botnet_binary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Botnet detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of botnet\n",
    "\n",
    "Raw data from: CTU-13 dataset [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] S. Garc ́ıa et al. An Empirical Comparison of Botnet Detection Methods.\n",
    "Computers & Security, 45:100–123, 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# ML algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import catboost as cb\n",
    "\n",
    "# Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/botnet_binary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <td>0.866065</td>\n",
       "      <td>0.611001</td>\n",
       "      <td>0.305781</td>\n",
       "      <td>2.547480</td>\n",
       "      <td>2.547480</td>\n",
       "      <td>0.849160</td>\n",
       "      <td>0.305781</td>\n",
       "      <td>0.305220</td>\n",
       "      <td>1.936479</td>\n",
       "      <td>0.438507</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191909</td>\n",
       "      <td>0.999687</td>\n",
       "      <td>0.406980</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.664189</td>\n",
       "      <td>0.305992</td>\n",
       "      <td>0.99968</td>\n",
       "      <td>3320.944008</td>\n",
       "      <td>1404.233487</td>\n",
       "      <td>6.634079e+03</td>\n",
       "      <td>-1060.499747</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.407122</td>\n",
       "      <td>0.273574</td>\n",
       "      <td>-0.362061</td>\n",
       "      <td>1.505125</td>\n",
       "      <td>2.539462</td>\n",
       "      <td>-0.400313</td>\n",
       "      <td>0.010620</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>60.190.222.139.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>65520</td>\n",
       "      <td>1039</td>\n",
       "      <td>73</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.248457</td>\n",
       "      <td>411</td>\n",
       "      <td>-0.565710</td>\n",
       "      <td>60.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>60</td>\n",
       "      <td>109</td>\n",
       "      <td>49</td>\n",
       "      <td>60</td>\n",
       "      <td>27.737612</td>\n",
       "      <td>16</td>\n",
       "      <td>21.917808</td>\n",
       "      <td>76.264015</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.642024</td>\n",
       "      <td>7.852450</td>\n",
       "      <td>1.469404</td>\n",
       "      <td>9.636386e-05</td>\n",
       "      <td>1.142735e-04</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>2.103153</td>\n",
       "      <td>1.370951</td>\n",
       "      <td>0.412697</td>\n",
       "      <td>0.628359</td>\n",
       "      <td>False</td>\n",
       "      <td>0.602060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.283852</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.843926</td>\n",
       "      <td>0.313772</td>\n",
       "      <td>0.313485</td>\n",
       "      <td>0.619253</td>\n",
       "      <td>2.973125</td>\n",
       "      <td>0.330347</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>2.353862</td>\n",
       "      <td>0.140343</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>738</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213947</td>\n",
       "      <td>0.898465</td>\n",
       "      <td>0.434197</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.683757</td>\n",
       "      <td>0.202726</td>\n",
       "      <td>0.89372</td>\n",
       "      <td>3418.782649</td>\n",
       "      <td>1624.178589</td>\n",
       "      <td>1.353230e+04</td>\n",
       "      <td>-4837.676869</td>\n",
       "      <td>0.187056</td>\n",
       "      <td>0.482592</td>\n",
       "      <td>0.285750</td>\n",
       "      <td>-0.098411</td>\n",
       "      <td>1.314013</td>\n",
       "      <td>2.857491</td>\n",
       "      <td>0.565604</td>\n",
       "      <td>0.021624</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328463</th>\n",
       "      <td>328463</td>\n",
       "      <td>74.125.232.199.</td>\n",
       "      <td>147.32.84.208</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>1029</td>\n",
       "      <td>158</td>\n",
       "      <td>60.0</td>\n",
       "      <td>181.923061</td>\n",
       "      <td>33096</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>60.00</td>\n",
       "      <td>158.25</td>\n",
       "      <td>60</td>\n",
       "      <td>453</td>\n",
       "      <td>393</td>\n",
       "      <td>60</td>\n",
       "      <td>115.141178</td>\n",
       "      <td>147</td>\n",
       "      <td>93.037975</td>\n",
       "      <td>232.383842</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.538689</td>\n",
       "      <td>2.275687</td>\n",
       "      <td>0.626167</td>\n",
       "      <td>4.152185e-08</td>\n",
       "      <td>5.178685e-08</td>\n",
       "      <td>-0.994911</td>\n",
       "      <td>1.791687</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.270426</td>\n",
       "      <td>0.463169</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363610</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.013187</td>\n",
       "      <td>0.008346</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.036260</td>\n",
       "      <td>0.036267</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.027907</td>\n",
       "      <td>0.185652</td>\n",
       "      <td>False</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.227399</td>\n",
       "      <td>0.641747</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.056336</td>\n",
       "      <td>0.063267</td>\n",
       "      <td>0.07496</td>\n",
       "      <td>281.679731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.102747e+07</td>\n",
       "      <td>-309222.745769</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.570916</td>\n",
       "      <td>0.277085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.391439</td>\n",
       "      <td>9.477181</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.672026</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328464</th>\n",
       "      <td>328464</td>\n",
       "      <td>147.32.84.208.</td>\n",
       "      <td>74.125.232.199</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1029</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.452222</td>\n",
       "      <td>1977</td>\n",
       "      <td>-0.313226</td>\n",
       "      <td>60.00</td>\n",
       "      <td>98.50</td>\n",
       "      <td>60</td>\n",
       "      <td>137</td>\n",
       "      <td>77</td>\n",
       "      <td>60</td>\n",
       "      <td>52.296731</td>\n",
       "      <td>34</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>93.039418</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.562402</td>\n",
       "      <td>4.386732</td>\n",
       "      <td>-1.330721</td>\n",
       "      <td>7.589765e-06</td>\n",
       "      <td>1.859105e-05</td>\n",
       "      <td>-0.298701</td>\n",
       "      <td>0.690887</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.439609</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.008410</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.013189</td>\n",
       "      <td>0.013189</td>\n",
       "      <td>0.008410</td>\n",
       "      <td>0.017968</td>\n",
       "      <td>0.734027</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.813981</td>\n",
       "      <td>3.044901</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.886561</td>\n",
       "      <td>1.750022</td>\n",
       "      <td>0.785367</td>\n",
       "      <td>3.04489</td>\n",
       "      <td>8750.110359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.184658e+03</td>\n",
       "      <td>2301.171256</td>\n",
       "      <td>0.750872</td>\n",
       "      <td>0.626362</td>\n",
       "      <td>0.270651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.739922</td>\n",
       "      <td>6.196735</td>\n",
       "      <td>0.879100</td>\n",
       "      <td>0.004934</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328465</th>\n",
       "      <td>328465</td>\n",
       "      <td>74.125.232.198.</td>\n",
       "      <td>147.32.84.209</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>1029</td>\n",
       "      <td>158</td>\n",
       "      <td>60.0</td>\n",
       "      <td>181.923061</td>\n",
       "      <td>33096</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>60.00</td>\n",
       "      <td>158.25</td>\n",
       "      <td>60</td>\n",
       "      <td>453</td>\n",
       "      <td>393</td>\n",
       "      <td>60</td>\n",
       "      <td>115.141178</td>\n",
       "      <td>147</td>\n",
       "      <td>93.037975</td>\n",
       "      <td>232.383842</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.538689</td>\n",
       "      <td>2.275687</td>\n",
       "      <td>0.626167</td>\n",
       "      <td>4.152185e-08</td>\n",
       "      <td>5.178685e-08</td>\n",
       "      <td>-0.994911</td>\n",
       "      <td>1.791687</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.270426</td>\n",
       "      <td>0.463169</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356503</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.016509</td>\n",
       "      <td>0.009954</td>\n",
       "      <td>0.009769</td>\n",
       "      <td>0.046300</td>\n",
       "      <td>0.046308</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.036340</td>\n",
       "      <td>0.182043</td>\n",
       "      <td>False</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-13.714278</td>\n",
       "      <td>0.695288</td>\n",
       "      <td>0.007859</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.051829</td>\n",
       "      <td>0.195399</td>\n",
       "      <td>0.06293</td>\n",
       "      <td>259.144821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.463521e+07</td>\n",
       "      <td>-349628.819380</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.561914</td>\n",
       "      <td>0.265709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.415039</td>\n",
       "      <td>28.543362</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.305928</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328466</th>\n",
       "      <td>328466</td>\n",
       "      <td>147.32.84.209.</td>\n",
       "      <td>74.125.232.198</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1029</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.452222</td>\n",
       "      <td>1977</td>\n",
       "      <td>-0.313226</td>\n",
       "      <td>60.00</td>\n",
       "      <td>98.50</td>\n",
       "      <td>60</td>\n",
       "      <td>137</td>\n",
       "      <td>77</td>\n",
       "      <td>60</td>\n",
       "      <td>52.296731</td>\n",
       "      <td>34</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>93.039418</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.562402</td>\n",
       "      <td>4.386732</td>\n",
       "      <td>-1.330721</td>\n",
       "      <td>7.589765e-06</td>\n",
       "      <td>1.859105e-05</td>\n",
       "      <td>-0.298701</td>\n",
       "      <td>0.690887</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.412999</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.014293</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034607</td>\n",
       "      <td>0.034607</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.026336</td>\n",
       "      <td>0.657028</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.802027</td>\n",
       "      <td>1.520759</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.863715</td>\n",
       "      <td>1.223380</td>\n",
       "      <td>0.212232</td>\n",
       "      <td>1.52075</td>\n",
       "      <td>6116.901522</td>\n",
       "      <td>-1912.244669</td>\n",
       "      <td>7.085380e+03</td>\n",
       "      <td>476.947053</td>\n",
       "      <td>0.586177</td>\n",
       "      <td>0.551710</td>\n",
       "      <td>0.282870</td>\n",
       "      <td>0.164352</td>\n",
       "      <td>1.243080</td>\n",
       "      <td>1.189093</td>\n",
       "      <td>0.856055</td>\n",
       "      <td>0.033199</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328467</th>\n",
       "      <td>328467</td>\n",
       "      <td>147.32.84.208.</td>\n",
       "      <td>61.191.61.238</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>54934</td>\n",
       "      <td>1711</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.875000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328468 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0           DST_IP          SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                0      147.32.80.9   147.32.84.165        4            2   \n",
       "1                1    147.32.84.255   147.32.84.165       38            0   \n",
       "2                2    147.32.84.255   147.32.84.165        6            0   \n",
       "3                3   147.32.84.165.  60.190.222.139        4            0   \n",
       "4                4  60.190.222.139.   147.32.84.165       10            0   \n",
       "...            ...              ...             ...      ...          ...   \n",
       "328463      328463  74.125.232.199.   147.32.84.208        8            0   \n",
       "328464      328464   147.32.84.208.  74.125.232.199        3            0   \n",
       "328465      328465  74.125.232.198.   147.32.84.209        8            0   \n",
       "328466      328466   147.32.84.209.  74.125.232.198        3            0   \n",
       "328467      328467   147.32.84.208.   61.191.61.238        1            0   \n",
       "\n",
       "        BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0         134        558  1.312967e+09  1.312967e+09        53      1025   \n",
       "1        2476          0  1.312967e+09  1.312967e+09       137       137   \n",
       "2        1160          0  1.312967e+09  1.312967e+09       138       138   \n",
       "3         249          0  1.312967e+09  1.312967e+09      1039     65520   \n",
       "4         138          0  1.312967e+09  1.312967e+09     65520      1039   \n",
       "...       ...        ...           ...           ...       ...       ...   \n",
       "328463    786          0  1.313673e+09  1.313673e+09        80      1029   \n",
       "328464     77          0  1.313673e+09  1.313673e+09      1029        80   \n",
       "328465    786          0  1.313673e+09  1.313673e+09        80      1029   \n",
       "328466     77          0  1.313673e+09  1.313673e+09      1029        80   \n",
       "328467      0          0  1.313673e+09  1.313673e+09     54934      1711   \n",
       "\n",
       "        MEAN  MEDIAN       STDEV    VAR  BURSTINESS      Q1      Q3  MIN  MAX  \\\n",
       "0        175   105.0  171.542998  29427   -0.009976   87.75  144.00   82  521   \n",
       "1        125   128.0    6.633250     44   -0.899216  128.00  128.00  110  128   \n",
       "2        253   261.0   11.874342    141   -0.910340  243.75  261.00  238  261   \n",
       "3        122    60.0  124.498996  15500    0.010138   60.00  122.25   60  309   \n",
       "4         73    60.0   20.248457    411   -0.565710   60.00   80.00   60  109   \n",
       "...      ...     ...         ...    ...         ...     ...     ...  ...  ...   \n",
       "328463   158    60.0  181.923061  33096    0.070378   60.00  158.25   60  453   \n",
       "328464    85    60.0   44.452222   1977   -0.313226   60.00   98.50   60  137   \n",
       "328465   158    60.0  181.923061  33096    0.070378   60.00  158.25   60  453   \n",
       "328466    85    60.0   44.452222   1977   -0.313226   60.00   98.50   60  137   \n",
       "328467    60    60.0   60.000000      0    0.000000   60.00   60.00   60   60   \n",
       "\n",
       "        MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                 439    82                 98.024570                 115   \n",
       "1                  18   128                  5.306600                   4   \n",
       "2                  23   261                  4.693416                  10   \n",
       "3                 249    60                102.048357                  93   \n",
       "4                  49    60                 27.737612                  16   \n",
       "...               ...   ...                       ...                 ...   \n",
       "328463            393    60                115.141178                 147   \n",
       "328464             77    60                 52.296731                  34   \n",
       "328465            393    60                115.141178                 147   \n",
       "328466             77    60                 52.296731                  34   \n",
       "328467              0    60                100.000000                   0   \n",
       "\n",
       "        PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0               65.714286        235.084382            0.833333   \n",
       "1                3.200000        125.329881            0.157895   \n",
       "2                3.952569        253.565245            0.333333   \n",
       "3               76.229508        163.003834            0.750000   \n",
       "4               21.917808         76.264015            0.600000   \n",
       "...                   ...               ...                 ...   \n",
       "328463          93.037975        232.383842            0.750000   \n",
       "328464          40.000000         93.039418            0.666667   \n",
       "328465          93.037975        232.383842            0.750000   \n",
       "328466          40.000000         93.039418            0.666667   \n",
       "328467           0.000000         60.000000            0.000000   \n",
       "\n",
       "        PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                 0.166667              0.542138              2.448366   \n",
       "1                 0.842105             -0.452267             37.236651   \n",
       "2                 0.666667             -0.673722             41.939166   \n",
       "3                 0.250000              0.497996              2.457851   \n",
       "4                 0.400000              0.642024              7.852450   \n",
       "...                    ...                   ...                   ...   \n",
       "328463            0.250000              0.538689              2.275687   \n",
       "328464            0.333333              0.562402              4.386732   \n",
       "328465            0.250000              0.538689              2.275687   \n",
       "328466            0.333333              0.562402              4.386732   \n",
       "328467            0.000000              0.000000              2.000000   \n",
       "\n",
       "        FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                   0.787684                6.603281e-08   \n",
       "1                  22.461333                5.409893e-04   \n",
       "2                  27.184983                1.990903e-04   \n",
       "3                   0.025423                1.295515e-07   \n",
       "4                   1.469404                9.636386e-05   \n",
       "...                      ...                         ...   \n",
       "328463              0.626167                4.152185e-08   \n",
       "328464             -1.330721                7.589765e-06   \n",
       "328465              0.626167                4.152185e-08   \n",
       "328466             -1.330721                7.589765e-06   \n",
       "328467             -3.000000                0.000000e+00   \n",
       "\n",
       "        FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                     9.041915e-08        -2.102222  2.796482  1.918296   \n",
       "1                     5.634804e-04         0.000000  4.164066  0.629249   \n",
       "2                     2.726156e-04        -0.072464  0.986151  0.918296   \n",
       "3                     2.243898e-07        -0.991968  1.318585  0.811278   \n",
       "4                     1.142735e-04        -0.300000  2.103153  1.370951   \n",
       "...                            ...              ...       ...       ...   \n",
       "328463                5.178685e-08        -0.994911  1.791687  0.811278   \n",
       "328464                1.859105e-05        -0.298701  0.690887  0.918296   \n",
       "328465                5.178685e-08        -0.994911  1.791687  0.811278   \n",
       "328466                1.859105e-05        -0.298701  0.690887  0.918296   \n",
       "328467               -0.000000e+00         0.000000  0.000000  0.000000   \n",
       "\n",
       "        SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0             0.742098        0.411985                  False   0.698970   \n",
       "1             0.119904        0.737466                  False   0.458925   \n",
       "2             0.355245        0.584406                  False   0.477121   \n",
       "3             0.405639        0.217397                  False   0.477121   \n",
       "4             0.412697        0.628359                  False   0.602060   \n",
       "...                ...             ...                    ...        ...   \n",
       "328463        0.270426        0.463169                  False   0.477121   \n",
       "328464        0.579380        0.535744                  False   0.477121   \n",
       "328465        0.270426        0.463169                  False   0.477121   \n",
       "328466        0.579380        0.535744                  False   0.477121   \n",
       "328467        0.000000        1.000000                  False   0.301030   \n",
       "\n",
       "        NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                       0.0               0.0           0.497004   \n",
       "1                       0.0               0.0           0.428112   \n",
       "2                       0.0               0.0           0.666641   \n",
       "3                       0.0               0.0           0.339969   \n",
       "4                       0.0               0.0           0.283852   \n",
       "...                     ...               ...                ...   \n",
       "328463                  0.0               0.0           0.363610   \n",
       "328464                  0.0               0.0           0.439609   \n",
       "328465                  0.0               0.0           0.356503   \n",
       "328466                  0.0               0.0           0.412999   \n",
       "328467                  0.0               0.0                NaN   \n",
       "\n",
       "        AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                       0.416667          1.131372            2.255602   \n",
       "1                       0.828125          5.330970            4.501289   \n",
       "2                       0.750000          2.675396            4.012946   \n",
       "3                       0.791667          0.866065            0.611001   \n",
       "4                       0.708333          0.843926            0.313772   \n",
       "...                          ...               ...                 ...   \n",
       "328463                  0.791667          0.013187            0.008346   \n",
       "328464                  0.750000          0.011596            0.008410   \n",
       "328465                  0.791667          0.016509            0.009954   \n",
       "328466                  0.750000          0.014293            0.008271   \n",
       "328467                  0.875000          0.000000            0.000000   \n",
       "\n",
       "        Q1_SCALED_TIME  Q3_SCALED_TIME   DURATION  MEAN_DIFFTIMES  \\\n",
       "0             0.000006        2.255614   2.276383        0.455277   \n",
       "1             1.916961        8.436423  12.452268        0.336548   \n",
       "2             0.000012        4.013236   4.013247        0.802649   \n",
       "3             0.305781        2.547480   2.547480        0.849160   \n",
       "4             0.313485        0.619253   2.973125        0.330347   \n",
       "...                ...             ...        ...             ...   \n",
       "328463        0.008129        0.036260   0.036267        0.005181   \n",
       "328464        0.000000        0.026378   0.026378        0.013189   \n",
       "328465        0.009769        0.046300   0.046308        0.006615   \n",
       "328466        0.000000        0.034607   0.034607        0.017303   \n",
       "328467        0.000000        0.000000   0.000000             NaN   \n",
       "\n",
       "        MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0               0.000619       0.000006       2.254977               0.201899   \n",
       "1               0.000015       0.000000       1.763265               0.190866   \n",
       "2               0.000012       0.000011       4.012922               0.200016   \n",
       "3               0.305781       0.305220       1.936479               0.438507   \n",
       "4               0.000010       0.000007       2.353862               0.140343   \n",
       "...                  ...            ...            ...                    ...   \n",
       "328463          0.000007       0.000005       0.027907               0.185652   \n",
       "328464          0.013189       0.008410       0.017968               0.734027   \n",
       "328465          0.000008       0.000006       0.036340               0.182043   \n",
       "328466          0.017303       0.008271       0.026336               0.657028   \n",
       "328467               NaN            NaN            NaN                    NaN   \n",
       "\n",
       "        SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0            False          1.200000       False        0.0   \n",
       "1            False          0.324324       False        0.0   \n",
       "2            False          0.800000       False        0.0   \n",
       "3            False          0.666667       False        0.0   \n",
       "4            False          0.666667       False        0.0   \n",
       "...            ...               ...         ...        ...   \n",
       "328463       False          0.571429        True        0.0   \n",
       "328464       False          1.000000       False        0.0   \n",
       "328465       False          0.571429        True        0.0   \n",
       "328466       False          1.000000       False        0.0   \n",
       "328467       False          0.000000       False        0.0   \n",
       "\n",
       "        CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                       0.0               1052    0.666667        False    0   \n",
       "1                       0.0               4756    1.000000        False    0   \n",
       "2                       0.0               1520    1.000000        False    0   \n",
       "3                       0.0                489    1.000000        False    0   \n",
       "4                       0.0                738    1.000000        False    0   \n",
       "...                     ...                ...         ...          ...  ...   \n",
       "328463                  0.0               1266    1.000000        False    0   \n",
       "328464                  0.0                257    1.000000        False    0   \n",
       "328465                  0.0               1266    1.000000        False    0   \n",
       "328466                  0.0                257    1.000000        False    0   \n",
       "328467                  0.0                 60    1.000000        False    0   \n",
       "\n",
       "        TIME   MIN_POWER    MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0        0.0 -453.441475  1109.026765        0.320957        0.885171   \n",
       "1        0.0    0.000119     0.208772        0.899673        0.069644   \n",
       "2        0.0 -318.548520    35.248517        0.499956        0.010243   \n",
       "3        0.0    0.191909     0.999687        0.406980        0.006667   \n",
       "4        0.0    0.213947     0.898465        0.434197        0.999801   \n",
       "...      ...         ...          ...             ...             ...   \n",
       "328463   0.0   -4.227399     0.641747        0.007660        0.007461   \n",
       "328464   0.0   -0.813981     3.044901        0.007461        0.886561   \n",
       "328465   0.0  -13.714278     0.695288        0.007859        0.007660   \n",
       "328466   0.0    0.802027     1.520759        0.007660        0.863715   \n",
       "328467   0.0         NaN          NaN             NaN             NaN   \n",
       "\n",
       "        POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0         0.261034  18.320460     1.12845      1305.172416               NaN   \n",
       "1         0.057344   0.066138     0.00012       286.717791        923.599485   \n",
       "2         0.164230   5.155446     0.24979       821.149907               NaN   \n",
       "3         0.664189   0.305992     0.99968      3320.944008       1404.233487   \n",
       "4         0.683757   0.202726     0.89372      3418.782649       1624.178589   \n",
       "...            ...        ...         ...              ...               ...   \n",
       "328463    0.056336   0.063267     0.07496       281.679731               NaN   \n",
       "328464    1.750022   0.785367     3.04489      8750.110359               NaN   \n",
       "328465    0.051829   0.195399     0.06293       259.144821               NaN   \n",
       "328466    1.223380   0.212232     1.52075      6116.901522      -1912.244669   \n",
       "328467         NaN        NaN     0.00000              NaN               NaN   \n",
       "\n",
       "        SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0            1.385326e+07      207900.070205          0.885171   \n",
       "1            1.316854e+04        5314.872532          0.038851   \n",
       "2            1.577782e+07     -271216.457937          0.010243   \n",
       "3            6.634079e+03       -1060.499747          0.006667   \n",
       "4            1.353230e+04       -4837.676869          0.187056   \n",
       "...                   ...                ...               ...   \n",
       "328463       2.102747e+07     -309222.745769          0.007461   \n",
       "328464       8.184658e+03        2301.171256          0.750872   \n",
       "328465       2.463521e+07     -349628.819380          0.007660   \n",
       "328466       7.085380e+03         476.947053          0.586177   \n",
       "328467                NaN                NaN          0.006667   \n",
       "\n",
       "        SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0                0.205123         0.417904             NaN     4248.583372   \n",
       "1                0.241710         0.223803       -1.333903        3.640725   \n",
       "2                0.393857         0.271931             NaN      214.629001   \n",
       "3                0.407122         0.273574       -0.362061        1.505125   \n",
       "4                0.482592         0.285750       -0.098411        1.314013   \n",
       "...                   ...              ...             ...             ...   \n",
       "328463           0.570916         0.277085             NaN       11.391439   \n",
       "328464           0.626362         0.270651             NaN        1.739922   \n",
       "328465           0.561914         0.265709             NaN       13.415039   \n",
       "328466           0.551710         0.282870        0.164352        1.243080   \n",
       "328467                NaN              NaN             NaN             NaN   \n",
       "\n",
       "        SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0         4544.559854            0.564213          0.003325   True  \n",
       "1            0.786195           -0.830029          0.046606  False  \n",
       "2         1171.605316           -0.489713          0.011965  False  \n",
       "3            2.539462           -0.400313          0.010620  False  \n",
       "4            2.857491            0.565604          0.021624  False  \n",
       "...               ...                 ...               ...    ...  \n",
       "328463       9.477181           -0.000199          0.672026  False  \n",
       "328464       6.196735            0.879100          0.004934  False  \n",
       "328465      28.543362           -0.000199          0.305928  False  \n",
       "328466       1.189093            0.856055          0.033199  False  \n",
       "328467            NaN            0.000000               NaN  False  \n",
       "\n",
       "[328468 rows x 84 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "]\n",
    "\n",
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    if F in bool_features:\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    # elif F in frequency_features: \n",
    "    #     df.replace([np.inf], -1, inplace=True)\n",
    "    #     df.replace([-np.inf], -1, inplace=True)\n",
    "    #     df.loc[df[F].isnull(), F] = -1\n",
    "    # else:\n",
    "    #     df.replace([np.inf], 0, inplace=True)\n",
    "    #     df.replace([-np.inf], 0, inplace=True)\n",
    "    #     df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df.loc[df[F].isnull(), F] = False\n",
    "    df[F] = df[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
       "      <th>PEARSON_SK1_SKEWNESS</th>\n",
       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>147.32.80.9</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>558</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>1025</td>\n",
       "      <td>175</td>\n",
       "      <td>105.0</td>\n",
       "      <td>171.542998</td>\n",
       "      <td>29427</td>\n",
       "      <td>-0.009976</td>\n",
       "      <td>87.75</td>\n",
       "      <td>144.00</td>\n",
       "      <td>82</td>\n",
       "      <td>521</td>\n",
       "      <td>439</td>\n",
       "      <td>82</td>\n",
       "      <td>98.024570</td>\n",
       "      <td>115</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>235.084382</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.542138</td>\n",
       "      <td>2.448366</td>\n",
       "      <td>0.787684</td>\n",
       "      <td>6.603281e-08</td>\n",
       "      <td>9.041915e-08</td>\n",
       "      <td>-2.102222</td>\n",
       "      <td>2.796482</td>\n",
       "      <td>1.918296</td>\n",
       "      <td>0.742098</td>\n",
       "      <td>0.411985</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.497004</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.131372</td>\n",
       "      <td>2.255602</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>2.255614</td>\n",
       "      <td>2.276383</td>\n",
       "      <td>0.455277</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>2.254977</td>\n",
       "      <td>0.201899</td>\n",
       "      <td>False</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1052</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-453.441475</td>\n",
       "      <td>1109.026765</td>\n",
       "      <td>0.320957</td>\n",
       "      <td>0.885171</td>\n",
       "      <td>0.261034</td>\n",
       "      <td>18.320460</td>\n",
       "      <td>1.12845</td>\n",
       "      <td>1305.172416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.385326e+07</td>\n",
       "      <td>207900.070205</td>\n",
       "      <td>0.885171</td>\n",
       "      <td>0.205123</td>\n",
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       "      <td>4248.583372</td>\n",
       "      <td>4544.559854</td>\n",
       "      <td>0.564213</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>147.32.84.255</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>0</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>125</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.633250</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.899216</td>\n",
       "      <td>128.00</td>\n",
       "      <td>128.00</td>\n",
       "      <td>110</td>\n",
       "      <td>128</td>\n",
       "      <td>18</td>\n",
       "      <td>128</td>\n",
       "      <td>5.306600</td>\n",
       "      <td>4</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>125.329881</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>-0.452267</td>\n",
       "      <td>37.236651</td>\n",
       "      <td>22.461333</td>\n",
       "      <td>5.409893e-04</td>\n",
       "      <td>5.634804e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.164066</td>\n",
       "      <td>0.629249</td>\n",
       "      <td>0.119904</td>\n",
       "      <td>0.737466</td>\n",
       "      <td>False</td>\n",
       "      <td>0.458925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.428112</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>5.330970</td>\n",
       "      <td>4.501289</td>\n",
       "      <td>1.916961</td>\n",
       "      <td>8.436423</td>\n",
       "      <td>12.452268</td>\n",
       "      <td>0.336548</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.763265</td>\n",
       "      <td>0.190866</td>\n",
       "      <td>False</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.208772</td>\n",
       "      <td>0.899673</td>\n",
       "      <td>0.069644</td>\n",
       "      <td>0.057344</td>\n",
       "      <td>0.066138</td>\n",
       "      <td>0.00012</td>\n",
       "      <td>286.717791</td>\n",
       "      <td>923.599485</td>\n",
       "      <td>1.316854e+04</td>\n",
       "      <td>5314.872532</td>\n",
       "      <td>0.038851</td>\n",
       "      <td>0.241710</td>\n",
       "      <td>0.223803</td>\n",
       "      <td>-1.333903</td>\n",
       "      <td>3.640725</td>\n",
       "      <td>0.786195</td>\n",
       "      <td>-0.830029</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>147.32.84.255</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>253</td>\n",
       "      <td>261.0</td>\n",
       "      <td>11.874342</td>\n",
       "      <td>141</td>\n",
       "      <td>-0.910340</td>\n",
       "      <td>243.75</td>\n",
       "      <td>261.00</td>\n",
       "      <td>238</td>\n",
       "      <td>261</td>\n",
       "      <td>23</td>\n",
       "      <td>261</td>\n",
       "      <td>4.693416</td>\n",
       "      <td>10</td>\n",
       "      <td>3.952569</td>\n",
       "      <td>253.565245</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.673722</td>\n",
       "      <td>41.939166</td>\n",
       "      <td>27.184983</td>\n",
       "      <td>1.990903e-04</td>\n",
       "      <td>2.726156e-04</td>\n",
       "      <td>-0.072464</td>\n",
       "      <td>0.986151</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.355245</td>\n",
       "      <td>0.584406</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666641</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.675396</td>\n",
       "      <td>4.012946</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>4.013236</td>\n",
       "      <td>4.013247</td>\n",
       "      <td>0.802649</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>4.012922</td>\n",
       "      <td>0.200016</td>\n",
       "      <td>False</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-318.548520</td>\n",
       "      <td>35.248517</td>\n",
       "      <td>0.499956</td>\n",
       "      <td>0.010243</td>\n",
       "      <td>0.164230</td>\n",
       "      <td>5.155446</td>\n",
       "      <td>0.24979</td>\n",
       "      <td>821.149907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.577782e+07</td>\n",
       "      <td>-271216.457937</td>\n",
       "      <td>0.010243</td>\n",
       "      <td>0.393857</td>\n",
       "      <td>0.271931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214.629001</td>\n",
       "      <td>1171.605316</td>\n",
       "      <td>-0.489713</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>147.32.84.165.</td>\n",
       "      <td>60.190.222.139</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1039</td>\n",
       "      <td>65520</td>\n",
       "      <td>122</td>\n",
       "      <td>60.0</td>\n",
       "      <td>124.498996</td>\n",
       "      <td>15500</td>\n",
       "      <td>0.010138</td>\n",
       "      <td>60.00</td>\n",
       "      <td>122.25</td>\n",
       "      <td>60</td>\n",
       "      <td>309</td>\n",
       "      <td>249</td>\n",
       "      <td>60</td>\n",
       "      <td>102.048357</td>\n",
       "      <td>93</td>\n",
       "      <td>76.229508</td>\n",
       "      <td>163.003834</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.497996</td>\n",
       "      <td>2.457851</td>\n",
       "      <td>0.025423</td>\n",
       "      <td>1.295515e-07</td>\n",
       "      <td>2.243898e-07</td>\n",
       "      <td>-0.991968</td>\n",
       "      <td>1.318585</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.405639</td>\n",
       "      <td>0.217397</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.339969</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.866065</td>\n",
       "      <td>0.611001</td>\n",
       "      <td>0.305781</td>\n",
       "      <td>2.547480</td>\n",
       "      <td>2.547480</td>\n",
       "      <td>0.849160</td>\n",
       "      <td>0.305781</td>\n",
       "      <td>0.305220</td>\n",
       "      <td>1.936479</td>\n",
       "      <td>0.438507</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191909</td>\n",
       "      <td>0.999687</td>\n",
       "      <td>0.406980</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.664189</td>\n",
       "      <td>0.305992</td>\n",
       "      <td>0.99968</td>\n",
       "      <td>3320.944008</td>\n",
       "      <td>1404.233487</td>\n",
       "      <td>6.634079e+03</td>\n",
       "      <td>-1060.499747</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.407122</td>\n",
       "      <td>0.273574</td>\n",
       "      <td>-0.362061</td>\n",
       "      <td>1.505125</td>\n",
       "      <td>2.539462</td>\n",
       "      <td>-0.400313</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>60.190.222.139.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>65520</td>\n",
       "      <td>1039</td>\n",
       "      <td>73</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.248457</td>\n",
       "      <td>411</td>\n",
       "      <td>-0.565710</td>\n",
       "      <td>60.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>60</td>\n",
       "      <td>109</td>\n",
       "      <td>49</td>\n",
       "      <td>60</td>\n",
       "      <td>27.737612</td>\n",
       "      <td>16</td>\n",
       "      <td>21.917808</td>\n",
       "      <td>76.264015</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.642024</td>\n",
       "      <td>7.852450</td>\n",
       "      <td>1.469404</td>\n",
       "      <td>9.636386e-05</td>\n",
       "      <td>1.142735e-04</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>2.103153</td>\n",
       "      <td>1.370951</td>\n",
       "      <td>0.412697</td>\n",
       "      <td>0.628359</td>\n",
       "      <td>False</td>\n",
       "      <td>0.602060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.283852</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.843926</td>\n",
       "      <td>0.313772</td>\n",
       "      <td>0.313485</td>\n",
       "      <td>0.619253</td>\n",
       "      <td>2.973125</td>\n",
       "      <td>0.330347</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>2.353862</td>\n",
       "      <td>0.140343</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>738</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213947</td>\n",
       "      <td>0.898465</td>\n",
       "      <td>0.434197</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.683757</td>\n",
       "      <td>0.202726</td>\n",
       "      <td>0.89372</td>\n",
       "      <td>3418.782649</td>\n",
       "      <td>1624.178589</td>\n",
       "      <td>1.353230e+04</td>\n",
       "      <td>-4837.676869</td>\n",
       "      <td>0.187056</td>\n",
       "      <td>0.482592</td>\n",
       "      <td>0.285750</td>\n",
       "      <td>-0.098411</td>\n",
       "      <td>1.314013</td>\n",
       "      <td>2.857491</td>\n",
       "      <td>0.565604</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328463</th>\n",
       "      <td>328463</td>\n",
       "      <td>74.125.232.199.</td>\n",
       "      <td>147.32.84.208</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>1029</td>\n",
       "      <td>158</td>\n",
       "      <td>60.0</td>\n",
       "      <td>181.923061</td>\n",
       "      <td>33096</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>60.00</td>\n",
       "      <td>158.25</td>\n",
       "      <td>60</td>\n",
       "      <td>453</td>\n",
       "      <td>393</td>\n",
       "      <td>60</td>\n",
       "      <td>115.141178</td>\n",
       "      <td>147</td>\n",
       "      <td>93.037975</td>\n",
       "      <td>232.383842</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.538689</td>\n",
       "      <td>2.275687</td>\n",
       "      <td>0.626167</td>\n",
       "      <td>4.152185e-08</td>\n",
       "      <td>5.178685e-08</td>\n",
       "      <td>-0.994911</td>\n",
       "      <td>1.791687</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.270426</td>\n",
       "      <td>0.463169</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363610</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.013187</td>\n",
       "      <td>0.008346</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.036260</td>\n",
       "      <td>0.036267</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.027907</td>\n",
       "      <td>0.185652</td>\n",
       "      <td>False</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.227399</td>\n",
       "      <td>0.641747</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.056336</td>\n",
       "      <td>0.063267</td>\n",
       "      <td>0.07496</td>\n",
       "      <td>281.679731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.102747e+07</td>\n",
       "      <td>-309222.745769</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.570916</td>\n",
       "      <td>0.277085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.391439</td>\n",
       "      <td>9.477181</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328464</th>\n",
       "      <td>328464</td>\n",
       "      <td>147.32.84.208.</td>\n",
       "      <td>74.125.232.199</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1029</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.452222</td>\n",
       "      <td>1977</td>\n",
       "      <td>-0.313226</td>\n",
       "      <td>60.00</td>\n",
       "      <td>98.50</td>\n",
       "      <td>60</td>\n",
       "      <td>137</td>\n",
       "      <td>77</td>\n",
       "      <td>60</td>\n",
       "      <td>52.296731</td>\n",
       "      <td>34</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>93.039418</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.562402</td>\n",
       "      <td>4.386732</td>\n",
       "      <td>-1.330721</td>\n",
       "      <td>7.589765e-06</td>\n",
       "      <td>1.859105e-05</td>\n",
       "      <td>-0.298701</td>\n",
       "      <td>0.690887</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.439609</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.008410</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.013189</td>\n",
       "      <td>0.013189</td>\n",
       "      <td>0.008410</td>\n",
       "      <td>0.017968</td>\n",
       "      <td>0.734027</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.813981</td>\n",
       "      <td>3.044901</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.886561</td>\n",
       "      <td>1.750022</td>\n",
       "      <td>0.785367</td>\n",
       "      <td>3.04489</td>\n",
       "      <td>8750.110359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.184658e+03</td>\n",
       "      <td>2301.171256</td>\n",
       "      <td>0.750872</td>\n",
       "      <td>0.626362</td>\n",
       "      <td>0.270651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.739922</td>\n",
       "      <td>6.196735</td>\n",
       "      <td>0.879100</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328465</th>\n",
       "      <td>328465</td>\n",
       "      <td>74.125.232.198.</td>\n",
       "      <td>147.32.84.209</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>1029</td>\n",
       "      <td>158</td>\n",
       "      <td>60.0</td>\n",
       "      <td>181.923061</td>\n",
       "      <td>33096</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>60.00</td>\n",
       "      <td>158.25</td>\n",
       "      <td>60</td>\n",
       "      <td>453</td>\n",
       "      <td>393</td>\n",
       "      <td>60</td>\n",
       "      <td>115.141178</td>\n",
       "      <td>147</td>\n",
       "      <td>93.037975</td>\n",
       "      <td>232.383842</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.538689</td>\n",
       "      <td>2.275687</td>\n",
       "      <td>0.626167</td>\n",
       "      <td>4.152185e-08</td>\n",
       "      <td>5.178685e-08</td>\n",
       "      <td>-0.994911</td>\n",
       "      <td>1.791687</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.270426</td>\n",
       "      <td>0.463169</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356503</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.016509</td>\n",
       "      <td>0.009954</td>\n",
       "      <td>0.009769</td>\n",
       "      <td>0.046300</td>\n",
       "      <td>0.046308</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.036340</td>\n",
       "      <td>0.182043</td>\n",
       "      <td>False</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-13.714278</td>\n",
       "      <td>0.695288</td>\n",
       "      <td>0.007859</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.051829</td>\n",
       "      <td>0.195399</td>\n",
       "      <td>0.06293</td>\n",
       "      <td>259.144821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.463521e+07</td>\n",
       "      <td>-349628.819380</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.561914</td>\n",
       "      <td>0.265709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.415039</td>\n",
       "      <td>28.543362</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328466</th>\n",
       "      <td>328466</td>\n",
       "      <td>147.32.84.209.</td>\n",
       "      <td>74.125.232.198</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1029</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.452222</td>\n",
       "      <td>1977</td>\n",
       "      <td>-0.313226</td>\n",
       "      <td>60.00</td>\n",
       "      <td>98.50</td>\n",
       "      <td>60</td>\n",
       "      <td>137</td>\n",
       "      <td>77</td>\n",
       "      <td>60</td>\n",
       "      <td>52.296731</td>\n",
       "      <td>34</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>93.039418</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.562402</td>\n",
       "      <td>4.386732</td>\n",
       "      <td>-1.330721</td>\n",
       "      <td>7.589765e-06</td>\n",
       "      <td>1.859105e-05</td>\n",
       "      <td>-0.298701</td>\n",
       "      <td>0.690887</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.412999</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.014293</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034607</td>\n",
       "      <td>0.034607</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.026336</td>\n",
       "      <td>0.657028</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.802027</td>\n",
       "      <td>1.520759</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.863715</td>\n",
       "      <td>1.223380</td>\n",
       "      <td>0.212232</td>\n",
       "      <td>1.52075</td>\n",
       "      <td>6116.901522</td>\n",
       "      <td>-1912.244669</td>\n",
       "      <td>7.085380e+03</td>\n",
       "      <td>476.947053</td>\n",
       "      <td>0.586177</td>\n",
       "      <td>0.551710</td>\n",
       "      <td>0.282870</td>\n",
       "      <td>0.164352</td>\n",
       "      <td>1.243080</td>\n",
       "      <td>1.189093</td>\n",
       "      <td>0.856055</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328467</th>\n",
       "      <td>328467</td>\n",
       "      <td>147.32.84.208.</td>\n",
       "      <td>61.191.61.238</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>1.313673e+09</td>\n",
       "      <td>54934</td>\n",
       "      <td>1711</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328468 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0           DST_IP          SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                0      147.32.80.9   147.32.84.165        4            2   \n",
       "1                1    147.32.84.255   147.32.84.165       38            0   \n",
       "2                2    147.32.84.255   147.32.84.165        6            0   \n",
       "3                3   147.32.84.165.  60.190.222.139        4            0   \n",
       "4                4  60.190.222.139.   147.32.84.165       10            0   \n",
       "...            ...              ...             ...      ...          ...   \n",
       "328463      328463  74.125.232.199.   147.32.84.208        8            0   \n",
       "328464      328464   147.32.84.208.  74.125.232.199        3            0   \n",
       "328465      328465  74.125.232.198.   147.32.84.209        8            0   \n",
       "328466      328466   147.32.84.209.  74.125.232.198        3            0   \n",
       "328467      328467   147.32.84.208.   61.191.61.238        1            0   \n",
       "\n",
       "        BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0         134        558  1.312967e+09  1.312967e+09        53      1025   \n",
       "1        2476          0  1.312967e+09  1.312967e+09       137       137   \n",
       "2        1160          0  1.312967e+09  1.312967e+09       138       138   \n",
       "3         249          0  1.312967e+09  1.312967e+09      1039     65520   \n",
       "4         138          0  1.312967e+09  1.312967e+09     65520      1039   \n",
       "...       ...        ...           ...           ...       ...       ...   \n",
       "328463    786          0  1.313673e+09  1.313673e+09        80      1029   \n",
       "328464     77          0  1.313673e+09  1.313673e+09      1029        80   \n",
       "328465    786          0  1.313673e+09  1.313673e+09        80      1029   \n",
       "328466     77          0  1.313673e+09  1.313673e+09      1029        80   \n",
       "328467      0          0  1.313673e+09  1.313673e+09     54934      1711   \n",
       "\n",
       "        MEAN  MEDIAN       STDEV    VAR  BURSTINESS      Q1      Q3  MIN  MAX  \\\n",
       "0        175   105.0  171.542998  29427   -0.009976   87.75  144.00   82  521   \n",
       "1        125   128.0    6.633250     44   -0.899216  128.00  128.00  110  128   \n",
       "2        253   261.0   11.874342    141   -0.910340  243.75  261.00  238  261   \n",
       "3        122    60.0  124.498996  15500    0.010138   60.00  122.25   60  309   \n",
       "4         73    60.0   20.248457    411   -0.565710   60.00   80.00   60  109   \n",
       "...      ...     ...         ...    ...         ...     ...     ...  ...  ...   \n",
       "328463   158    60.0  181.923061  33096    0.070378   60.00  158.25   60  453   \n",
       "328464    85    60.0   44.452222   1977   -0.313226   60.00   98.50   60  137   \n",
       "328465   158    60.0  181.923061  33096    0.070378   60.00  158.25   60  453   \n",
       "328466    85    60.0   44.452222   1977   -0.313226   60.00   98.50   60  137   \n",
       "328467    60    60.0   60.000000      0    0.000000   60.00   60.00   60   60   \n",
       "\n",
       "        MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                 439    82                 98.024570                 115   \n",
       "1                  18   128                  5.306600                   4   \n",
       "2                  23   261                  4.693416                  10   \n",
       "3                 249    60                102.048357                  93   \n",
       "4                  49    60                 27.737612                  16   \n",
       "...               ...   ...                       ...                 ...   \n",
       "328463            393    60                115.141178                 147   \n",
       "328464             77    60                 52.296731                  34   \n",
       "328465            393    60                115.141178                 147   \n",
       "328466             77    60                 52.296731                  34   \n",
       "328467              0    60                100.000000                   0   \n",
       "\n",
       "        PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0               65.714286        235.084382            0.833333   \n",
       "1                3.200000        125.329881            0.157895   \n",
       "2                3.952569        253.565245            0.333333   \n",
       "3               76.229508        163.003834            0.750000   \n",
       "4               21.917808         76.264015            0.600000   \n",
       "...                   ...               ...                 ...   \n",
       "328463          93.037975        232.383842            0.750000   \n",
       "328464          40.000000         93.039418            0.666667   \n",
       "328465          93.037975        232.383842            0.750000   \n",
       "328466          40.000000         93.039418            0.666667   \n",
       "328467           0.000000         60.000000            0.000000   \n",
       "\n",
       "        PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                 0.166667              0.542138              2.448366   \n",
       "1                 0.842105             -0.452267             37.236651   \n",
       "2                 0.666667             -0.673722             41.939166   \n",
       "3                 0.250000              0.497996              2.457851   \n",
       "4                 0.400000              0.642024              7.852450   \n",
       "...                    ...                   ...                   ...   \n",
       "328463            0.250000              0.538689              2.275687   \n",
       "328464            0.333333              0.562402              4.386732   \n",
       "328465            0.250000              0.538689              2.275687   \n",
       "328466            0.333333              0.562402              4.386732   \n",
       "328467            0.000000              0.000000              2.000000   \n",
       "\n",
       "        FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                   0.787684                6.603281e-08   \n",
       "1                  22.461333                5.409893e-04   \n",
       "2                  27.184983                1.990903e-04   \n",
       "3                   0.025423                1.295515e-07   \n",
       "4                   1.469404                9.636386e-05   \n",
       "...                      ...                         ...   \n",
       "328463              0.626167                4.152185e-08   \n",
       "328464             -1.330721                7.589765e-06   \n",
       "328465              0.626167                4.152185e-08   \n",
       "328466             -1.330721                7.589765e-06   \n",
       "328467             -3.000000                0.000000e+00   \n",
       "\n",
       "        FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                     9.041915e-08        -2.102222  2.796482  1.918296   \n",
       "1                     5.634804e-04         0.000000  4.164066  0.629249   \n",
       "2                     2.726156e-04        -0.072464  0.986151  0.918296   \n",
       "3                     2.243898e-07        -0.991968  1.318585  0.811278   \n",
       "4                     1.142735e-04        -0.300000  2.103153  1.370951   \n",
       "...                            ...              ...       ...       ...   \n",
       "328463                5.178685e-08        -0.994911  1.791687  0.811278   \n",
       "328464                1.859105e-05        -0.298701  0.690887  0.918296   \n",
       "328465                5.178685e-08        -0.994911  1.791687  0.811278   \n",
       "328466                1.859105e-05        -0.298701  0.690887  0.918296   \n",
       "328467               -0.000000e+00         0.000000  0.000000  0.000000   \n",
       "\n",
       "        SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0             0.742098        0.411985                  False   0.698970   \n",
       "1             0.119904        0.737466                  False   0.458925   \n",
       "2             0.355245        0.584406                  False   0.477121   \n",
       "3             0.405639        0.217397                  False   0.477121   \n",
       "4             0.412697        0.628359                  False   0.602060   \n",
       "...                ...             ...                    ...        ...   \n",
       "328463        0.270426        0.463169                  False   0.477121   \n",
       "328464        0.579380        0.535744                  False   0.477121   \n",
       "328465        0.270426        0.463169                  False   0.477121   \n",
       "328466        0.579380        0.535744                  False   0.477121   \n",
       "328467        0.000000        1.000000                  False   0.301030   \n",
       "\n",
       "        NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                       0.0               0.0           0.497004   \n",
       "1                       0.0               0.0           0.428112   \n",
       "2                       0.0               0.0           0.666641   \n",
       "3                       0.0               0.0           0.339969   \n",
       "4                       0.0               0.0           0.283852   \n",
       "...                     ...               ...                ...   \n",
       "328463                  0.0               0.0           0.363610   \n",
       "328464                  0.0               0.0           0.439609   \n",
       "328465                  0.0               0.0           0.356503   \n",
       "328466                  0.0               0.0           0.412999   \n",
       "328467                  0.0               0.0           0.500000   \n",
       "\n",
       "        AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                       0.416667          1.131372            2.255602   \n",
       "1                       0.828125          5.330970            4.501289   \n",
       "2                       0.750000          2.675396            4.012946   \n",
       "3                       0.791667          0.866065            0.611001   \n",
       "4                       0.708333          0.843926            0.313772   \n",
       "...                          ...               ...                 ...   \n",
       "328463                  0.791667          0.013187            0.008346   \n",
       "328464                  0.750000          0.011596            0.008410   \n",
       "328465                  0.791667          0.016509            0.009954   \n",
       "328466                  0.750000          0.014293            0.008271   \n",
       "328467                  0.875000          0.000000            0.000000   \n",
       "\n",
       "        Q1_SCALED_TIME  Q3_SCALED_TIME   DURATION  MEAN_DIFFTIMES  \\\n",
       "0             0.000006        2.255614   2.276383        0.455277   \n",
       "1             1.916961        8.436423  12.452268        0.336548   \n",
       "2             0.000012        4.013236   4.013247        0.802649   \n",
       "3             0.305781        2.547480   2.547480        0.849160   \n",
       "4             0.313485        0.619253   2.973125        0.330347   \n",
       "...                ...             ...        ...             ...   \n",
       "328463        0.008129        0.036260   0.036267        0.005181   \n",
       "328464        0.000000        0.026378   0.026378        0.013189   \n",
       "328465        0.009769        0.046300   0.046308        0.006615   \n",
       "328466        0.000000        0.034607   0.034607        0.017303   \n",
       "328467        0.000000        0.000000   0.000000             NaN   \n",
       "\n",
       "        MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0               0.000619       0.000006       2.254977               0.201899   \n",
       "1               0.000015       0.000000       1.763265               0.190866   \n",
       "2               0.000012       0.000011       4.012922               0.200016   \n",
       "3               0.305781       0.305220       1.936479               0.438507   \n",
       "4               0.000010       0.000007       2.353862               0.140343   \n",
       "...                  ...            ...            ...                    ...   \n",
       "328463          0.000007       0.000005       0.027907               0.185652   \n",
       "328464          0.013189       0.008410       0.017968               0.734027   \n",
       "328465          0.000008       0.000006       0.036340               0.182043   \n",
       "328466          0.017303       0.008271       0.026336               0.657028   \n",
       "328467               NaN            NaN            NaN                    NaN   \n",
       "\n",
       "        SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0            False          1.200000       False        0.0   \n",
       "1            False          0.324324       False        0.0   \n",
       "2            False          0.800000       False        0.0   \n",
       "3            False          0.666667       False        0.0   \n",
       "4            False          0.666667       False        0.0   \n",
       "...            ...               ...         ...        ...   \n",
       "328463       False          0.571429        True        0.0   \n",
       "328464       False          1.000000       False        0.0   \n",
       "328465       False          0.571429        True        0.0   \n",
       "328466       False          1.000000       False        0.0   \n",
       "328467       False          0.000000       False        0.0   \n",
       "\n",
       "        CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                       0.0               1052    0.666667        False    0   \n",
       "1                       0.0               4756    1.000000        False    0   \n",
       "2                       0.0               1520    1.000000        False    0   \n",
       "3                       0.0                489    1.000000        False    0   \n",
       "4                       0.0                738    1.000000        False    0   \n",
       "...                     ...                ...         ...          ...  ...   \n",
       "328463                  0.0               1266    1.000000        False    0   \n",
       "328464                  0.0                257    1.000000        False    0   \n",
       "328465                  0.0               1266    1.000000        False    0   \n",
       "328466                  0.0                257    1.000000        False    0   \n",
       "328467                  0.0                 60    1.000000        False    0   \n",
       "\n",
       "        TIME   MIN_POWER    MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0        0.0 -453.441475  1109.026765        0.320957        0.885171   \n",
       "1        0.0    0.000119     0.208772        0.899673        0.069644   \n",
       "2        0.0 -318.548520    35.248517        0.499956        0.010243   \n",
       "3        0.0    0.191909     0.999687        0.406980        0.006667   \n",
       "4        0.0    0.213947     0.898465        0.434197        0.999801   \n",
       "...      ...         ...          ...             ...             ...   \n",
       "328463   0.0   -4.227399     0.641747        0.007660        0.007461   \n",
       "328464   0.0   -0.813981     3.044901        0.007461        0.886561   \n",
       "328465   0.0  -13.714278     0.695288        0.007859        0.007660   \n",
       "328466   0.0    0.802027     1.520759        0.007660        0.863715   \n",
       "328467   0.0         NaN          NaN             NaN             NaN   \n",
       "\n",
       "        POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0         0.261034  18.320460     1.12845      1305.172416               NaN   \n",
       "1         0.057344   0.066138     0.00012       286.717791        923.599485   \n",
       "2         0.164230   5.155446     0.24979       821.149907               NaN   \n",
       "3         0.664189   0.305992     0.99968      3320.944008       1404.233487   \n",
       "4         0.683757   0.202726     0.89372      3418.782649       1624.178589   \n",
       "...            ...        ...         ...              ...               ...   \n",
       "328463    0.056336   0.063267     0.07496       281.679731               NaN   \n",
       "328464    1.750022   0.785367     3.04489      8750.110359               NaN   \n",
       "328465    0.051829   0.195399     0.06293       259.144821               NaN   \n",
       "328466    1.223380   0.212232     1.52075      6116.901522      -1912.244669   \n",
       "328467         NaN        NaN     0.00000              NaN               NaN   \n",
       "\n",
       "        SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0            1.385326e+07      207900.070205          0.885171   \n",
       "1            1.316854e+04        5314.872532          0.038851   \n",
       "2            1.577782e+07     -271216.457937          0.010243   \n",
       "3            6.634079e+03       -1060.499747          0.006667   \n",
       "4            1.353230e+04       -4837.676869          0.187056   \n",
       "...                   ...                ...               ...   \n",
       "328463       2.102747e+07     -309222.745769          0.007461   \n",
       "328464       8.184658e+03        2301.171256          0.750872   \n",
       "328465       2.463521e+07     -349628.819380          0.007660   \n",
       "328466       7.085380e+03         476.947053          0.586177   \n",
       "328467                NaN                NaN          0.006667   \n",
       "\n",
       "        SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0                0.205123         0.417904             NaN     4248.583372   \n",
       "1                0.241710         0.223803       -1.333903        3.640725   \n",
       "2                0.393857         0.271931             NaN      214.629001   \n",
       "3                0.407122         0.273574       -0.362061        1.505125   \n",
       "4                0.482592         0.285750       -0.098411        1.314013   \n",
       "...                   ...              ...             ...             ...   \n",
       "328463           0.570916         0.277085             NaN       11.391439   \n",
       "328464           0.626362         0.270651             NaN        1.739922   \n",
       "328465           0.561914         0.265709             NaN       13.415039   \n",
       "328466           0.551710         0.282870        0.164352        1.243080   \n",
       "328467                NaN              NaN             NaN             NaN   \n",
       "\n",
       "        SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0         4544.559854            0.564213              True   True           1  \n",
       "1            0.786195           -0.830029              True  False           0  \n",
       "2         1171.605316           -0.489713              True  False           0  \n",
       "3            2.539462           -0.400313              True  False           0  \n",
       "4            2.857491            0.565604              True  False           0  \n",
       "...               ...                 ...               ...    ...         ...  \n",
       "328463       9.477181           -0.000199              True  False           0  \n",
       "328464       6.196735            0.879100              True  False           0  \n",
       "328465      28.543362           -0.000199              True  False           0  \n",
       "328466       1.189093            0.856055              True  False           0  \n",
       "328467            NaN            0.000000             False  False           0  \n",
       "\n",
       "[328468 rows x 85 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 37546\tclear: 290922\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63898, 77)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 11804\tclear: 52094\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n=5000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    # Get and reshape confusion matrix data\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    percentage_matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
    "    return matrix, percentage_matrix\n",
    "\n",
    "def plot_confusion_matrix(matrix, percentage_matrix, model=\"\"):\n",
    "    # Build the plot\n",
    "    fig, ax = plt.subplots(1, 2,figsize=(15,5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[0])\n",
    "    sns.heatmap(percentage_matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[1])\n",
    "    # Add labels to the plot\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=5)\n",
    "    # tick_marks = np.arange(len(classifications_array)) + 0.5\n",
    "    # plt.xticks(tick_marks, classifications_array, rotation=85)\n",
    "    # plt.yticks(tick_marks, classifications_array, rotation=0)\n",
    "    ax[0].set_xlabel('Predicted label')\n",
    "    ax[0].set_ylabel('True label')\n",
    "    ax[0].set_title(f'Absolute Confusion Matrix for {model}')\n",
    "    ax[1].set_xlabel('Predicted label')\n",
    "    ax[1].set_ylabel('True label')\n",
    "    ax[1].set_title(f'Relative Confusion Matrix for {model}')\n",
    "    # pyplot.savefig(\"ddos_timeseries_plugin_classification.eps\", format=\"eps\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:43<00:00,  1.03s/trial, best loss: -0.9955487336914812]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.37594858122514, 'gamma': 2.7859153580388956, 'max_depth': 24.0, 'min_child_weight': 1.0, 'n_estimators': 180.0, 'reg_alpha': 1.0, 'reg_lambda': 0.003090768487160084}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL_code']  # Labels\n",
    "      #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "      )\n",
    "      model.fit(X_train, y_train)\n",
    "      y_pred = model.predict(X_test)\n",
    "      \n",
    "      accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "      precision = precision_score(y_test, y_pred)  * 100\n",
    "      recall = recall_score(y_test, y_pred)  * 100\n",
    "      F1 = f1_score(y_test, y_pred)  * 100\n",
    "      print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\n",
    "      matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "      plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.80 & 99.57 & 99.33 & 99.45 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "    best_results = (0,\"\",None,None, None)\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        #Split on train and test\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "        model = XGBClassifier(\n",
    "                n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "                reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "                colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "                # subsample=int(best_hyperparams['subsample']),\n",
    "                # eta=best_hyperparams['eta'],\n",
    "        )\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if best_results[0] < F1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (F1, \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1), matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "\n",
      "\n",
      "99.67884997706071\n",
      "99.88 & 99.51 & 99.85 & 99.68 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.76 & 99.67 & 99.03 & 99.35 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL_code']  # Labels\n",
    "    \n",
    "\n",
    "y_pred = best_model.predict(X_validation)\n",
    "accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "precision = precision_score(y_validation, y_pred)  * 100\n",
    "recall = recall_score(y_validation, y_pred)  * 100\n",
    "F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                \n",
    "tmp_str = \"\"\n",
    "tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "tmp_str += \"\\n\"\n",
    "tmp_str += \"\\hline\"\n",
    "tmp_str += \"\\n\"\n",
    "print(tmp_str)\n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = \"Botnet-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "f_df = feat_importances[default_faetures].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf, f_df]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\",  \"Default\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.32410914, 0.12717336, 0.08184792, 0.18369634, 0.21719658, 0.08184792]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PACKETS                  0.000000\n",
       "PEARSON_SK2_SKEWNESS     0.000000\n",
       "GALTON_SKEWNESS          0.000000\n",
       "BENFORD_LAW_PRESENTED    0.000000\n",
       "P_BENFORD                0.000000\n",
       "                           ...   \n",
       "PACKETS_REV              0.071589\n",
       "POWER_MODE               0.076939\n",
       "MEDIAN_SCALED_TIME       0.079196\n",
       "MEDIAN                   0.100581\n",
       "DIRECTIONS               0.111161\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PACKETS', 'PEARSON_SK2_SKEWNESS', 'GALTON_SKEWNESS',\n",
       "       'BENFORD_LAW_PRESENTED', 'P_BENFORD', 'AREA_VALUES_DISTRIBUTION',\n",
       "       'SPECTRAL_BANDWIDTH', 'SIG_SPACES', 'CNT_ZEROS', 'CNT_NZ_DISTRIBUTION',\n",
       "       'PERIODICITY', 'TIME', 'MAX_POWER_FREQ', 'POWER_MEAN',\n",
       "       'SPECTRAL_SKEWNESS', 'SPECTRAL_CREST', 'PEARSON_SK1_SKEWNESS',\n",
       "       'PERCENT_BELOW_MEAN', 'PERIODICITY_SCDF', 'MEAN', 'Q1_SCALED_TIME',\n",
       "       'MAX_DIFFTIMES', 'MEAN_SCALED_TIME', 'Q3_SCALED_TIME', 'SPECTRAL_FLUX',\n",
       "       'FISHER_PEARSON_g1_SKEWNESS', 'SPECTRAL_CENTROID', 'BIGGEST_CNT_1_SEC',\n",
       "       'MAX_POWER', 'TIME_DISTRIBUTION', 'SPECTRAL_SLOPE', 'SPECTRAL_ENERGY',\n",
       "       'ROOT_MEAN_SQUARE', 'COEFFICIENT_OF_VARIATION', 'NORMAL_DISTRIBUTION',\n",
       "       'MEAN_SCALED_DIFFTIMES', 'SPECTRAL_KURTOSIS', 'DURATION',\n",
       "       'SPECTRAL_ENTROPY', 'VAL', 'BURSTINESS', 'MAX', 'BYTES_REV', 'STDEV',\n",
       "       'FISHER_PEARSON_G1_SKEWNESS', 'POWER_STD', 'BYTES', 'SPECTRAL_ROLLOFF',\n",
       "       'KURTOSIS', 'MIN_POWER_FREQ', 'PERCENT_DEVIATION', 'SCALED_ENTROPY',\n",
       "       'PERCENT_ABOVE_MEAN', 'MIN_MINUS_MAX', 'MEAN_DIFFTIMES', 'MODE', 'MIN',\n",
       "       'MIN_DIFFTIMES', 'FISHER_MI_3_SKEWNESS', 'Q1', 'SPECTRAL_SPREAD',\n",
       "       'MEDIAN_DIFFTIMES', 'AVERAGE_DISPERSION', 'HURST_EXPONENT', 'VAR', 'Q3',\n",
       "       'CNT_DISTRIBUTION', 'TRANSIENTS', 'SWITCHING_METRIC', 'ENTROPY',\n",
       "       'MIN_POWER', 'PACKETS_REV', 'POWER_MODE', 'MEDIAN_SCALED_TIME',\n",
       "       'MEDIAN', 'DIRECTIONS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PACKETS': 0.0,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'P_BENFORD': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0,\n",
       " 'POWER_MEAN': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'SPECTRAL_CREST': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'PERIODICITY_SCDF': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'Q1_SCALED_TIME': 0.001156507758423686,\n",
       " 'MAX_DIFFTIMES': 0.0012333655031397939,\n",
       " 'MEAN_SCALED_TIME': 0.001279137097299099,\n",
       " 'Q3_SCALED_TIME': 0.00133511854801327,\n",
       " 'SPECTRAL_FLUX': 0.0015704219695180655,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0018422926077619195,\n",
       " 'SPECTRAL_CENTROID': 0.001946461503393948,\n",
       " 'BIGGEST_CNT_1_SEC': 0.0023123822174966335,\n",
       " 'MAX_POWER': 0.0023247115314006805,\n",
       " 'TIME_DISTRIBUTION': 0.0024147476069629192,\n",
       " 'SPECTRAL_SLOPE': 0.002582751912996173,\n",
       " 'SPECTRAL_ENERGY': 0.002669218461960554,\n",
       " 'ROOT_MEAN_SQUARE': 0.002771128201857209,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0028047978412359953,\n",
       " 'NORMAL_DISTRIBUTION': 0.0028795360121876,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0034460655879229307,\n",
       " 'SPECTRAL_KURTOSIS': 0.0034488877281546593,\n",
       " 'DURATION': 0.0036827411968261003,\n",
       " 'SPECTRAL_ENTROPY': 0.004032055381685495,\n",
       " 'VAL': 0.004065373446792364,\n",
       " 'BURSTINESS': 0.0041020154021680355,\n",
       " 'MAX': 0.004291426856070757,\n",
       " 'BYTES_REV': 0.004443619400262833,\n",
       " 'STDEV': 0.004827812779694796,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.004990797955542803,\n",
       " 'POWER_STD': 0.0056715188547968864,\n",
       " 'BYTES': 0.005815400741994381,\n",
       " 'SPECTRAL_ROLLOFF': 0.006146557629108429,\n",
       " 'KURTOSIS': 0.006326742935925722,\n",
       " 'MIN_POWER_FREQ': 0.0065362779423594475,\n",
       " 'PERCENT_DEVIATION': 0.0072006830014288425,\n",
       " 'SCALED_ENTROPY': 0.007373575586825609,\n",
       " 'PERCENT_ABOVE_MEAN': 0.008001145906746387,\n",
       " 'MIN_MINUS_MAX': 0.008533916436135769,\n",
       " 'MEAN_DIFFTIMES': 0.009084135293960571,\n",
       " 'MODE': 0.009624241851270199,\n",
       " 'MIN': 0.010267299599945545,\n",
       " 'MIN_DIFFTIMES': 0.010977406054735184,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.011310167610645294,\n",
       " 'Q1': 0.012641404755413532,\n",
       " 'SPECTRAL_SPREAD': 0.013220191933214664,\n",
       " 'MEDIAN_DIFFTIMES': 0.015783000737428665,\n",
       " 'AVERAGE_DISPERSION': 0.01658976823091507,\n",
       " 'HURST_EXPONENT': 0.019550714641809464,\n",
       " 'VAR': 0.019804572686553,\n",
       " 'Q3': 0.028165223076939583,\n",
       " 'CNT_DISTRIBUTION': 0.04113169014453888,\n",
       " 'TRANSIENTS': 0.04785085842013359,\n",
       " 'SWITCHING_METRIC': 0.05180687457323074,\n",
       " 'ENTROPY': 0.05205894261598587,\n",
       " 'MIN_POWER': 0.05660811811685562,\n",
       " 'PACKETS_REV': 0.0715889036655426,\n",
       " 'POWER_MODE': 0.0769391730427742,\n",
       " 'MEDIAN_SCALED_TIME': 0.07919587939977646,\n",
       " 'MEDIAN': 0.10058120638132095,\n",
       " 'DIRECTIONS': 0.11116108298301697}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, cycles=25):\n",
    "    best_hyperparams = tunne_together_XGBoost_classification(df_features, selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test   \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "        )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                    \n",
    "    print(\"Test: {:.2f} {:.2f} {:.2f} {:.2f} \\n\".format(accuracy,precision,recall,F1))\n",
    "    \n",
    "    return accuracy, precision, recall, F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:02<00:00,  1.59trial/s, best loss: -0.9949285384970031]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9531361440228374, 'gamma': 3.5053478862838263, 'max_depth': 14.0, 'min_child_weight': 5.0, 'n_estimators': 260.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6356982236437059}\n",
      "Test: 99.84 99.68 99.46 99.57 \n",
      "\n",
      "100%|██████████| 100/100 [00:53<00:00,  1.88trial/s, best loss: -0.9952446694278265]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.40556788540178146, 'gamma': 2.444491583415006, 'max_depth': 14.0, 'min_child_weight': 5.0, 'n_estimators': 280.0, 'reg_alpha': 17.0, 'reg_lambda': 0.3599478848796673}\n",
      "Test: 99.78 99.46 99.35 99.41 \n",
      "\n",
      "100%|██████████| 100/100 [01:08<00:00,  1.45trial/s, best loss: -0.9932515337423312]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.024709163740587112, 'gamma': 4.70082713804457, 'max_depth': 30.0, 'min_child_weight': 15.0, 'n_estimators': 300.0, 'reg_alpha': 1.0, 'reg_lambda': 0.5654082487809409}\n",
      "Test: 99.72 99.35 99.14 99.24 \n",
      "\n",
      "100%|██████████| 100/100 [00:44<00:00,  2.27trial/s, best loss: -0.9969334559950935]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5451412766782127, 'gamma': 1.7038310116702506, 'max_depth': 10.0, 'min_child_weight': 1.0, 'n_estimators': 280.0, 'reg_alpha': 0.0, 'reg_lambda': 0.09839799527883951}\n",
      "Test: 99.86 99.68 99.57 99.62 \n",
      "\n",
      "100%|██████████| 100/100 [01:33<00:00,  1.07trial/s, best loss: -0.9943207981580966]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4584647562979414, 'gamma': 1.012695602765504, 'max_depth': 28.0, 'min_child_weight': 12.0, 'n_estimators': 180.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7530645395654819}\n",
      "Test: 99.80 99.57 99.35 99.46 \n",
      "\n",
      "100%|██████████| 100/100 [01:23<00:00,  1.20trial/s, best loss: -0.9976983274512812]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9693381998589374, 'gamma': 1.1270103075876419, 'max_depth': 30.0, 'min_child_weight': 10.0, 'n_estimators': 240.0, 'reg_alpha': 1.0, 'reg_lambda': 0.5684595868112666}\n",
      "Test: 99.84 99.68 99.46 99.57 \n",
      "\n",
      "100%|██████████| 100/100 [00:21<00:00,  4.55trial/s, best loss: -0.939144471347861]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6947295664347019, 'gamma': 6.633467944190843, 'max_depth': 18.0, 'min_child_weight': 3.0, 'n_estimators': 80.0, 'reg_alpha': 2.0, 'reg_lambda': 0.544830411071422}\n",
      "Test: 99.82 99.67 99.35 99.51 \n",
      "\n",
      "100%|██████████| 100/100 [01:14<00:00,  1.35trial/s, best loss: -0.9966236955187231]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6302085567640651, 'gamma': 5.43458111880356, 'max_depth': 6.0, 'min_child_weight': 8.0, 'n_estimators': 260.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6931893594564914}\n",
      "Test: 99.80 99.67 99.24 99.46 \n",
      "\n",
      "100%|██████████| 100/100 [01:13<00:00,  1.37trial/s, best loss: -0.9957028852056476]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9724482063737369, 'gamma': 8.643361911856786, 'max_depth': 30.0, 'min_child_weight': 0.0, 'n_estimators': 220.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9636992285234933}\n",
      "Test: 99.84 99.68 99.46 99.57 \n",
      "\n",
      "100%|██████████| 100/100 [01:01<00:00,  1.64trial/s, best loss: -0.9966257668711657]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6044033370440018, 'gamma': 2.82561960285966, 'max_depth': 30.0, 'min_child_weight': 8.0, 'n_estimators': 320.0, 'reg_alpha': 1.0, 'reg_lambda': 0.40461898277433483}\n",
      "Test: 99.82 99.67 99.35 99.51 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.botnet_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.doh_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dos_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dns_malware_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.tor_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.vpn_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.all_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_2_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.crypto_features)\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"Botnet-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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