
      "TN: 1609; FP:  90; FN:  315; TP: 30631\n",
      "\n",
      "Algorithm: NaiveBayes   Accuracy: 0.92755, F1-score: 0.96037, Precision: 0.99749, Recall: 0.92590, FPR: 0.04238, FNR: 0.07410\n",
      "TN: 1627; FP:  72; FN: 2293; TP: 28653\n",
      "\n",
      "Algorithm: RandomTree   Accuracy: 0.98894, F1-score: 0.99415, Precision: 0.99714, Recall: 0.99118, FPR: 0.05180, FNR: 0.00882\n",
      "TN: 1611; FP:  88; FN:  273; TP: 30673\n",
      "\n",
      "Algorithm: RandomForest Accuracy: 0.98821, F1-score: 0.99376, Precision: 0.99633, Recall: 0.99121, FPR: 0.06651, FNR: 0.00879\n",
      "TN: 1586; FP: 113; FN:  272; TP: 30674\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "\n",
    "seed = 12\n",
    "num_trees = 45\n",
    "\n",
    "classifiers = {\n",
    "               \"AdaBoost\":AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees),\n",
    "               \"DecisionTree\":DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13),\n",
    "               \"NaiveBayes\":GaussianNB(),\n",
    "               \"RandomTree\":BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13), n_estimators=93),\n",
    "               \"RandomForest\":RandomForestClassifier(n_estimators=178, max_depth=31, random_state=0),\n",
    "}\n",
    "\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    Acc[name] = metrics.accuracy_score(y_test, y_pred)\n",
    "    F1S[name] = metrics.f1_score(y_test, y_pred)\n",
    "    Prec[name] = metrics.precision_score(y_test, y_pred)\n",
    "    Rec[name] = metrics.recall_score(y_test, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_test, y_pred).ravel()\n",
    "    FPR[name] = fp/(fp+tn)\n",
    "    FNR[name] = fn/(tp+fn)\n",
    "\n",
    "    print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "    print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56ccf3128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56ccf3198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56ccf2a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa549142518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa549109828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa548fe4f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\", 8, 55)\n",
    "plot_stats(F1S, \"F1-score\", 8, 55)\n",
    "plot_stats(Prec, \"Precision\", 8, 55)\n",
    "plot_stats(Rec, \"Recall\", 8, 55)\n",
    "plot_stats(FPR, \"False Positive Rate\", 8, 55)\n",
    "plot_stats(FNR, \"False Negative Rate\", 8, 55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Features for Large-Scale Kernel Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152339, 232)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input_fn = tf.estimator.inputs.numpy_input_fn(    \n",
    "    x={\"features\": X_train.values},    \n",
    "    y=y_train,    \n",
    "    batch_size=180,    \n",
    "    num_epochs=None,    \n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32644, 232)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"features\": X_validate.values},\n",
    "    y=y_validate,\n",
    "    batch_size=X_validate.shape[0],\n",
    "    num_epochs=1,\n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32645, 232)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"features\": X_test.values},\n",
    "    y=y_test,\n",
    "    batch_size=X_test.shape[0],\n",
    "    num_epochs=1,\n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using explicit kernel mappings with the linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'Dst host count': 'Dst_host_count', \"Dst host srv count\" : \"Dst_host_srv_count\",\n",
    "                    \"Dst host same src port rate\" : \"Dst_host_same_src_port_rate\",\n",
    "                    \"Dst host serror rate\" : \"Dst_host_serror_rate\",\n",
    "                    \"Dst host srv serror rate\" : \"Dst_host_srv_serror_rate\",\n",
    "                    \"Same srv rate\" : \"Same_srv_rate\",\n",
    "                    \"Srv serror rate\" : \"Srv_serror_rate\",\n",
    "                    \"conn len\" : \"conn_len\",\n",
    "                    \"src bytes\" : \"src_bytes\",\n",
    "                    \"dst bytes\" : \"dst_bytes\",\n",
    "                    \"conn numb\" : \"conn_numb\",\n",
    "                    \"Serror rate\" : \"Serror_rate\",\n",
    "                    \"cli src pool\" : \"cli_src_pool\",\n",
    "                    \"registered src pool\" : \"registered_src_pool\",\n",
    "                    \"well-known src pool\" : \"well_known_src_pool\", \n",
    "                    \"dst port\" : \"dst_port\",\n",
    "                    \"src port\" : \"src_port\",\n",
    "                    \"Conn state\" : \"Conn_state\" \n",
    "                    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_column = tf.contrib.layers.real_valued_column('features', dimension=len(X_train.columns.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_mapper = tf.contrib.kernel_methods.RandomFourierFeatureMapper(input_dim=len(X_train.columns.values), output_dim=5000, stddev=5, name='rffm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_mappers = {feat_column: [kernel_mapper]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "! rm -r kernel_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.FtrlOptimizer(learning_rate=25, l2_regularization_strength=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/contrib/kernel_methods/python/kernel_estimators.py:305: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.contrib.estimator.*_head.\n",
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.__init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*\n",
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fa530876358>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_device_fn': None, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_protocol': None, '_session_config': None, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': 'kernel_train'}\n"
     ]
    }
   ],
   "source": [
    "estimator_kernel = tf.contrib.kernel_methods.KernelLinearClassifier(\n",
    "    n_classes=2,\n",
    "    optimizer=optimizer,\n",
    "    kernel_mappers=kernel_mappers, \n",
    "    model_dir=\"kernel_train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.\n",
      "WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py:804: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into kernel_train/model.ckpt.\n",
      "INFO:tensorflow:loss = 0.6931474, step = 1\n",
      "INFO:tensorflow:global_step/sec: 110.113\n",
      "INFO:tensorflow:loss = 0.049137414, step = 101 (0.908 sec)\n",
      "INFO:tensorflow:global_step/sec: 131.228\n",
      "INFO:tensorflow:loss = 0.067286834, step = 201 (0.762 sec)\n",
      "INFO:tensorflow:global_step/sec: 129.676\n",
      "INFO:tensorflow:loss = 0.032155592, step = 301 (0.771 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.471\n",
      "INFO:tensorflow:loss = 0.08087082, step = 401 (0.785 sec)\n",
      "INFO:tensorflow:global_step/sec: 130.235\n",
      "INFO:tensorflow:loss = 0.057637844, step = 501 (0.768 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.072\n",
      "INFO:tensorflow:loss = 0.008079912, step = 601 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.994\n",
      "INFO:tensorflow:loss = 0.0074813096, step = 701 (0.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 124.984\n",
      "INFO:tensorflow:loss = 0.06537902, step = 801 (0.800 sec)\n",
      "INFO:tensorflow:global_step/sec: 129.16\n",
      "INFO:tensorflow:loss = 0.014716287, step = 901 (0.774 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.871\n",
      "INFO:tensorflow:loss = 0.0132372165, step = 1001 (0.788 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.139\n",
      "INFO:tensorflow:loss = 0.015940186, step = 1101 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 129.593\n",
      "INFO:tensorflow:loss = 0.046306625, step = 1201 (0.772 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.311\n",
      "INFO:tensorflow:loss = 0.026614906, step = 1301 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.768\n",
      "INFO:tensorflow:loss = 0.019914037, step = 1401 (0.789 sec)\n",
      "INFO:tensorflow:global_step/sec: 125.499\n",
      "INFO:tensorflow:loss = 0.012871843, step = 1501 (0.797 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.672\n",
      "INFO:tensorflow:loss = 0.0098857405, step = 1601 (0.777 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.546\n",
      "INFO:tensorflow:loss = 0.005372802, step = 1701 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 125.815\n",
      "INFO:tensorflow:loss = 0.0027731424, step = 1801 (0.795 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.065\n",
      "INFO:tensorflow:loss = 0.06797324, step = 1901 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.534\n",
      "INFO:tensorflow:loss = 0.032333143, step = 2001 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 129.559\n",
      "INFO:tensorflow:loss = 0.007892344, step = 2101 (0.772 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.515\n",
      "INFO:tensorflow:loss = 0.029222181, step = 2201 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.92\n",
      "INFO:tensorflow:loss = 0.014990324, step = 2301 (0.776 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.186\n",
      "INFO:tensorflow:loss = 0.00662799, step = 2401 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.15\n",
      "INFO:tensorflow:loss = 0.007928455, step = 2501 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 129.68\n",
      "INFO:tensorflow:loss = 0.00806705, step = 2601 (0.771 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.009\n",
      "INFO:tensorflow:loss = 0.0031041007, step = 2701 (0.794 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.155\n",
      "INFO:tensorflow:loss = 0.006898805, step = 2801 (0.780 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.611\n",
      "INFO:tensorflow:loss = 0.0085344445, step = 2901 (0.783 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.101\n",
      "INFO:tensorflow:loss = 0.0075426023, step = 3001 (0.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.738\n",
      "INFO:tensorflow:loss = 0.006161268, step = 3101 (0.777 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.649\n",
      "INFO:tensorflow:loss = 0.014346991, step = 3201 (0.777 sec)\n",
      "INFO:tensorflow:global_step/sec: 125.591\n",
      "INFO:tensorflow:loss = 0.014057562, step = 3301 (0.797 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.003\n",
      "INFO:tensorflow:loss = 0.13492481, step = 3401 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.608\n",
      "INFO:tensorflow:loss = 0.031084405, step = 3501 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.092\n",
      "INFO:tensorflow:loss = 0.013662156, step = 3601 (0.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.324\n",
      "INFO:tensorflow:loss = 0.011649611, step = 3701 (0.792 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.211\n",
      "INFO:tensorflow:loss = 0.0077103344, step = 3801 (0.792 sec)\n",
      "INFO:tensorflow:global_step/sec: 125.595\n",
      "INFO:tensorflow:loss = 0.0075447485, step = 3901 (0.796 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.202\n",
      "INFO:tensorflow:loss = 0.009284087, step = 4001 (0.781 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.236\n",
      "INFO:tensorflow:loss = 0.002033595, step = 4101 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.166\n",
      "INFO:tensorflow:loss = 0.008482958, step = 4201 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 123.92\n",
      "INFO:tensorflow:loss = 0.009877056, step = 4301 (0.807 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.605\n",
      "INFO:tensorflow:loss = 0.02231225, step = 4401 (0.784 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.579\n",
      "INFO:tensorflow:loss = 0.0027840426, step = 4501 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 122.585\n",
      "INFO:tensorflow:loss = 0.0077775633, step = 4601 (0.815 sec)\n",
      "INFO:tensorflow:global_step/sec: 125.774\n",
      "INFO:tensorflow:loss = 0.055367768, step = 4701 (0.795 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.895\n",
      "INFO:tensorflow:loss = 0.020432493, step = 4801 (0.788 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.734\n",
      "INFO:tensorflow:loss = 0.040379018, step = 4901 (0.789 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.793\n",
      "INFO:tensorflow:loss = 0.007130698, step = 5001 (0.789 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.129\n",
      "INFO:tensorflow:loss = 0.007142152, step = 5101 (0.780 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.394\n",
      "INFO:tensorflow:loss = 0.1305489, step = 5201 (0.779 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.647\n",
      "INFO:tensorflow:loss = 0.00477994, step = 5301 (0.791 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.834\n",
      "INFO:tensorflow:loss = 0.01323237, step = 5401 (0.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.149\n",
      "INFO:tensorflow:loss = 0.0053730155, step = 5501 (0.780 sec)\n",
      "INFO:tensorflow:global_step/sec: 130.693\n",
      "INFO:tensorflow:loss = 0.0030495296, step = 5601 (0.765 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.91\n",
      "INFO:tensorflow:loss = 0.008572183, step = 5701 (0.782 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.109\n",
      "INFO:tensorflow:loss = 0.013580009, step = 5801 (0.781 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.139\n",
      "INFO:tensorflow:loss = 0.0044750837, step = 5901 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 131.201\n",
      "INFO:tensorflow:loss = 0.0068363952, step = 6001 (0.762 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.045\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 0.009976852, step = 6101 (0.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 129.463\n",
      "INFO:tensorflow:loss = 0.006024813, step = 6201 (0.773 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.245\n",
      "INFO:tensorflow:loss = 0.0072238063, step = 6301 (0.792 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.693\n",
      "INFO:tensorflow:loss = 0.0067242044, step = 6401 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.309\n",
      "INFO:tensorflow:loss = 0.006583132, step = 6501 (0.785 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.571\n",
      "INFO:tensorflow:loss = 0.024788486, step = 6601 (0.784 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.667\n",
      "INFO:tensorflow:loss = 0.022964207, step = 6701 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.266\n",
      "INFO:tensorflow:loss = 0.0049038134, step = 6801 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.235\n",
      "INFO:tensorflow:loss = 0.005774965, step = 6901 (0.791 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.155\n",
      "INFO:tensorflow:loss = 0.034280207, step = 7001 (0.793 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.119\n",
      "INFO:tensorflow:loss = 0.0026336287, step = 7101 (0.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.192\n",
      "INFO:tensorflow:loss = 0.0077471277, step = 7201 (0.792 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.669\n",
      "INFO:tensorflow:loss = 0.00574121, step = 7301 (0.783 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.934\n",
      "INFO:tensorflow:loss = 0.037161417, step = 7401 (0.788 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.247\n",
      "INFO:tensorflow:loss = 0.023628434, step = 7501 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.41\n",
      "INFO:tensorflow:loss = 0.007912647, step = 7601 (0.785 sec)\n",
      "INFO:tensorflow:global_step/sec: 128.197\n",
      "INFO:tensorflow:loss = 0.0013283589, step = 7701 (0.780 sec)\n",
      "INFO:tensorflow:global_step/sec: 127.216\n",
      "INFO:tensorflow:loss = 0.02314648, step = 7801 (0.786 sec)\n",
      "INFO:tensorflow:global_step/sec: 126.398\n",
      "INFO:tensorflow:loss = 0.006996989, step = 7901 (0.791 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 8000 into kernel_train/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.043965913.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelLinearClassifier(params={'head': <tensorflow.contrib.learn.python.learn.estimators.head._BinaryLogisticHead object at 0x7fa5308dcfd0>, 'feature_columns': {_RealValuedColumn(column_name='features_MAPPED', dimension=5000, default_value=None, dtype=tf.float32, normalizer=None)}, 'optimizer': <tensorflow.python.training.ftrl.FtrlOptimizer object at 0x7fa5308dcc18>, 'kernel_mappers': {_RealValuedColumn(column_name='features', dimension=232, default_value=None, dtype=tf.float32, normalizer=None): [<tensorflow.contrib.kernel_methods.python.mappers.random_fourier_features.RandomFourierFeatureMapper object at 0x7fa5308dcbe0>]}})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator_kernel.fit(input_fn=train_input_fn, steps=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.\n",
      "WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "INFO:tensorflow:Starting evaluation at 2019-06-12-07:14:53\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from kernel_train/model.ckpt-8000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Evaluation [1/1]\n",
      "INFO:tensorflow:Finished evaluation at 2019-06-12-07:14:55\n",
      "INFO:tensorflow:Saving dict for global step 8000: accuracy = 0.9983764, accuracy/baseline_label_mean = 0.2973594, accuracy/threshold_0.500000_mean = 0.9983764, auc = 0.9999087, auc_precision_recall = 0.999759, global_step = 8000, labels/actual_label_mean = 0.2973594, labels/prediction_mean = 0.29662213, loss = 0.00682516, precision/positive_threshold_0.500000_mean = 0.9955852, recall/positive_threshold_0.500000_mean = 0.9989698\n"
     ]
    }
   ],
   "source": [
    "eval_metrics = estimator_kernel.evaluate(input_fn=eval_input_fn, steps=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9983764\n",
      "accuracy/baseline_label_mean: 0.2973594\n",
      "accuracy/threshold_0.500000_mean: 0.9983764\n",
      "auc: 0.9999087\n",
      "auc_precision_recall: 0.999759\n",
      "global_step: 8000\n",
      "labels/actual_label_mean: 0.2973594\n",
      "labels/prediction_mean: 0.29662213\n",
      "loss: 0.00682516\n",
      "precision/positive_threshold_0.500000_mean: 0.9955852\n",
      "recall/positive_threshold_0.500000_mean: 0.9989698\n"
     ]
    }
   ],
   "source": [
    "for key,value in sorted(eval_metrics.items()):\n",
    "  print('%s: %s' % (key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "correctness = []\n",
    "class_id = []\n",
    "pred_iterator = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from kernel_train/model.ckpt-8000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "pred_iterator = estimator_kernel.predict(input_fn = test_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for pred_dict in pred_iterator:\n",
    "    class_id.append(pred_dict['classes'])\n",
    "    #print(pred_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: RFFM         Accuracy: 0.99354, F1-score: 0.99659, Precision: 0.99542, Recall: 0.99777, FPR: 0.08358, FNR: 0.00223\n",
      "TN: 1557; FP: 142; FN:   69; TP: 30877\n",
      "\n"
     ]
    }
   ],
   "source": [
    "name = \"RFFM\"\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(y_test, class_id)\n",
    "F1S[name] = metrics.f1_score(y_test, class_id)\n",
    "Prec[name] = metrics.precision_score(y_test, class_id)\n",
    "Rec[name] = metrics.recall_score(y_test, class_id)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test, class_id).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural network part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import io\n",
    "import math\n",
    "import tensorflow as tf\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_numeric_feat = [\"Same_srv_rate\", \"Serror_rate\", \"Srv_serror_rate\", \"Dst_host_count\", \"Dst_host_srv_count\", \"Dst_host_same_src_port_rate\", \"Dst_host_serror_rate\", \"Dst_host_srv_serror_rate\", \"well_known_src_pool\", \"registered_src_pool\", \"cli_src_pool\"]\n",
    "wide_numeric_feat = [\"conn_len\",  \"src_bytes\", \"dst_bytes\", \"conn_numb\"]\n",
    "categorical_feat = [\"service\", \"Conn_state\", \"src_port\", \"dst_port\", \"proto\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_numeric_feat_tf = [tf.feature_column.numeric_column(k) for k in deep_numeric_feat]\n",
    "wide_numeric_feat_tf = [tf.feature_column.numeric_column(k) for k in wide_numeric_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "src_port_mod = data[\"src_port\"].unique().tolist()\n",
    "src_port = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    'src_port', src_port_mod)\n",
    "\n",
    "dst_port_mod = data[\"dst_port\"].unique().tolist()\n",
    "dst_port = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    'dst_port', dst_port_mod)\n",
    "\n",
    "service = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    'service', data[\"service\"].unique().tolist())\n",
    "\n",
    "Conn_state = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    'Conn_state', data[\"Conn_state\"].unique().tolist())\n",
    "\n",
    "proto = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    'proto', data[\"proto\"].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_columns = [\n",
    "    tf.feature_column.indicator_column(service),\n",
    "    tf.feature_column.indicator_column(Conn_state),\n",
    "    tf.feature_column.indicator_column(proto),\n",
    "    tf.feature_column.embedding_column(src_port, dimension=10),\n",
    "    tf.feature_column.embedding_column(dst_port, dimension=6),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossed_columns = [\n",
    "      tf.feature_column.crossed_column(\n",
    "          ['src_port', 'service'], hash_bucket_size=10000),\n",
    "      tf.feature_column.crossed_column(\n",
    "          ['dst_port', 'service'], hash_bucket_size=10000),\n",
    "      tf.feature_column.crossed_column(\n",
    "          ['conn_len', 'src_bytes'], hash_bucket_size=10000),\n",
    "      tf.feature_column.crossed_column(\n",
    "          ['conn_len', 'dst_bytes'], hash_bucket_size=10000),\n",
    "      tf.feature_column.crossed_column(\n",
    "          ['dst_port', 'conn_numb'], hash_bucket_size=10000),  \n",
    "      tf.feature_column.crossed_column(\n",
    "          ['service', 'proto'], hash_bucket_size=100),\n",
    "    \n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "! rm -r NN_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': '/home/matz/Desktop/ml_ids/NN_model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fa51858d5f8>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "NN_model = tf.estimator.DNNLinearCombinedClassifier(\n",
    "    model_dir = \"/home/matz/Desktop/ml_ids/NN_model\",\n",
    "    linear_optimizer = tf.train.FtrlOptimizer(learning_rate=3, l2_regularization_strength=0.001),\n",
    "    linear_feature_columns = deep_columns + deep_numeric_feat_tf + wide_numeric_feat_tf + crossed_columns,\n",
    "    dnn_activation_fn=tf.nn.selu,\n",
    "    dnn_feature_columns = deep_columns + deep_numeric_feat_tf + wide_numeric_feat_tf,\n",
    "    dnn_hidden_units = [950, 550, 300, 175]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_set= deep_numeric_feat + wide_numeric_feat + categorical_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_input_fn(data_set, num_epochs=None, n_batch = 256, shuffle=True):\n",
    "    return tf.estimator.inputs.pandas_input_fn(\n",
    "       x=pd.DataFrame({k: data_set[k].values for k in feature_set}),\n",
    "       y = pd.Series(data_set[\"attack_flag\"].values),\n",
    "       batch_size=n_batch,   \n",
    "       num_epochs=num_epochs,\n",
    "       shuffle=shuffle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:From /home/matz/.local/lib/python3.6/site-packages/tensorflow/python/ops/sparse_ops.py:1165: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /home/matz/Desktop/ml_ids/NN_model/model.ckpt.\n",
      "INFO:tensorflow:loss = 104.5662, step = 1\n",
      "INFO:tensorflow:global_step/sec: 64.8931\n",
      "INFO:tensorflow:loss = 85.66586, step = 101 (1.542 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.3847\n",
      "INFO:tensorflow:loss = 1.9838582e-07, step = 201 (1.260 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.9533\n",
      "INFO:tensorflow:loss = 0.513646, step = 301 (1.235 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.2875\n",
      "INFO:tensorflow:loss = 259.70264, step = 401 (1.294 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.6559\n",
      "INFO:tensorflow:loss = 2.2262657, step = 501 (1.288 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.2094\n",
      "INFO:tensorflow:loss = 2.1066373e-14, step = 601 (1.247 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.1151\n",
      "INFO:tensorflow:loss = 0.0053106225, step = 701 (1.232 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.2717\n",
      "INFO:tensorflow:loss = 7.675779e-06, step = 801 (1.295 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.3454\n",
      "INFO:tensorflow:loss = 3.1086202, step = 901 (1.244 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.945\n",
      "INFO:tensorflow:loss = 91.96327, step = 1001 (1.235 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.4342\n",
      "INFO:tensorflow:loss = 0.047766466, step = 1101 (1.291 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.4253\n",
      "INFO:tensorflow:loss = 9.1960906e-08, step = 1201 (1.243 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.38\n",
      "INFO:tensorflow:loss = 0.0006185362, step = 1301 (1.260 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.0469\n",
      "INFO:tensorflow:loss = 0.10275731, step = 1401 (1.315 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.734\n",
      "INFO:tensorflow:loss = 9.454053e-13, step = 1501 (1.240 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.5291\n",
      "INFO:tensorflow:loss = 27.106567, step = 1601 (1.241 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.4971\n",
      "INFO:tensorflow:loss = 2.400621e-05, step = 1701 (1.290 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.2232\n",
      "INFO:tensorflow:loss = 27.136848, step = 1801 (1.278 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.3764\n",
      "INFO:tensorflow:loss = 1.6246488e-05, step = 1901 (1.260 sec)\n",
      "INFO:tensorflow:global_step/sec: 82.2264\n",
      "INFO:tensorflow:loss = 31.849619, step = 2001 (1.216 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.0182\n",
      "INFO:tensorflow:loss = 24.326727, step = 2101 (1.234 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.2915\n",
      "INFO:tensorflow:loss = 5.2896354e-09, step = 2201 (1.261 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.3124\n",
      "INFO:tensorflow:loss = 1.05124114e-13, step = 2301 (1.261 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.7346\n",
      "INFO:tensorflow:loss = 5.415954e-13, step = 2401 (1.270 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.8448\n",
      "INFO:tensorflow:loss = 3.7566014e-12, step = 2501 (1.237 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.767\n",
      "INFO:tensorflow:loss = 9.304446, step = 2601 (1.253 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.4547\n",
      "INFO:tensorflow:loss = 42.33331, step = 2701 (1.275 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.1906\n",
      "INFO:tensorflow:loss = 43.23096, step = 2801 (1.263 sec)\n",
      "INFO:tensorflow:global_step/sec: 82.4892\n",
      "INFO:tensorflow:loss = 0.00028893808, step = 2901 (1.212 sec)\n",
      "INFO:tensorflow:global_step/sec: 83.131\n",
      "INFO:tensorflow:loss = 5.466059, step = 3001 (1.202 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.5362\n",
      "INFO:tensorflow:loss = 0.1709547, step = 3101 (1.307 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.337\n",
      "INFO:tensorflow:loss = 3.3454106e-15, step = 3201 (1.230 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.5465\n",
      "INFO:tensorflow:loss = 5.0040134e-18, step = 3301 (1.226 sec)\n",
      "INFO:tensorflow:global_step/sec: 83.341\n",
      "INFO:tensorflow:loss = 106.46649, step = 3401 (1.200 sec)\n",
      "INFO:tensorflow:global_step/sec: 82.764\n",
      "INFO:tensorflow:loss = 0.00081940857, step = 3501 (1.208 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.0248\n",
      "INFO:tensorflow:loss = 0.008473145, step = 3601 (1.234 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.8618\n",
      "INFO:tensorflow:loss = 0.0003104708, step = 3701 (1.301 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.1176\n",
      "INFO:tensorflow:loss = 0.00086719077, step = 3801 (1.280 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.0104\n",
      "INFO:tensorflow:loss = 9.338471e-06, step = 3901 (1.234 sec)\n",
      "INFO:tensorflow:global_step/sec: 75.8681\n",
      "INFO:tensorflow:loss = 5.4063683, step = 4001 (1.318 sec)\n",
      "INFO:tensorflow:global_step/sec: 80.6243\n",
      "INFO:tensorflow:loss = 1.5707303e-13, step = 4101 (1.240 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.7192\n",
      "INFO:tensorflow:loss = 4.0480865e-07, step = 4201 (1.304 sec)\n",
      "INFO:tensorflow:global_step/sec: 82.6725\n",
      "INFO:tensorflow:loss = 26.497284, step = 4301 (1.209 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.033\n",
      "INFO:tensorflow:loss = 11.068474, step = 4401 (1.282 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.3893\n",
      "INFO:tensorflow:loss = 4.876019, step = 4501 (1.276 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.1519\n",
      "INFO:tensorflow:loss = 83.62516, step = 4601 (1.313 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.6571\n",
      "INFO:tensorflow:loss = 6.55699e-08, step = 4701 (1.271 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.0426\n",
      "INFO:tensorflow:loss = 4.627993e-09, step = 4801 (1.265 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.0553\n",
      "INFO:tensorflow:loss = 3.0566416e-10, step = 4901 (1.281 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.3991\n",
      "INFO:tensorflow:loss = 1.5344186e-06, step = 5001 (1.276 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.2959\n",
      "INFO:tensorflow:loss = 0.00224662, step = 5101 (1.294 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.1639\n",
      "INFO:tensorflow:loss = 16.882627, step = 5201 (1.296 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.2221\n",
      "INFO:tensorflow:loss = 0.0003800239, step = 5301 (1.279 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.7877\n",
      "INFO:tensorflow:loss = 0.0034767236, step = 5401 (1.269 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.0167\n",
      "INFO:tensorflow:loss = 1.4172622e-07, step = 5501 (1.266 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.2843\n",
      "INFO:tensorflow:loss = 2.6316582e-06, step = 5601 (1.261 sec)\n",
      "INFO:tensorflow:global_step/sec: 81.2734\n",
      "INFO:tensorflow:loss = 1.3261825e-13, step = 5701 (1.230 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.4253\n",
      "INFO:tensorflow:loss = 3.672338e-10, step = 5801 (1.309 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.0782\n",
      "INFO:tensorflow:loss = 8.132034e-06, step = 5901 (1.264 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.7718\n",
      "INFO:tensorflow:loss = 0.0017702074, step = 6001 (1.303 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.005\n",
      "INFO:tensorflow:loss = 45.233635, step = 6101 (1.298 sec)\n",
      "INFO:tensorflow:global_step/sec: 84.0148\n",
      "INFO:tensorflow:loss = 3.484258e-05, step = 6201 (1.190 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.204\n",
      "INFO:tensorflow:loss = 0.00013909479, step = 6301 (1.262 sec)\n",
      "INFO:tensorflow:global_step/sec: 75.0216\n",
      "INFO:tensorflow:loss = 3.2052283e-06, step = 6401 (1.333 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.8597\n",
      "INFO:tensorflow:loss = 1.3317999e-12, step = 6501 (1.269 sec)\n",
      "INFO:tensorflow:global_step/sec: 73.9452\n",
      "INFO:tensorflow:loss = 2.0475562, step = 6601 (1.352 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.5096\n",
      "INFO:tensorflow:loss = 4.5953593, step = 6701 (1.258 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.008\n",
      "INFO:tensorflow:loss = 56.80867, step = 6801 (1.266 sec)\n",
      "INFO:tensorflow:global_step/sec: 76.4509\n",
      "INFO:tensorflow:loss = 6.67816, step = 6901 (1.308 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.5118\n",
      "INFO:tensorflow:loss = 25.065445, step = 7001 (1.290 sec)\n",
      "INFO:tensorflow:global_step/sec: 82.8986\n",
      "INFO:tensorflow:loss = 14.105591, step = 7101 (1.206 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.1033\n",
      "INFO:tensorflow:loss = 45.860817, step = 7201 (1.297 sec)\n",
      "INFO:tensorflow:global_step/sec: 78.9325\n",
      "INFO:tensorflow:loss = 1.2660873e-07, step = 7301 (1.266 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.337\n",
      "INFO:tensorflow:loss = 8.8107965e-11, step = 7401 (1.261 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.4378\n",
      "INFO:tensorflow:loss = 8.031134, step = 7501 (1.259 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 79.2923\n",
      "INFO:tensorflow:loss = 1.3680773e-10, step = 7601 (1.262 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.8704\n",
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      "INFO:tensorflow:loss = 0.00022495192, step = 15601 (1.237 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 82.1077\n",
      "INFO:tensorflow:loss = 8.796061e-06, step = 15701 (1.218 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.0957\n",
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      "INFO:tensorflow:loss = 6.8204737, step = 18501 (1.290 sec)\n",
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      "INFO:tensorflow:loss = 6.3847e-08, step = 19101 (1.268 sec)\n",
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      "INFO:tensorflow:loss = 8.4828216e-08, step = 19201 (1.286 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.9421\n",
      "INFO:tensorflow:loss = 2.004697e-09, step = 19301 (1.283 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.4541\n",
      "INFO:tensorflow:loss = 1.9279037e-06, step = 19401 (1.259 sec)\n",
      "INFO:tensorflow:global_step/sec: 74.989\n",
      "INFO:tensorflow:loss = 0.0133775305, step = 19501 (1.334 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.5498\n",
      "INFO:tensorflow:loss = 0.0145853255, step = 19601 (1.257 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.5647\n",
      "INFO:tensorflow:loss = 0.005009033, step = 19701 (1.257 sec)\n",
      "INFO:tensorflow:global_step/sec: 79.6137\n",
      "INFO:tensorflow:loss = 22.330528, step = 19801 (1.256 sec)\n",
      "INFO:tensorflow:global_step/sec: 77.3401\n",
      "INFO:tensorflow:loss = 0.15021041, step = 19901 (1.293 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 20000 into /home/matz/Desktop/ml_ids/NN_model/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.019135898.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.dnn_linear_combined.DNNLinearCombinedClassifier at 0x7fa51858d320>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NN_model.train(input_fn=get_input_fn(train_ex, n_batch=180), steps=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2019-06-12-07:19:19\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /home/matz/Desktop/ml_ids/NN_model/model.ckpt-20000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Evaluation [1/1]\n",
      "INFO:tensorflow:Finished evaluation at 2019-06-12-07:19:22\n",
      "INFO:tensorflow:Saving dict for global step 20000: accuracy = 0.9952824, accuracy_baseline = 0.7026406, auc = 0.9969023, auc_precision_recall = 0.9957713, average_loss = 0.17119467, global_step = 20000, label/mean = 0.2973594, loss = 5588.479, precision = 0.9942059, prediction/mean = 0.29593167, recall = 0.98990417\n",
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /home/matz/Desktop/ml_ids/NN_model/model.ckpt-20000\n"
     ]
    }
   ],
   "source": [
    "eval_metrics = NN_model.evaluate(input_fn=get_input_fn(validate_ex, n_batch=validate_ex.shape[0], shuffle=False), steps=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9952824\n",
      "accuracy_baseline: 0.7026406\n",
      "auc: 0.9969023\n",
      "auc_precision_recall: 0.9957713\n",
      "average_loss: 0.17119467\n",
      "global_step: 20000\n",
      "label/mean: 0.2973594\n",
      "loss: 5588.479\n",
      "precision: 0.9942059\n",
      "prediction/mean: 0.29593167\n",
      "recall: 0.98990417\n"
     ]
    }
   ],
   "source": [
    "for key,value in sorted(eval_metrics.items()):\n",
    "  print('%s: %s' % (key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /home/matz/Desktop/ml_ids/NN_model/model.ckpt-20000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "results = list(NN_model.predict(input_fn=get_input_fn(test_ex, num_epochs=1, n_batch = test_ex.shape[0], shuffle=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "for el in results:\n",
    "    y_pred.append(el[\"class_ids\"])\n",
    "\n",
    "y_pred_NN = [int(a) for a in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: Wide&Deep    Accuracy: 0.98180, F1-score: 0.99039, Precision: 0.99206, Recall: 0.98872, FPR: 0.14420, FNR: 0.01128\n",
      "TN: 1454; FP: 245; FN:  349; TP: 30597\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "name = \"Wide&Deep\"\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(test_ex[\"attack_flag\"], y_pred_NN)\n",
    "F1S[name] = metrics.f1_score(test_ex[\"attack_flag\"], y_pred_NN)\n",
    "Prec[name] = metrics.precision_score(test_ex[\"attack_flag\"], y_pred_NN)\n",
    "Rec[name] = metrics.recall_score(test_ex[\"attack_flag\"], y_pred_NN)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(test_ex[\"attack_flag\"], y_pred_NN).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4c85a2048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4c85a2e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4c9e00e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4c9d35b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4c9c92e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4c848e5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\", 8, 55)\n",
    "plot_stats(F1S, \"F1-score\", 8, 55)\n",
    "plot_stats(Prec, \"Precision\", 8, 55)\n",
    "plot_stats(Rec, \"Recall\", 8, 55)\n",
    "plot_stats(FPR, \"False Positive Rate\", 8, 55)\n",
    "plot_stats(FNR, \"False Negative Rate\", 8, 55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred[\"RFFM\"] = class_id\n",
    "y_pred[\"Wide&Deep\"] = y_pred_NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metrics of baseline method\n",
    "\n",
    "name = \"baseline\"\n",
    "\n",
    "if(y_test.mean() > (1-y_test.mean())):\n",
    "    y_pred[\"baseline\"] = np.ones(len(y_test)).ravel() \n",
    "else:\n",
    "    y_pred[\"baseline\"] = np.zeros(len(y_test)).ravel()\n",
    "        \n",
    "y_pred[\"baseline\"] = y_pred[\"baseline\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = {\n",
    "               \"AdaBoost\":AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees),\n",
    "               \"DecisionTree\":DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13),\n",
    "               \"NaiveBayes\":GaussianNB(),\n",
    "               \"RandomTree\":BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13), n_estimators=93),\n",
    "               \"RandomForest\":RandomForestClassifier(n_estimators=178, max_depth=31, random_state=0),\n",
    "              }\n",
    "\n",
    "name = \"LogisticReg\"\n",
    "model = LogisticRegression(solver = 'newton-cg').fit(X_train, y_train)\n",
    "y_pred[name] = model.predict(X_test)\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred[name] = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32645\n",
      "32645\n",
      "32645\n"
     ]
    }
   ],
   "source": [
    "print(len(y_pred[\"Wide&Deep\"]))\n",
    "print(len(y_pred[\"RFFM\"]))\n",
    "print(len(y_pred[\"AdaBoost\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4aa804898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_pand = pd.DataFrame.from_dict(y_pred)\n",
    "names = [\"LogisticReg\", \"AdaBoost\", \"DecisionTree\", \"NaiveBayes\", \"RandomTree\", \"RandomForest\", \"RFFM\", \"Wide&Deep\"]\n",
    "correlations = y_pred_pand[names].corr()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(correlations, vmin=0.5, vmax=1)\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,8,1)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(names, rotation='vertical')\n",
    "ax.set_yticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              LogisticReg  AdaBoost  DecisionTree  NaiveBayes  RandomTree  \\\n",
      "LogisticReg      1.000000  0.834581      0.837733    0.654069    0.843351   \n",
      "AdaBoost         0.834581  1.000000      0.968509    0.657696    0.977235   \n",
      "DecisionTree     0.837733  0.968509      1.000000    0.659435    0.986130   \n",
      "NaiveBayes       0.654069  0.657696      0.659435    1.000000    0.664510   \n",
      "RandomTree       0.843351  0.977235      0.986130    0.664510    1.000000   \n",
      "RandomForest     0.836977  0.985521      0.969293    0.662160    0.980853   \n",
      "RFFM             0.738493  0.870604      0.876012    0.598981    0.880592   \n",
      "Wide&Deep       -0.000888 -0.002627     -0.003567   -0.005571   -0.002193   \n",
      "\n",
      "              RandomForest      RFFM  Wide&Deep  \n",
      "LogisticReg       0.836977  0.738493  -0.000888  \n",
      "AdaBoost          0.985521  0.870604  -0.002627  \n",
      "DecisionTree      0.969293  0.876012  -0.003567  \n",
      "NaiveBayes        0.662160  0.598981  -0.005571  \n",
      "RandomTree        0.980853  0.880592  -0.002193  \n",
      "RandomForest      1.000000  0.874851  -0.002673  \n",
      "RFFM              0.874851  1.000000  -0.004811  \n",
      "Wide&Deep        -0.002673 -0.004811   1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "failures = {}\n",
    "failures[\"baseline\"] = []\n",
    "failures[\"LogisticReg\"] = []\n",
    "failures[\"AdaBoost\"] = []\n",
    "failures[\"DecisionTree\"] = []\n",
    "failures[\"NaiveBayes\"] = []\n",
    "failures[\"RandomTree\"] = []\n",
    "failures[\"RandomForest\"] = []\n",
    "failures[\"RFFM\"] = []\n",
    "failures[\"Wide&Deep\"] = []\n",
    "\n",
    "for i in range(0, len(y_test)):\n",
    "    for key in y_pred.keys():\n",
    "        if y_pred[key][i] != y_test[i]:\n",
    "            failures[key].append(10)\n",
    "        else:\n",
    "            failures[key].append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4aa887400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "failures_pand = pd.DataFrame.from_dict(failures)\n",
    "names = [\"baseline\", \"LogisticReg\", \"AdaBoost\", \"DecisionTree\", \"NaiveBayes\", \"RandomTree\", \"RandomForest\", \"RFFM\", \"Wide&Deep\"]\n",
    "correlations = failures_pand[names].corr()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(correlations, vmin=0, vmax=1)\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,9,1)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(names, rotation='vertical')\n",
    "ax.set_yticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              baseline  LogisticReg  AdaBoost  DecisionTree  NaiveBayes  \\\n",
      "baseline      1.000000     0.282582  0.119290      0.085872   -0.027178   \n",
      "LogisticReg   0.282582     1.000000  0.583391      0.585238    0.363861   \n",
      "AdaBoost      0.119290     0.583391  1.000000      0.859969    0.328321   \n",
      "DecisionTree  0.085872     0.585238  0.859969      1.000000    0.333787   \n",
      "NaiveBayes   -0.027178     0.363861  0.328321      0.333787    1.000000   \n",
      "RandomTree    0.089555     0.604095  0.896990      0.935902    0.345842   \n",
      "RandomForest  0.118758     0.590448  0.935771      0.859337    0.338354   \n",
      "RFFM          0.225481     0.254377  0.309344      0.315582    0.114599   \n",
      "Wide&Deep     0.656258     0.188721  0.081532      0.058196   -0.020059   \n",
      "\n",
      "              RandomTree  RandomForest      RFFM  Wide&Deep  \n",
      "baseline        0.089555      0.118758  0.225481   0.656258  \n",
      "LogisticReg     0.604095      0.590448  0.254377   0.188721  \n",
      "AdaBoost        0.896990      0.935771  0.309344   0.081532  \n",
      "DecisionTree    0.935902      0.859337  0.315582   0.058196  \n",
      "NaiveBayes      0.345842      0.338354  0.114599  -0.020059  \n",
      "RandomTree      1.000000      0.909759  0.313286   0.062999  \n",
      "RandomForest    0.909759      1.000000  0.302787   0.081266  \n",
      "RFFM            0.313286      0.302787  1.000000   0.148319  \n",
      "Wide&Deep       0.062999      0.081266  0.148319   1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 30946, 10: 1699})\n",
      "Counter({1: 31772, 10: 873})\n",
      "Counter({1: 32233, 10: 412})\n",
      "Counter({1: 32240, 10: 405})\n",
      "Counter({1: 30280, 10: 2365})\n",
      "Counter({1: 32268, 10: 377})\n",
      "Counter({1: 32260, 10: 385})\n",
      "Counter({1: 32434, 10: 211})\n",
      "Counter({1: 29313, 10: 3332})\n",
      "[[30946.0, 31772.0, 32233.0, 32240.0, 30280.0, 32268.0, 32260.0, 32434.0, 29313.0], [1699.0, 873.0, 412.0, 405.0, 2365.0, 377.0, 385.0, 211.0, 3332.0]]\n"
     ]
    }
   ],
   "source": [
    "cont_table = [[],[]]\n",
    "for name in failures.keys():\n",
    "    print(Counter(failures[name]))\n",
    "    cont_table[0].append(float(Counter(failures[name])[1]))\n",
    "    cont_table[1].append(float(Counter(failures[name])[10]))\n",
    "print(cont_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability=0.950, critical=15.507, stat=9051.514\n",
      "\n",
      "reject H0 - results dependent\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# test of independence\n",
    "# H0 : results are independent\n",
    "\n",
    "stat, p, dof, expected = stats.chi2_contingency(cont_table)\n",
    "stats.chi2_contingency(cont_table)\n",
    "prob = 0.95\n",
    "critical = stats.chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f' % (prob, critical, stat))\n",
    "\n",
    "if abs(stat) >= critical:\n",
    "    print('\\nreject H0 - results dependent')\n",
    "else:\n",
    "    print('\\nfailed to reject H0 - results independent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New data prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({-1: 210159, 1: 2830})\n"
     ]
    }
   ],
   "source": [
    "test_set = pd.read_csv(\"20151221.txt\", sep = \"\\t\", header = None)\n",
    "test_set.columns = [\"conn len\", \"service\", \"src bytes\", \"dst bytes\", \"conn numb\", \"Same srv rate\", \"Serror rate\", \"Srv serror rate\", \"Dst host count\", \"Dst host srv count\", \"Dst host same src port rate\", \"Dst host serror rate\", \"Dst host srv serror rate\", \"Conn state\", \"IDS detection\", \"Malware detection\", \"Ashula detection\", \"attack_flag\", \"src IP\", \"src port\", \"dst IP\", \"dst port\", \"start time\", \"proto\"]\n",
    "\n",
    "print(Counter(test_set[\"attack_flag\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 210159, 0: 2830})\n"
     ]
    }
   ],
   "source": [
    "test_set.loc[:, \"attack_flag\"] = test_set.loc[:,\"attack_flag\"].replace(-2, -1)\n",
    "test_set.loc[:, \"attack_flag\"] = test_set.loc[:, \"attack_flag\"] * -1\n",
    "test_set.loc[:, \"attack_flag\"] = test_set.loc[:,\"attack_flag\"].replace(-1, 0)\n",
    "\n",
    "print(Counter(test_set[\"attack_flag\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212989, 24)\n",
      "(212989, 21)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    210159\n",
       "0      2830\n",
       "Name: attack_flag, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice dataset, drop additional features\n",
    "print(test_set.shape)\n",
    "test_set = test_set.drop(['start time', 'dst IP', 'src IP'], axis=1)\n",
    "print(test_set.shape)\n",
    "\n",
    "# drop software markers\n",
    "test_set = test_set.drop([\"IDS detection\", \"Malware detection\", \"Ashula detection\"], axis=1)\n",
    "test_set[\"attack_flag\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212989, 18)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing NaNs\n",
    "test_set = test_set.dropna()\n",
    "test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set[\"well-known src pool\"] = test_set.apply(wk, axis=1)\n",
    "test_set[\"registered src pool\"] = test_set.apply(reg, axis=1)\n",
    "test_set[\"cli src pool\"] = test_set.apply(cli, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set[\"src port\"] = test_set[\"src port\"].apply(str)\n",
    "test_set[\"dst port\"] = test_set[\"dst port\"].apply(str)\n",
    "test_set['src port'].nunique()\n",
    "test_set['dst port'].nunique()\n",
    "\n",
    "thresh1 = 125\n",
    "thresh2 = 125\n",
    "counts1 = test_set['src port'].value_counts()\n",
    "counts2 = test_set['dst port'].value_counts()\n",
    "\n",
    "repl1 = counts1[counts1 <= thresh1].index\n",
    "repl2 = counts2[counts2 <= thresh2].index\n",
    "\n",
    "#hot one encoding for src port numbers\n",
    "test_set = pd.concat([test_set, pd.get_dummies(test_set[\"src port\"].replace(repl1, 'other_src'), prefix=\"is_src\")], axis=1)\n",
    "\n",
    "#hot one encoding for dst port numbers\n",
    "test_set = pd.concat([test_set, pd.get_dummies(test_set[\"dst port\"].replace(repl2, 'other_dst'), prefix=\"is_dst\")], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>conn len</th>\n",
       "      <th>service</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
       "      <th>Serror rate</th>\n",
       "      <th>Srv serror rate</th>\n",
       "      <th>Dst host count</th>\n",
       "      <th>Dst host srv count</th>\n",
       "      <th>...</th>\n",
       "      <th>state_OTH</th>\n",
       "      <th>state_REJ</th>\n",
       "      <th>state_RSTO</th>\n",
       "      <th>state_RSTOS0</th>\n",
       "      <th>state_RSTR</th>\n",
       "      <th>state_RSTRH</th>\n",
       "      <th>state_S0</th>\n",
       "      <th>state_S1</th>\n",
       "      <th>state_SF</th>\n",
       "      <th>state_SHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>212979</th>\n",
       "      <td>0.000061</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212980</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212981</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.26</td>\n",
       "      <td>6</td>\n",
       "      <td>84</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212982</th>\n",
       "      <td>0.000029</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212983</th>\n",
       "      <td>0.000039</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212984</th>\n",
       "      <td>0.000045</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212985</th>\n",
       "      <td>0.000053</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.22</td>\n",
       "      <td>19</td>\n",
       "      <td>84</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212986</th>\n",
       "      <td>0.000056</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.21</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212987</th>\n",
       "      <td>0.000058</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.20</td>\n",
       "      <td>20</td>\n",
       "      <td>84</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212988</th>\n",
       "      <td>0.000110</td>\n",
       "      <td>dns</td>\n",
       "      <td>36</td>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        conn len service  src bytes  dst bytes  conn numb  Same srv rate  \\\n",
       "212979  0.000061   other          0          0         20            1.0   \n",
       "212980  0.000000   other          0          0          1            1.0   \n",
       "212981  0.000013   other          0          0         13            1.0   \n",
       "212982  0.000029   other          0          0          2            1.0   \n",
       "212983  0.000039   other          0          0         14            1.0   \n",
       "212984  0.000045   other          0          0         15            1.0   \n",
       "212985  0.000053   other          0          0         16            1.0   \n",
       "212986  0.000056   other          0          0         17            1.0   \n",
       "212987  0.000058   other          0          0         18            1.0   \n",
       "212988  0.000110     dns         36        194          1            1.0   \n",
       "\n",
       "        Serror rate  Srv serror rate  Dst host count  Dst host srv count  \\\n",
       "212979         0.05             0.15               2                  85   \n",
       "212980         0.00             0.22               4                   6   \n",
       "212981         0.08             0.26               6                  84   \n",
       "212982         0.50             0.25               1                   6   \n",
       "212983         0.07             0.24               1                  84   \n",
       "212984         0.07             0.23               3                  84   \n",
       "212985         0.06             0.22              19                  84   \n",
       "212986         0.06             0.21               7                  84   \n",
       "212987         0.06             0.20              20                  84   \n",
       "212988         0.00             0.50               0                  89   \n",
       "\n",
       "          ...      state_OTH  state_REJ  state_RSTO state_RSTOS0  state_RSTR  \\\n",
       "212979    ...              0          1           0            0           0   \n",
       "212980    ...              0          0           0            0           0   \n",
       "212981    ...              0          1           0            0           0   \n",
       "212982    ...              0          1           0            0           0   \n",
       "212983    ...              0          1           0            0           0   \n",
       "212984    ...              0          1           0            0           0   \n",
       "212985    ...              0          1           0            0           0   \n",
       "212986    ...              0          1           0            0           0   \n",
       "212987    ...              0          1           0            0           0   \n",
       "212988    ...              0          0           0            0           0   \n",
       "\n",
       "       state_RSTRH state_S0 state_S1  state_SF  state_SHR  \n",
       "212979           0        0        0         0          0  \n",
       "212980           0        1        0         0          0  \n",
       "212981           0        0        0         0          0  \n",
       "212982           0        0        0         0          0  \n",
       "212983           0        0        0         0          0  \n",
       "212984           0        0        0         0          0  \n",
       "212985           0        0        0         0          0  \n",
       "212986           0        0        0         0          0  \n",
       "212987           0        0        0         0          0  \n",
       "212988           0        0        0         1          0  \n",
       "\n",
       "[10 rows x 193 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### hot one encoding ###\n",
    "\n",
    "# hot one encoding for service\n",
    "test_set = pd.concat([test_set, pd.get_dummies(test_set[\"service\"], prefix=\"is\")], axis=1)\n",
    "test_set.tail(10)\n",
    "\n",
    "#hot one encoding for proto - commented due to reduced column number\n",
    "test_set = pd.concat([test_set, pd.get_dummies(test_set[\"proto\"], prefix=\"is\")], axis=1)\n",
    "test_set.tail(10)\n",
    "\n",
    "# hot one encoding for conn state\n",
    "test_set = pd.concat([test_set, pd.get_dummies(test_set[\"Conn state\"], prefix=\"state\")], axis=1)\n",
    "test_set.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set.rename(columns={'Dst host count': 'Dst_host_count', \"Dst host srv count\" : \"Dst_host_srv_count\",\n",
    "                    \"Dst host same src port rate\" : \"Dst_host_same_src_port_rate\",\n",
    "                    \"Dst host serror rate\" : \"Dst_host_serror_rate\",\n",
    "                    \"Dst host srv serror rate\" : \"Dst_host_srv_serror_rate\",\n",
    "                    \"Same srv rate\" : \"Same_srv_rate\",\n",
    "                    \"Srv serror rate\" : \"Srv_serror_rate\",\n",
    "                    \"conn len\" : \"conn_len\",\n",
    "                    \"src bytes\" : \"src_bytes\",\n",
    "                    \"dst bytes\" : \"dst_bytes\",\n",
    "                    \"conn numb\" : \"conn_numb\",\n",
    "                    \"Serror rate\" : \"Serror_rate\",\n",
    "                    \"cli src pool\" : \"cli_src_pool\",\n",
    "                    \"registered src pool\" : \"registered_src_pool\",\n",
    "                    \"well-known src pool\" : \"well_known_src_pool\",\n",
    "                    \"dst port\" : \"dst_port\",\n",
    "                    \"src port\" : \"src_port\",\n",
    "                     \"Conn state\" : \"Conn_state\" \n",
    "                    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['is_dns,sip', 'is_dst_102', 'is_dst_10200', 'is_dst_1081', 'is_dst_119',\n",
      "       'is_dst_135', 'is_dst_143', 'is_dst_223', 'is_dst_389', 'is_dst_5432',\n",
      "       'is_dst_6050', 'is_dst_631', 'is_dst_82', 'is_dst_88', 'is_dst_9080',\n",
      "       'is_src_0', 'is_src_13941', 'is_src_18382', 'is_src_21320',\n",
      "       'is_src_22055', 'is_src_22206', 'is_src_22207', 'is_src_2295',\n",
      "       'is_src_29174', 'is_src_300', 'is_src_37286', 'is_src_37396',\n",
      "       'is_src_37511', 'is_src_38396', 'is_src_40082', 'is_src_40718',\n",
      "       'is_src_41161', 'is_src_41287', 'is_src_41708', 'is_src_41793',\n",
      "       'is_src_42206', 'is_src_443', 'is_src_4445', 'is_src_46432',\n",
      "       'is_src_49354', 'is_src_49362', 'is_src_49376', 'is_src_49414',\n",
      "       'is_src_5061', 'is_src_5071', 'is_src_5094', 'is_src_5100',\n",
      "       'is_src_5114', 'is_src_51361', 'is_src_5187', 'is_src_5208',\n",
      "       'is_src_5211', 'is_src_52131', 'is_src_5229', 'is_src_5247',\n",
      "       'is_src_53265', 'is_src_53451', 'is_src_53762', 'is_src_54110',\n",
      "       'is_src_54334', 'is_src_54499', 'is_src_54509', 'is_src_5497',\n",
      "       'is_src_5501', 'is_src_56118', 'is_src_5844', 'is_src_58663',\n",
      "       'is_src_60000', 'is_src_6044', 'is_src_61605', 'is_src_6173',\n",
      "       'is_src_62029', 'is_src_63265', 'is_src_63432', 'is_src_64486'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(212989, 193)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check differences between datasets after hot one encoding\n",
    "\n",
    "test_new_data_full = test_set\n",
    "    \n",
    "print(test_set.columns.difference(data.columns))  \n",
    "test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_new_data = test_set.loc[range(30000), :]\n",
    "\n",
    "for c in data.columns.difference(test_new_data.columns):\n",
    "    test_new_data[c] = 0\n",
    "for c in test_new_data.columns.difference(data.columns):\n",
    "    test_new_data = test_new_data.drop(c, axis=1)\n",
    "\n",
    "test_new_data = test_new_data.drop([\"service\", \"Conn_state\", \"proto\", \"src_port\", \"dst_port\"], axis=1)\n",
    "    \n",
    "test_new_data = test_new_data.sample(frac=1)\n",
    "y_test_new_data = test_new_data.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_test_new_data = test_new_data.drop([\"attack_flag\"], axis=1).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 232)\n",
      "(32645, 232)\n",
      "1    29498\n",
      "0      502\n",
      "Name: attack_flag, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_test_new_data.shape)\n",
    "print(X_test.shape)\n",
    "print(test_new_data[\"attack_flag\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: baseline     Accuracy: 0.98327, F1-score: 0.99156, Precision: 0.98327, Recall: 1.00000, FPR: 1.00000, FNR: 0.00000\n",
      "TN:   0; FP: 502; FN:    0; TP: 29498\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Metrics of baseline method\n",
    "from sklearn import metrics\n",
    "\n",
    "name = \"baseline\"\n",
    "\n",
    "if(y_test_new_data.mean() > (1-y_test_new_data.mean())):\n",
    "    y_pred = np.ones(len(y_test_new_data)).ravel() \n",
    "else:\n",
    "    y_pred = np.zeros(len(y_test_new_data)).ravel() \n",
    "    \n",
    "Acc[name] = metrics.accuracy_score(y_test_new_data, y_pred)\n",
    "F1S[name] = metrics.f1_score(y_test_new_data, y_pred)\n",
    "Prec[name] = metrics.precision_score(y_test_new_data, y_pred)\n",
    "Rec[name] = metrics.recall_score(y_test_new_data, y_pred)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test_new_data, y_pred).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: LogisticReg  Accuracy: 0.98183, F1-score: 0.99083, Precision: 0.98324, Recall: 0.99854, FPR: 1.00000, FNR: 0.00146\n",
      "TN:   0; FP: 502; FN:   43; TP: 29455\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "clf = LogisticRegression(solver = 'newton-cg')\n",
    "\n",
    "model = clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test_new_data)\n",
    "\n",
    "name = \"LogisticReg\"\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(y_test_new_data, y_pred)\n",
    "F1S[name] = metrics.f1_score(y_test_new_data, y_pred)\n",
    "Prec[name] = metrics.precision_score(y_test_new_data, y_pred)\n",
    "Rec[name] = metrics.recall_score(y_test_new_data, y_pred)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test_new_data, y_pred).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: AdaBoost     Accuracy: 0.98017, F1-score: 0.98998, Precision: 0.98331, Recall: 0.99675, FPR: 0.99402, FNR: 0.00325\n",
      "TN:   3; FP: 499; FN:   96; TP: 29402\n",
      "\n",
      "Algorithm: DecisionTree Accuracy: 0.98797, F1-score: 0.99388, Precision: 0.99363, Recall: 0.99414, FPR: 0.37450, FNR: 0.00586\n",
      "TN: 314; FP: 188; FN:  173; TP: 29325\n",
      "\n",
      "Algorithm: NaiveBayes   Accuracy: 0.98320, F1-score: 0.99153, Precision: 0.98327, Recall: 0.99993, FPR: 1.00000, FNR: 0.00007\n",
      "TN:   0; FP: 502; FN:    2; TP: 29496\n",
      "\n",
      "Algorithm: RandomTree   Accuracy: 0.99003, F1-score: 0.99495, Precision: 0.99221, Recall: 0.99769, FPR: 0.46016, FNR: 0.00231\n",
      "TN: 271; FP: 231; FN:   68; TP: 29430\n",
      "\n",
      "Algorithm: RandomForest Accuracy: 0.95563, F1-score: 0.97731, Precision: 0.98279, Recall: 0.97190, FPR: 1.00000, FNR: 0.02810\n",
      "TN:   0; FP: 502; FN:  829; TP: 28669\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "seed = 12\n",
    "num_trees = 28\n",
    "\n",
    "classifiers = {\n",
    "               \"AdaBoost\":AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees),\n",
    "               \"DecisionTree\":DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13),\n",
    "               \"NaiveBayes\":GaussianNB(),\n",
    "               \"RandomTree\":BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13), n_estimators=93),\n",
    "               \"RandomForest\":RandomForestClassifier(n_estimators=178, max_depth=31, random_state=0),\n",
    "              }\n",
    "\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred = model.predict(X_test_new_data)\n",
    "    \n",
    "    Acc[name] = metrics.accuracy_score(y_test_new_data, y_pred)\n",
    "    F1S[name] = metrics.f1_score(y_test_new_data, y_pred)\n",
    "    Prec[name] = metrics.precision_score(y_test_new_data, y_pred)\n",
    "    Rec[name] = metrics.recall_score(y_test_new_data, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_test_new_data, y_pred).ravel()\n",
    "    FPR[name] = fp/(fp+tn)\n",
    "    FNR[name] = fn/(tp+fn)\n",
    "\n",
    "    print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "    print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"features\": X_test_new_data.values},\n",
    "    y=y_test_new_data,\n",
    "    batch_size=X_test_new_data.shape[0],\n",
    "    num_epochs=1,\n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from kernel_train/model.ckpt-8000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "pred_iterator = estimator_kernel.predict(input_fn = test_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_id = []\n",
    "\n",
    "for pred_dict in pred_iterator:\n",
    "    class_id.append(pred_dict['classes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: RFFM         Accuracy: 0.99007, F1-score: 0.99496, Precision: 0.99235, Recall: 0.99759, FPR: 0.45219, FNR: 0.00241\n",
      "TN: 275; FP: 227; FN:   71; TP: 29427\n",
      "\n"
     ]
    }
   ],
   "source": [
    "name = \"RFFM\"\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(y_test_new_data, class_id)\n",
    "F1S[name] = metrics.f1_score(y_test_new_data, class_id)\n",
    "Prec[name] = metrics.precision_score(y_test_new_data, class_id)\n",
    "Rec[name] = metrics.recall_score(y_test_new_data, class_id)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test_new_data, class_id).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /home/matz/Desktop/ml_ids/NN_model/model.ckpt-20000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "results = list(NN_model.predict(input_fn=get_input_fn(test_new_data_full.loc[range(30000), :], num_epochs=1, n_batch = X_test.shape[0], shuffle=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "for el in results:\n",
    "    y_pred.append(el[\"class_ids\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: Wide&Deep    Accuracy: 0.96373, F1-score: 0.98152, Precision: 0.98346, Recall: 0.97959, FPR: 0.96813, FNR: 0.02041\n",
      "TN:  16; FP: 486; FN:  602; TP: 28896\n",
      "\n"
     ]
    }
   ],
   "source": [
    "name = \"Wide&Deep\"\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(y_test_new_data, y_pred)\n",
    "F1S[name] = metrics.f1_score(y_test_new_data, y_pred)\n",
    "Prec[name] = metrics.precision_score(y_test_new_data, y_pred)\n",
    "Rec[name] = metrics.recall_score(y_test_new_data, y_pred)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test_new_data, y_pred).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "FNR[name] = fn/(tp+fn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}, FNR: {6:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name], FNR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4ab7cd518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A5iuZXK666iqGDx9O586dGT58OI0bN6ZmzZr07t2bvn37st122zFw4EC6d+++xD668847mTZtGm3atCn+QD/wwAMcfvjhTJ06laFDh3LIIYewaNEi3n33XWrUqMG0adOYOHEiV199NRMmTKiS/V2ZxzfAwoUL6dSpExtttBG77bYbXbt2LfM1qmJ/T5s2jYcffrj4BKgirwXw+eefM3LkSO6//35OOeUUPvvss2XugxVNFq+88krxl36nTp1Ya621ik8Idtxxx+Llm2yyCfvuG/dzefLJJykqKqJTp0506dKFN954o/j39OnTh/r167PXXnstM+as7PMFCxbw+uuvc9VVV/Hee+8xYcIE7rrrrjLjXrhwISeccALPPfcc48aN44EHHmDcuHFLbHP66adz6KGHMmbMGM477zzOPvtsAN58801GjBjBmDFj+PDDD3nvvfcYPjxuMLf33nvz7rvvlvo7DzroIEaPHs3o0aM5+uijgbhQueeeexg7dizPP/88p5xyCrNmzSp7h1ehVSI5V+WXbmm22morJk+ezAcffMBJJ51U/AFbsGAB77//PscffzyjRo2ibt26pX6oqzLu8l6zrGQB0LVrV8aOHct7773H4MGDmTt3bpXEPX78eD766COmTp3KF198wbBhw3jttddYf/31ufnmmznooIPYcccdadasWfHfYXlfqyxNmjRZIjFNnTq1uPUgZ5NNNuGxxx5j1KhRXHLJJQCst956QJyFjx49mhdffBF3p1WrVkv83xo1anDQQQcVJ8k77riDAw88EIDu3bszd+5cZsyYwf3330+fPn2oVasWG220Edtvvz0jR46skv1dmcd37j2OHj2aqVOn8u677/Lhhx+W+TpVsb9HjRrF+PHjadmyJc2aNeOnn36iZcuWy3yt3O9t3rw5PXr0YNSoUeXug1+TLHr27Fn8pT9s2DDq1KlD7969AXj99deL13Xv3p39998fgF69evHBBx8wevRo/vWvfxUnC4BBgwZx7733lhtv1vZ5kyZN6Ny5M82bN6dmzZrsu+++vP/++2XGXRWtcQDdunVb6mq/PFtssUXx53qTTTZho402Yvr06RX+/5VplUjOVf2lW9K6665b3EzSt29f5s+fz4wZM2jSpAlNmjQpvpro379/uQdkVcTdoEEDZs2axYIFC5Z6zbKSRb42bdpQt27dKvvSffzxx+nWrRv16tWjXr167LHHHrz99ttAnOW+8847vPXWW7Ru3XqJD8nyvlZpttlmGz799FMmTpzIvHnzGDJkCP369VtimxkzZhSfsAwePJgjjzwSiC/rXBPpmDFjGDNmDL1798bdGT9+PBBJ9Omnn2bLLbcEYNNNN+Xll18G4KOPPmLu3Lk0bNiQTTfdlGHDhuHuzJkzh7fffrv4/1T2/obKO77z1a9fnx49ehQ3kZemKvb3nnvuyVdffcWkSZOYNGkSderUKd7/Zb3WzJkz+eWXX4q3GTFiBG3bti13H/yaZJHvkUceYY899qBOnTpLLJ89ezbDhg0rPvGpV69e8QnXnDlzljj56tWrF+ussw4VkZV9vs022zBz5szixDZs2LBy93lVtMYty6OPPkpRURH9+/cvtTXp3XffZd68ebRo0WKZr1UlltUpDfyLuCnAh2WsN+A6YuL9McBWFensrsyCsPnz5/vmm2/uEyZMKC6Y+fDDD5fYZvr06b5w4UJ3d//LX/7i5557rrtHocOMGTPc3f2DDz7wdu3aLVUMVbJg5ssvvyyuCn7nnXe8adOmxc932GEH//jjj93d/fzzz/fTTz99pcfdv3//JQrCbrzxRnd379Onj995553u7j5u3Dhv1KiRL1q0yCdMmFD8fydNmuSNGjVa4v1WZtxDhgzxXr16+fz5833evHm+yy67+FNPPeXu7l9//bW7u3/33XfesWNH/+STT1b4tcry7LPPeqtWrbx58+Z+8cUXu7v7ueee608++aS7uz/88MPesmVLb9WqlR911FE+d+5cd49K5TZt2nibNm28a9euPmrUKHd3X7hwoW+33Xbevn17b9eunf/ud78rLrgaO3asb7fddl5UVOQdO3b0F154wd2jcrV///7etm1bb9OmjV9xxRXlxpyV4/ubb74pLnD86aeffIcddvCnn356pe7vkvKLk8p6rREjRnj79u29qKjI27dv77fffnu5Mede66ijjip+fs899/gJJ5ywxDYDBw70a6+91t3dH330UQeK93VOz549S91Hd999tx9wwAFLLHvssce8devWvv766y81yqO8ItSSsrDP3d3/85//eIcOHbx9+/Z+2GGHFY8aKc1DDz201P4+8cQTl9jmiy++8P322887derkJ598sjdu3NhnzZrln376qfft29dnz57ts2fP9m7duvnw4cPLjNndfcaMGcWx3nzzzd6zZ88l1k+bNs232GKLcgseV1DlVWsTd/vZqpzk3Je49Z4B3Yhb663U5OxeNQfkP/7xD2/cuLHXqFHDGzVqVHzwXH/99d62bVsvKiryrl27+ogRI4r/z6hRo3zrrbf2Dh06+D777OPffffdSo/7s88+82222cZbtGjh/fv3L/4/ZSWLe+65x9u2besdO3b0zp07++OPP15l+3vBggV+7LHH+pZbbult2rTxP//5z8WvOWDAgOL3lDu5WNHXWtVk4fj+4IMPiofhtWvXbokhhKuaX5MscqZNm+YNGjTwefPmLfX6ffr08UceeaTU3z18+HDv1avXEsuWJzlXRxWpMs83e/Zsb9y4sbtHlflFF11UvO7CCy/0yy+/fInty6vWX7Bgga+77rrFz7///nvv3LmzP/TQQ8v9PiqgcodSEbdrKys5/xMYmPf8E6DRsl6zspOziEhl+TXJIufaa6/1Y445ZqltZ8yY4RtssIH//PPPZb5es2bNKjx8c1VQVa1xOSWT87Rp04ofP/bYY961a1d3j+Gvu+yyi19zzTWV/h6TlTqUqjFx39ecqWnZUszsWDMbaWYjC9XJLiKyLL+m7zbngQceYODAgUu99sMPP8xee+3FWmutVbxs/PjxxYV/77//PvPmzWPDDTes7LeVWTVr1uSGG24o7i8+8MADadeuHeeddx5PPfUUAK+++iqtW7dmiy224Ouvv+acc84BoranRYsWdOjQgY4dO9KxY0f23ntvIIZYNWnShJ9++okmTZpwwQUXAHDdddfRrl07OnbsyHXXXVdcSf7QQw/x2muvcddddxVX1Y8ePXql7w+o4I0vzKwZ8Iy7ty9l3bPAYHd/Iz1/GTjD3f9b3mt26dLFy6tQXV7Nznq20l6rsky6bM9lblNd467Oqus+V9yVpyJxDx06lFNOOYWFCxdy5JFHcs4553DeeefRpUsX+vXrxyOPPMLZZ5+NmbHTTjtx4403suaaa8brT5rE9ttvz5QpU1hjjSWvgXr06MFZZ51Fnz59ipddfvnl3HPPPdSqVYu1116bK6+8kh122AGI4Vcff/wxP/74IxtuuCF33HEHu+++e5lxV9f9vZoofxKJPOWPqaiYqUDTvOdNgGmV8LqScVn7EtAXgFSmvn370rdv3yWWXXTRRcWP+/fvXzzZT0nNmjVbqto459VXX11q2ZlnnsmZZ55Z6vavv/56BSOWVUllJOengBPNbAjQFfje3b+shNcVEZHVRNZO9qGwJ/zLTM5m9gDQA2hgZlOB84FaAO5+CzCUqNgeD/wEHFFVwYqIrCxKFlJIy0zO7r50RcOS6x04odIiEhERWc2tEjOEiYiIrEqUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDKmQsnZzPqY2SdmNt7Mzipl/aZm9oqZjTKzMWbWt/JDFRERWT0sMzmbWQ3gRmAPoC0w0Mzaltjsr8BD7t4ZGADcVNmBioiIrC4qcuW8LTDe3Se4+zxgCLBPiW0cWDc9Xg+YVnkhioiIrF4qkpwbA1Pynk9Ny/JdABxsZlOBocBJpb2QmR1rZiPNbOT06dNXIFwREZFVX0WSs5WyzEs8Hwjc5e5NgL7AvWa21Gu7+63u3sXduzRs2HD5oxUREVkNVCQ5TwWa5j1vwtLN1kcBDwG4+1vAWkCDyghQRERkdVOR5Pwe0MrMNjez2kTB11Mltvkc6AVgZm2I5Kx2axERkRWwzOTs7guAE4EXgI+IquyxZnaRmfVLm50GHGNmHwAPAIe7e8mmbxEREamAmhXZyN2HEoVe+cvOy3s8Dti+ckMTERFZPWmGMBERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMqVByNrM+ZvaJmY03s7PK2OZAMxtnZmPN7P7KDVNERGT1UXNZG5hZDeBGYDdgKvCemT3l7uPytmkFnA1s7+4zzWyjqgpYRERkVVeRK+dtgfHuPsHd5wFDgH1KbHMMcKO7zwRw928qN0wREZHVR0WSc2NgSt7zqWlZvi2ALcxshJm9bWZ9SnshMzvWzEaa2cjp06evWMQiIiKruIokZytlmZd4XhNoBfQABgK3m1n9pf6T+63u3sXduzRs2HB5YxUREVktVCQ5TwWa5j1vAkwrZZsn3X2+u08EPiGStYiIiCyniiTn94BWZra5mdUGBgBPldjmCaAngJk1IJq5J1RmoCIiIquLZSZnd18AnAi8AHwEPOTuY83sIjPrlzZ7AfjWzMYBrwCD3P3bqgpaRERkVbbMoVQA7j4UGFpi2Xl5jx04Nf2IiIjIr6AZwkRERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjlJxFREQyRslZREQkY5ScRUREMkbJWUREJGOUnEVERDJGyVlERCRjKpSczayPmX1iZuPN7KxytutvZm5mXSovRBERkdXLMpOzmdUAbgT2ANoCA82sbSnbrQOcDLxT2UGKiIisTipy5bwtMN7dJ7j7PGAIsE8p2/0NuAKYW4nxiYiIrHYqkpwbA1Pynk9Ny4qZWWegqbs/U94LmdmxZjbSzEZOnz59uYMVERFZHVQkOVspy7x4pdkawDXAact6IXe/1d27uHuXhg0bVjxKERGR1UhFkvNUoGne8ybAtLzn6wDtgVfNbBLQDXhKRWEiIiIrpiLJ+T2glZltbma1gQHAU7mV7v69uzdw92bu3gx4G+jn7iOrJGIREZFV3DKTs7svAE4EXgA+Ah5y97FmdpGZ9avqAEVERFY3NSuykbsPBYaWWHZeGdv2+PVhiYiIrL40Q5iIiEjGKDmLiIhkjJKziIhIxig5i4iIZIySs4iISMYoOYuIiGSMkrOIiEjGKDmLiIhkjJKziIhIxig5i4iIZIySs4iISMYoOYuIiGSMkrOIiEjGKDmLiIhkjJKziIhIxig5i4iIZIySs4iISMYoOYuIiGSMkrOIiEjGKDmLiIhkjJKziIhIxig5i4iIZIySs4iISMYoOYuIiGSMkrOIiEjGKDmLiIhkjJKziIhIxig5i4iIZIySs4ik8EYqAAAgAElEQVSISMYoOYuIiGSMkrOIiEjGKDmLiIhkjJKziIhIxlQoOZtZHzP7xMzGm9lZpaw/1czGmdkYM3vZzDar/FBFRERWD8tMzmZWA7gR2ANoCww0s7YlNhsFdHH3IuAR4IrKDlRERGR1UZEr522B8e4+wd3nAUOAffI3cPdX3P2n9PRtoEnlhikiIrL6qEhybgxMyXs+NS0ry1HAc6WtMLNjzWykmY2cPn16xaMUERFZjVQkOVspy7zUDc0OBroAV5a23t1vdfcu7t6lYcOGFY9SRERkNVKzAttMBZrmPW8CTCu5kZntCpwD7Ozuv1ROeCIiIqufilw5vwe0MrPNzaw2MAB4Kn8DM+sM/BPo5+7fVH6YIiIiq49lJmd3XwCcCLwAfAQ85O5jzewiM+uXNrsSqAc8bGajzeypMl5ORERElqEizdq4+1BgaIll5+U93rWS4xIREVltaYYwERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYxRchYREckYJWcREZGMUXIWERHJGCVnERGRjFFyFhERyRglZxERkYypUHI2sz5m9omZjTezs0pZv6aZPZjWv2NmzSo7UBERkdXFMpOzmdUAbgT2ANoCA82sbYnNjgJmuntL4Brg8soOVEREZHVRkSvnbYHx7j7B3ecBQ4B9SmyzD3B3evwI0MvMrPLCFBERWX2Yu5e/gVl/oI+7H52eHwJ0dfcT87b5MG0zNT3/LG0zo8RrHQscm562Bj6prDdSyRoAM5a5VfYo7pVLca9c1TVuqL6xK+7KNcPd+1Rkw5oV2Ka0K+CSGb0i2+DutwK3VuB3FpSZjXT3LoWOY3kp7pVLca9c1TVuqL6xK+7CqUiz9lSgad7zJsC0srYxs5rAesB3lRGgiIjI6qYiyfk9oJWZbW5mtYEBwFMltnkKOCw97g8M82W1l4uIiEipltms7e4LzOxE4AWgBvAvdx9rZhcBI939KeAO4F4zG09cMQ+oyqBXgsw3vZdBca9cinvlqq5xQ/WNXXEXyDILwkRERGTl0gxhIiIiGaPkLCIikjFKziIiIhmj5CyZppnmRArLzLY0s9/nPV8lP5NZe19Kzssha3+8FWVm1envnhs/f7iZdSp0ML9WdTyG0vz61VLuWDezdcys6bK2zxozWzPvcYc0j8TKdiYxGuciM1tvVRomm39s57+vLHxHqlq7gsxsDXdflD4sHYFZwGx3/7LAoa0wMzsV+MHdby90LKUxs82JedsbAQcC25ScEra6MrP2wBziMzih0PGUJe+4rwX8BRgMzK8OX9B5sbcErgVqEzMXHl5dPrdmtj2wAbArsMjd/2xmtjL3v5ntAfwdWBuYDpzj7v9J61ZqLJUpF3s64TkNWAT84u7X5a8vVHyFOAurltx9UXp4J/Aj0Bl41Mwur04Hp5nVcPeFZtaNuKlJKzPrAFyZmxs9K9x9oplNBM4DXgMamtlCd59Z4NBWSF6yOBrYG/geWGhmp7l7JmfUyzvubyKSw7z0ZbaggGFVSF7sVwLXAWsC57v7l2ZWO93IJ7PS1dsawNFAN+APsPgKb2UlD3d/zswc2IiY6+JGM3sCOMvdF1b1768qefvuamBz4D9ATzPbCzjG3ScXLDjUrL1c0k1Aarn7sUA94J105tWowKFVWErMvwFuB+4H/gH8ANxkZr0LGlyevObfMcBB6d/TgL5mtpaZnWxmFZpAPitSYt4EOAXYn7gS+cLdvzOzpgVqslymdCLXxt2PSydy/zazF8ysTaFjK4+FzYnphScC5wAnpdVnpy/hTMqdyLn768QEUG8AnczsSDNbP222o5mtVUW/v5aZ7WpmTcysPjAC2B34kjip3xIYbWatq+L3V7Xc94uZ9SJagvq5+w3ufgDwAfH5LCgl5+WzNjDMzC4Hnnf3V8xsC+ASM1u3wLEtj27AG2l2t/uBu4jmvtPN7Hgzq1HIvtF0de9m1hc4091fdPfziDPbvYgmtrOAUYWK8VfYGHgM2Blo4O5/Tcv/DLQsWFTl+wGYaWaXAmcAzxNfYEdkuT/aw0RgNvAAcI+7v5WS22+B8QUNsBy5q34zOx54GTiKuKfB1sCxZvYwcKy7z62iEC4hPm+DgfuAXYAniZP5Bu6+N7FP51fR769y6TvuDOBUM9s1b9ULQA8zq1PI70El5+XzBpEcegEXpmUXAF+7+w+FCqqi8g60ycDeZtbP3Re4+2fEF+77wFZA3UI21aer+/WIL4K7oLiP9r/ELUfvBXZ1968LFeOv8DGwCfAgcBlAaubext0/LmRgOSW/kNx9HLHPFwHnuvud6fG8rDVr5hfypCKw9YjkUhvYxMxOI+45/6+s7O+S8orYDgAOBma5+yx3/ycxLeU8oubltCoM4xHiGJ1JJKv9ib7vNYGdANz90izXS5THF9udqKV4zsxOTUWDOwPT3P2nQn4PqiCsHHl9hL8B6rv7J2Y2ANiTuE2mAY3cfZeCBroMef3MdYCfiVqDXYlEN5V0VQHsAVwO3OruzxcqXoC0n3dx92PTF+peQHfgt+7+dCFjWx55+74BcY/ZWcTVz17EF91PxJfBQHf/sHCRLpZ33B9HVMtvD/zF3d9K608GDnL37dPzzBUFmdn1xFXdoenn/4g+22+IZHd3AcNbJoubDA0nunRmA4cQ8Z9alZ9NM9sIODsVnjUH/ggsBEYCb6ZY1nT36VUVQ1XKO7b7EK1Yc4kTka3Tv3WAy939irR9jUKdgCo5lyHvj7gjcBVQi+i3Ooi4bWZb4uphZJYriPO/OM1sCPEe2gE3E81kpxOFSS8DnxCJemt3L0jBT14FZXOiyX3jFOutwG5Ac3e/rBCxLa8S+34oUenalDieviGSdQPg/9z9g4IFWgozawfcTRzv9wNPuPvgtO4A4FN3H1PIL6+ypL7kk929t5mNAq7NejIuycw2IPb/GKJY6RPimNmbqDb/pop+70PE8fi3vGVHAT2AccBzwFh3r3bN2XnfLUXEzZqGEN0F57j742mb64iRIcfnlhWMu+unxA+wRt7jh4F+6fH9wBfA7oWOcQXe09VE1equwFigMenkLK2vT1w971Kg+Gqkf+sRQ6eaEH387dPyjYmz910LvS9X4L1dBpyd3tdkYMO0vFahYysn5vOAvkQR0Mtp2TrAEbnPR/7xk6Uf4ERgR6Lw7oG0rFn6O9TPcNxrlHjegujW2Ts970Bcvdaoot9/OvBi3vNLgGbp8VZExf6dRGthwffXr3iftwNd0zHyalrWANg8PR4AjC7591jZP5msDi2k3BVzenwo0QQ8CcDdf2dmBxH9E4Pc/erCRVoxqf+wNtEEfybwT+Aud//CzHZKzViPEsPDBrv7R4WI0Rdffd1MfFC+J8YBX5OWX0IkiZdWdnwrIu8s3YhmwTeJPvS/ufu3ZrYfMYztSk/fCIVWonn6TaI5uC2RpAGOBLp79DmTlbjzmdm2RFfNuUA9d98urTqV+LKdVbDgypHX/dGMSJLzgInufnhaX0S0av3Jq6ClwszqEVfHX6bi1mOBtu4+CcDd30/dSzt6NRkjXhqLERHjiK7JvsDAtOqPxMnnIHcfYmYP+eKheAWhgrClHWdmm6bHNYk+iAGWZudx9weB9Yizr8xLX6DzicKO8cRZ75Vp9WXA2h4WFCIx58WImZ0F1AUOAC4m+glPSE18F7j7mYWIb3mlEzxPxUhFwKtEU3YNXzzhyyDg8ywluBTzdqkq+xVgLaIF4zcWwwgPJ4YjZWIGpZJSncIR7v4EcexsaWYHp9EVWxMV8ZlU4uT0A6IWoR8UzxI2D7ixqk5O3f1Horh1DPAv4vg8Mf3+Wum772dPk49UJ/nHqkd33YvEicgYd/80jbg5gNj3uROlgiZm0CQkS7AYM/gpMM3MjgX+TYzvO43om3jOzEa7+9eFLLFfFouxtAcBmwJPuvurZnYN0d9ZL1UHFwGT3P3eAoZa0hzgaXf/ycw+Jr6griKa1t4vbGgVl/fBvpxoxr6FOI7czO4kmuv/5+5DChRieT4nZsBr7e4HmtlfgNuA14FL3f2z/NalQjOzpu4+JbUAtSX6RHH308zsXaLpcgzwb894P6mZdQUmuPttZjYSOCGt2g94yd1vqqLf+w+iOvlyM5tO1EbMAw42syc9o1XtFZFXO1QT+D3xnfIdMfqgtZl9QnTz3eDuE9L2maihUEFYYmaNgYbuPtrMtiTK62cShQOfEJWSvYmZtF4tWKAVYGbPAF8TX7T9iErbFywmZOhONOm8Dtzn7j8W6su2ZJVvqqC8NcV7X1r2IvAPd39mZce3IsysefqQdwcuIvoL56bksQPRdPYt8FxWvgRgqeK1U4Ct3P3Q9LyOu/9U0ABLYWY9iOKdk4jP5yDgaWK416y0TeYqyfPlNWevC2wIXEEMWXrE3W9OzdyPE8dRpc/gZ2Y7ES1o+7j7dDNrnLq8epHG4gOvu/sDlf27VyYze4CoF+pCdFMeR7SA1gF+9CwW9VZ1p3Z1+SHGE75BVOlBFJCcQRRAHEL02W5LgYsEKvA+BhGVtbnnhxD9R81YXNxRI299wd8P0AbYgmhC3ZEovBsO3Ai8UOj4luN9XEj025P2+WiiibBZoWOrQOw7Ac2JE1SAG4hWizWpogKkXxlvLaJfvGfesj3TcXMJsFkWju1lvIf8wtMHieR8KnFSfTjQkBhj/Ocq3IdTSQWuxDz2D+St34LoCvhLoffVr3yf3XLfiUST9sD0uIiMFge6u66ccyxmDdqG+FDMJj7gU4F9gT7E+OAz3P2XQsW4LKmo42ngf0Qf7ZcWY1WvIppVtyIqnoe4+z2Fi3SJK4a9iZOgXFPaVKIIbE+ii2Gku88uXKQVk65ArgF29miNqE+cpe9GNGM/4xnsr8srXLuSOPkcR1y5jSAKwg70DE40YWa3E8lkY89rgTCzJkS9wm+Aq70aFBCmoWkd3P0CM1ubmI3rHOL4/9rdz6ii37spcVX+MfEdcR3R6vBq3jb1iekt51RFDFWhlBa5TsTF14bAAnc/xsw2JL4rj3D3TwoUarlW++RcojnPiDGFhxEFJHe6+6Nm1hnYwN1fLmCoFWIxz/f5xLCdfxNVq2d4TF6/EdDK3UcUMsYci0lR/kv0BX1OfKGeCIzw1KxdHVjcsWksEfcRZlbLU/9m+mLYiRi68YxnpHmwtObedHK3FnFC2opI1hsSQwknrfQgy2AxYuIUomumF5GE7y+xzRlE/+0jBQixwlIx0sfAUHffq8S6Kh9Dnupszia6BkZ7mlApHdMLSh4j1UEqoFuTqDZ/OxWEXUBM/HOCxxSutxB3oPpTAUMtl5Lz4iuH1sS0iqOIO6/sAvQHZhDzO/9YwDCXqeQH2WLigCOIIqs+QO38q/4s9MWlfrbbgUM9+mVrE2MMdyL6hBYVOsaKSFdx6xGT0vxAjAcd6+muRxYzzO1MjKmskskjlldeoczhxKQ0PwNvepp9ytJdm8zsr8D77j60gOEWS8fME8SQl/9aDEk7kyj6Otmrbq7pSlPKld02RFfO58C+uZaiqvqMpqvz9YgLjnFpWS/ipH4S0Yz+bWX/3pXFzG4l5kVoQYxU+SsxNHMg8b4bEbPE7Z+2z0yBY77MDYdYmWzxkJediMH+hwIfEX/Ax4lCiYXpeWalD/HC9PhmM9vW3e8gmuh/IGYa2sHKuLH4So61WzoRwmM+8oXAk2ZWlJLZ18RJkleTxNwJaOruv3X3g4hK0CuAg1JXCe7+tbs/lKHEbCkxFxFXoC8TYz7/aGaDUwV07naKWxITkWTFPKLp9b/p8/s4cRJdA3jZzHYrbHjLltdS193ifs3T3L0V0fryPzMbmL9dFcjdke58MzvDzNZMrYIHEF16IyzDd+wqj5ldS/Sln+DuuVnuziG6mE5i8cibw9L2mRg2VaqV0bGd9R9gKFEcsBdxtymA9dO/NQsd33K8j8tZPCNSfrHJ+cD1GYjvCqIgYxpxlQNxgvhHosXiHmKMZ7WZBYwo2mlQYtnewEvp79GWjBYmEX2MvYkxn68Rs8e9AzxDJGUjqqEz8RkoLw4iOR9JXPl1KHSs5cWZ/v0jMCx9Hm4F/pqW/5YqnJ2K6Db6NzFD4AHEaJSSx29uXvuC76/lfG97EAWBtUosLyKGNJ5c6BiX52e1btZOfRHrAn8iCqUuBvZ394lm9k9glLvfUsgYK8pioo67iCapz9LZ8C9mtrG7f5XrBy3gsKkewBXuvm26cr4ceJaYL/gr4j6x2wDjvECToSwvW3I2ueJ+5vS8ATFlah3gd56BMbZ5Tdm5JuttiZai+4DzPYYR3kpMznBDYaMtm8UkKd8TV3lPu/uUvHWbuPu0ggVXAemz+hoxMmEOUah5GPCKuz9UVZ/RVHPyFdDL3V9Jy+4gjtGvgfWJ20S+6u5fVPbvr0qpVXAE0Qqxf94yT8f8CcSoiUGFjHN5rJbN2rZ4xphaHuMh1wCuBa5Kibk9UX7/aKFirAgz29PMWgC4+3fAZ8C+ucScNrsmNXPPT9sVIjHXIO4Lm5tIfjNivPWWRGvF74Ev3f3R6paYzWy91JR2o8XkLgC4+wx3P4zoGy14Yk4usbixwSCLGZ/e9ejfnA7sZ2ZtiCKwR6B4qsNMSZXNuxJ95JsBZ5tZcbN7VhOzmbUws7bpaU3iZLSBu89z97eBt4B9UlFelfDoVukB3Gdmf0yLd0qxTCCOgx7pebXi0a13IDHJ0vDUTbaQaP2BmB2xYaprqRZWu+Sc62OwmFrx5lRgcitx5XxQOpO8BbjEs39btPWA7/L62Z4gJro4wMz6WwyPqePu7xYswtCA1Jef+tMGEy0UpxH9+u2IIUfVRt5JznXE3+EF4FgzeyQdW7ntJhUgvKWkwq9uxLHdFzjQzBqZ2cZEi1E/4u9yY2ppWcMLdGeyknK1EumLtT7ROnQdMdf0x8BvzezM/JqKLEmVz3cQrXS5JPkGcEHeicUvwFru/mNVnkC7+2tEJX4/M/sBeNbdTyW6vU4n+mqz2QdbjnS8fu7uvYkupSfM7M++uEj2bKLgcZ5Zdmd3zLfaNmub2ZNEs/UF6XkNYDui7+qnDCS0cuUqOVPl5X+I5uGjgdbElejaxM0sznX32YWuSExfrCcQVz0GHJNrOjOzl4ikUNhbtC0nM9uBOFsflLoQ6hB3/jqAuK1fQe+JnWMx+92bwAHuPtLMLiOqx6cTBWy3ufsIM2vgGZspKb9i2cyuJgp7Zrr7vmnZRsSJ3Sx3f7ZwkZbNYtjOXHc/JV0ZNyH2e3/ipGit9HOsu49ZiXHtS0zNeqRXo3uk5zOzusT9AWbY0kMYbycuCmYB63i6iUi1UehO70L8EH08z6bHfYGniKk6q0UhEotncapFFG9sAFxKFFPtlbddzfRvZgqSiNve3Qb8nfiiHQw8XOi4lvM95O709SAxaccA4gb0ufW/A7YpdJx58RxMFB71IU6OPiTmWd+CmFHuhkLHWN6+Tv8eT9y+dWuiaO1ZoEVal5nju5T4dyW6m5ql548Q8w5ATOW6HrA90KRA8TUghnBdWOh9tYLxP0ucDK+dO16AAXnrb0r7f92sHyslf1arK+e8q81WREKeRFQO3w1sRPS3nObZngWsFlGV2pmouHzE043kzWwPYtjATKLqc74XcP5mWzwL2LZEUv4KeJvoLzwe6EmMRdzJs9+FUNr4VCNaK35HDE15zDM4PjRdrR0AdCKqs5/36FLIzRL1EDET2OeFi7JsKcbbiILCl9OyK4mTogs8hg1mUupyOo4YrrYpsK2790rrCj7XQC4OYnTKd4WOZXmY2fHAnu6+V14NyMbEZ/Fad38qbbeeu39vK2FSl8qUuYKPqpD7EOQ+CB63CduDKL1/wN1npaanGVlOzAAeFde3EdWe7Yg+z9y658zs/4jq4IJOxpA+LAstxvreSlwxnEDMCPaou19tcdegedUhMeezuH3ixkT/5x1EMc+lwPZmdqG7TyxkfCV5TKBzd9rfc4GaZnYI8SV2MvBuVhNz0pLokz03FbK94O6DzGw4GZ+DwN1fNLMpwDFE8dWQXMFmulAoeIJOv79aJeakPjHdL8AG6SR0HeIC4FQz+z3wT3cfBkvclrNaWC2unPOumI8jKoS/IooGRqcEci6wm7vvVNBAlyHvSrQmMU5yPjEL2DvuflLa5lSi//aXLJwpmtkNxLjNF4i+8XuJSS1eJu429X0Bw6uwvH3fnbghx0NAPWKqy78Qx9OVwGWeN7SnkPKuJrYm7pO9kDiROJa4yUULYFN33yZ/+4IFXA6LWdZ+R9RUjAXuyfKxk3e85PeZ70e0YHwIPA986BkpuquOLO7YdQlRBPszMRd5beICoAcxJerFBQrvV1vlk3NeYt6euNPOWUQ/xNvAu8AQ4kt2fpavHkp8yK8gqiunWMzNex7QnpgN7Fl3v7yAoWJm2xG32ZxFFEwNJSprr3L3YWb2AlEd/9dCXzUsLzO7ixgHeld6vgtxu8KBhT4RypeXmNsTV8g3AdcD3Txm1+pJNAvf7DG+ueAncjl5ia03Uey1PdEyMYE4ue5LFLP9NSsxl8ViTPYc4oLgQWLqyJOIOpEr3f2DAoZXbeVVXK9PtEg0dvcb07o1gCeBg6tjc3bOKj+UKu/L/1iin3NtokDg2bTsYmB2lhNzYlD8Yd8sJebaRAI8nrjBxaO5xFyo4QIpWZ1N3CC+kceNHn4kJo1YK21WE7i/OiRmM6tnMYFKzhiiOwGA1GRWm+g/z4y8K+CridsQTgFeTol5Y6Ko6uSUmC0rX1553SHrEHMPDCNu2/oHotDnMeI2nPdkJeay2OIx2bOJ26JeQRoKRrQk/V/hoquect9red2Us4kuvlPN7ASLu009SXTVfJ+2zfRxUpZVus+5RH/ObSy+Uj7c3adZ3K7wfc/InMflSVdBmxFNwv3SUIFBRN/nw543k1khmyfTlXFtYp7yrhbjxt8lKm3vNbOPibs3jS1EfCtgV+ALM2tI9Gc9BDxmMZnHn4HNiUKf9wsX4tLyjoHhxFXb7URXCERB4U/ufi0Ubp710qTj3IjJaca5+3MAZvYU8LSZfeapADKL8q7688dkj7DFdyc7gbgNamZnYKsOUjFYbaIl4lrimL6ZaM7+yt3/lrYreJ/+ilqlk3Nqzu5DzHX8A3F7uXWBIyzG1rYkrjozK519/+Tuz7n75FQEczlRCHMf0Y840MyGAN+nE8qCJOZUrLOA6Ms04jaJ6xFTLN6bYm/q7qMLEd8KesbdF5jZxUS/7d3EcXQ+8BzR/3mZZ6TSNe/LqDkxKxLEELu/ufv7ZtaROHHqU6gYy5K6a67xuA/5G8RkOtsSfbNfm9lNxPvKpBItEINJY7KJk9HRZjYN+JZoRZLllNdV05uoP/g7UQn/hrs/A7S1GM2SuwlQtWzOzlkl+5wtZqH63t2HmtnrwGNEct6MmJ5wc2AicLtn/36vWxHN8PsQfbcNif7l/7r7BDMbBLTzjAywt5g3+2F3L0rPDybdK5bUv1nI+FZEKsBrQ0wY8Ruiv/xeYva1TN6E3sweJibrODYVCZ5GnEy0Iu5TfleWCsAsJpP4OzGO+RJ3f9zMzia6EF4mih9PIyZ8ealwkZYtr77leOKWs5cRff0ziC6Ez7K0z6uj1KpyG1EI1o0YArifmTUFmgFvrTJFdp6BwdaV/QMcQlzRDAUuTsvWIiZduJK4wcJahY5zOd7PGsQdm94nxvXllu9HJIrMDLAnvkyHA1vlLRtA9NV2K3R8y7PP078HA+elx+sTs6/dSHzxbl3oOMuJfwNimF339LwNcYK3ZaFjKyVWy3t8GXHifBHR+rIPkbSvA44udKwVeC+bEv3JvfKWXUn0+R9V6Piq4w/R2rkZiy8mTyFasEbmvseJpu1LCx1rZf6skgVh7n4v0ZT9LXCKme3o7nPd/X/EB/73nvGbsqeKQ8ysrrsvcvdDiS+uC83spnQ1919i6r0frED3JTWzDcxsOzPbEsCjL/l5YOdUIQ/RtP28xwT/1YJH81lLovk6dwOUDYm+878Ts7PNLFB4ZcodNx7N7GOAf5pZO3f/yN2fdPeP03aZm1/YYkjj2kTBV1vgVeATdz/V3U9299sLGV8F5Y/J3h3A405IxxNjzGX5nULMJ7BjOr4fISZgmgisk7oudyC+HzN5bK+IVbJZOyf9Ic8ibsf2JFEMdiExpGd4IWMrT4lhUw8AHYG+7j7JYrakvxF3derlBR5Ta2aXE4Vp/yauzgYTTaebE31rnYn5ygd4NSi8s3Q7xfT4XOBHd7/GzE4jrpqbEjNtzfKMNGmnfuT/I/rE3yRuHfopMWXnQGK62rM97kCVSamC/EVgZ3f/zmKu+8uJVpd/uvvfqktxj1WzMdnVgZmdQdRJvEYMiW1LjJA4lGipe9hjEqZq3c+cb5W8cs5JV5yXEn/APYg/7P1ZTsxJbtjUAKKo5zvgAzM7xePOK4cRBT5ZmOziIeI+qncQJw1tib6gXYmroCnAidUkMdcAfmdxMxGA14E/mdlI4gSjBzHb2TYZSswbElNyGhHj0cR+34mY93snYshgt0LFWEHfEneY2haKh7/cTQx5HJeWZS4x2+I7ZvU2syvN7E2i2yw36U5H4CzL6B2zqpENidnAjgOuAua4+4XEyf9Rnqr6V5XEDKyafc6l/RATjRxS6DiWI97mwGSiuhniam0aUdxWL2+7LPQzn0BUBNclijImA0cRRRsHFDq+5XgfTYiTig2B/Vlcp7Bb3t/kIzLUb0sMJ+hd2RsAAAoNSURBVFmXKBJ8FOiZt64FkawvKHScFXwvA4imyrOJk43zgcsLHVc58ebqEtYhTiD2IKbpfAa4KK3bDmhT6Fir4w+LW3YPA15Ij2sRrXOTiaGM9QsdZ1X9rNLN2tWZmW1C9KEc5rkjNYZVnUf0Xf3O3T8rYIhLSM1Os4gitXfc/YK8oVWZV6IrYT9i3OTLxD2ypxCtTM8Tw8KuLVigeUrE3Jo4sehE3NDlPnefnL9dlpr88obF9AB2JEYkDCdO8G4iqrNrECfUXxcs0GVI/ZsDiJPQ/mnZb4CniWl0MzsmO8vMrJG7f5keDyBGShznqWvGzP5BtGTt7u5fFSzQKrRKN2tXczOIM/IH85ZtCNxC9M01KURQ5XiFmImqg6d7ZJPGG1YHeUluZ4/7Sl9BXI2eTAyLyU0okbnEnEzzmL5wCFFVfrqZ9YPF7y2DiXkd4B/EsbwjMVSqhbvvSsxZvldWE7OZXZESiANvpGXbmlmdFHOmx2RnmZnlbiWb8yzRktI7DZkCWEBUZ3+1qhSAlaQr54ywxTMLtSDGMjd096ct7pbVi7hq60vcevF2Yjzf9YWLeGmpYvtcYg7n97KSDJYlL1nsR/TPHuJx8/YNiGkjOxJ9iHd6Rj4weVfDFxInbfWBF939bjNrTNwQZYG7X1bQQMthZucDX7v7LWbWhjjOtyBaYG7OXTllzaowJjur0iQig4h7H6xJFPD2JU50ziWOjY2IeyHslf5PtSgUXF5KzhmQ90VbmyhC+i9R+bwesCdxMK5FTMNYBPzF3bsXKt6ypDPYi4i+uHMKHc/yMLM6RGHb0R7zT9dy9/lpXV9guru/V9Agk7yTid2IftljiArtOURdwrnuPtHM1nb3n7P45WVmTYgCzenE/bx/SX+DvYgv4suzFjMs1ZVwGXAQMSHN+UTT687EzItjvHoM/coUi9s+Xkyc6JxKFDseTNQhvEQUnK4N/M/dZ2apq6ayKTlniJkNJgocjk/PTyKKIfZKzTf1iC+D1z3GbGeSma3r7j8UOo7lYWbNiSEah3teZbnFPWGfzsr7MbO1PI3RN7OriCFs7YgCtpOJMaDrAntnqSYBlr7Csbjl3zXE/PBnu/uraXldz0g1fEl5J9LnAg2I7qeORGvXcZ7Gkcvyy51MpsfXEEOl/khcmFwMjALO9Lg/+Sp7xZyjPudsGUeaDzmNt72euHtQC4B0UN6T5cQMkJVEtjzcfQLwP+CQXL9WmkTicOKuWgVnZvWBs81sx7ToaqKQqh8w2N2nEv2fl2Y5MZtZOzPr6e6T3H0/4q5Tfzez/2/v3EKsrqIw/luUGgOlVohKYQUKPQVlNIVKo5j1IBpC4oXC6AKRogWpFWMadEFCNPJBsQtY9mIWJU5gpKVRFhJUJKmVDSV0kcIaQaqvh7XPzHHQMUpn7/M/6/fknDkDy+E/Z+219vq+tSa9vStboKchJebh+CrU5fIFCzOBj4HtKWlXxgijv0hDdGvNbDaApEX4IovpwHf44XMk3tomvaeyiRkqvviiZOpO4PWnv05ghZkdlbQuVcqtuCsVALVWa/D/qLvjPx93dfoe16VOAxan++Yx+Em9FC/ky/CWXs1L+A1Jf5hZJ778ZDTeBmyHvNvJTkVqBY/AN5Z9hVfM68y15HOhIT506zXZHek5eokeSVUj/B9KoxWXok1KA2G78We91gVahT/350L1q2aItnY20lRnV/p3972JmV2Lmy8cAo4DX6dTZHCG6FXFbcOlR1PwtvBB3B95CHBIhVmOmjvE3YSbLxzG29i/4VX0EXyLVkehibkVWC3pOjPbiB82RgErJa3p84cLI8l7nsRNaZ7CD0QtkhZnDayBSR2h2fiO5n34kpm5+PzNq/jOBJX2XJ8tIjlnwsx2AJ9Jmp++HoA/eH+mr6cDHypp+Er8sG10zGwBMFrSfDM7hFukflF/91UC6W52LD4sOFBSZ2q5X4UfIraUMqzWF2kavgu/Q7xL0lQzewwf9mmT9EHO+E6FVUSTXSLpiuAaYEfqAk3D7++3yleHTsa9tT+StCJnrP1NJOdMpPbMatwje0aarq21Wi/CrTCXNOL9bSOQfv8P4vfM0/EFC0+kD4NbgYVKHtu5MbeEbMWdpzrxqvktXHp0FDceWSppU7YgT4K5DepgYKikL9Nrhk/hXiJpkZndiw9BPp0x1FNiJ2qyd+ESn7+BY7h0bZuZDcIP1kU8L42EmS3Ck/NIfCBzD74P4SjwSPrd13cZm6ZIieScGTMbh7tQPS5pdXrtfXwi++GswVWQlBxa0il9PPAoJ2omO/BTezEacnNf5uW4ZGo8XrENxRcBDMKdkuZJ2psrxpNhZi/j98s/4pO2qyQdN99RvgRPcNfjay1/yRfp6bEG1WSXTvp7HI4b/bTjBksHcQ/t3bgs8Id8EeYjknMBJH3nZnruWsZJmpi+V/nBh/4gDXjdjbeCr8CH7DbgUo2xeEU6AjhH0oxccfaF+QrOLXhFsT691l1JlFRVmNn9eOJ9iJ5hnyWSfk7fb8WHfX4vtZ1dwxpUk91opEPoPPy5aMM9HiZLeidrYJmI5FwQZjYL162OkXSgygL7/sbMtgKfA5/i2tRn8bWKc3Bjg0vx+9CdNR1lidQd5Ay4rcRrDzMbhhvmTJL0bnptA9CCV9Hn4buat0v6KVecfVEFTXajkq4QBMyqHUKbkUjOhZH0zccjMZ85UhU3XtLMXq+/iN/fTiy9rdobc3OUZ4DLSxpeq2FmE4BNuL3lWjPbjy+D+Baf0L4AN+0ootKvp7cmGxhWd8i4B7d03SVpQXS2zj7N+lkYyTmoNHVVXJuknal1NkA9LlvbgWWSdueM879gyS2spHZ2PanKfw1fm/i8pIV1+v6BpQ5Q1cXYrcnGBweXpmn+q4G5kh7IGmhQacIhLKg0civOG4FXzGyhpL9SQmtJb9lHeRu+/hW1A0aJiRlAUpekm4HbgTlmNrVWZZaamKHbBawVP9DdAXwCXAh0mNkCSXsjMQdnm0jOQeWR9B4wGphiZm+b2RBJXeZ2mG3AN3kjrDaSXseNJJ4z36LVCIwA2pPudrCkcfgA4UozuyFvaEEzEPadQVOQdJK3mNlMYH8avpsGbJa0J2901Ue+gnMULgErjt6abPkayJom+0B622GgvfTp8qAaxJ1z0HSY2cV4q/KYpCtzxxPkp0qa7KAaRHIOmpJUFQ2W9GvuWIK8VEmTHVSHSM5BEDQtVdBkB9UkBsKCIGha6qb5N5rZfenlCfj98kF8l/dEfE1kEPQbUTkHQdD0NKomO6gukZyDIAgSaVXreuBOSW/mjidoXiI5B0EQ1JGm+fcCL0haljueoDmJ5BwEQdCLNM0/VNKR3LEEzUkk5yAIgiAojJjWDoIgCILCiOQcBEEQBIURyTkIgiAICiOScxAEQRAURiTnIAiCICiMfwCf2nib7QYM4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4aa84f518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56d1de748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56d14a8d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56d228e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56d308128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\", 8, 55)\n",
    "plot_stats(F1S, \"F1-score\", 8, 55)\n",
    "plot_stats(Prec, \"Precision\", 8, 55)\n",
    "plot_stats(Rec, \"Recall\", 8, 55)\n",
    "plot_stats(FPR, \"False Positive Rate\", 8, 55)\n",
    "plot_stats(FNR, \"False Negative Rate\", 8, 55)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}