botnet_mutliclass.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Botnet multiclass classification using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: mutliclass calssification 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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\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",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\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 sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "import lightgbm as lgb\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",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/botnet_multiclass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CNT_ZEROS</th>\n",
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       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
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       "      <th>SPECTRAL_BANDWIDTH</th>\n",
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       "      <th>LABEL</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147.32.80.9</td>\n",
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       "      <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",
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       "      <td>82</td>\n",
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       "      <td>1.918296</td>\n",
       "      <td>0.742098</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.000006</td>\n",
       "      <td>2.254977</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1109.026765</td>\n",
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       "      <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",
       "      <td>0.417904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4248.583372</td>\n",
       "      <td>4544.559854</td>\n",
       "      <td>0.564213</td>\n",
       "      <td>0.003325</td>\n",
       "      <td>neris</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>147.32.84.165</td>\n",
       "      <td>38</td>\n",
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       "      <td>1.312967e+09</td>\n",
       "      <td>1.312967e+09</td>\n",
       "      <td>137</td>\n",
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       "      <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>0.000000</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>0.046606</td>\n",
       "      <td>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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",
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       "      <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",
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       "      <td>0.000012</td>\n",
       "      <td>4.013236</td>\n",
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       "      <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>0.000000</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>0.011965</td>\n",
       "      <td>clear</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
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       "      <td>60.00</td>\n",
       "      <td>122.25</td>\n",
       "      <td>60</td>\n",
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       "      <td>102.048357</td>\n",
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       "      <td>76.229508</td>\n",
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       "      <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>0.000000</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>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>0.000000</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>clear</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>176635</th>\n",
       "      <td>11618</td>\n",
       "      <td>11618</td>\n",
       "      <td>64.142.97.114.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313558e+09</td>\n",
       "      <td>1.313558e+09</td>\n",
       "      <td>20346</td>\n",
       "      <td>10000</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</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>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</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>120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</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>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>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176636</th>\n",
       "      <td>11619</td>\n",
       "      <td>11619</td>\n",
       "      <td>147.32.84.165.</td>\n",
       "      <td>95.130.170.231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313563e+09</td>\n",
       "      <td>1.313563e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>29083</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>0.000000</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>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176637</th>\n",
       "      <td>11620</td>\n",
       "      <td>11620</td>\n",
       "      <td>95.130.170.231.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313563e+09</td>\n",
       "      <td>1.313563e+09</td>\n",
       "      <td>29083</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</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>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000005</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1.000000</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>120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</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>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>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176638</th>\n",
       "      <td>11621</td>\n",
       "      <td>11621</td>\n",
       "      <td>147.32.84.165.</td>\n",
       "      <td>199.59.164.114</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313565e+09</td>\n",
       "      <td>1.313565e+09</td>\n",
       "      <td>40924</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</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>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000017</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>1.000000</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>120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</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>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>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176639</th>\n",
       "      <td>11622</td>\n",
       "      <td>11622</td>\n",
       "      <td>199.59.164.114.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313565e+09</td>\n",
       "      <td>1.313565e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>40924</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</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>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.522727</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.497175</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>240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000009</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>clear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>176640 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0.1  Unnamed: 0           DST_IP          SRC_IP  PACKETS  \\\n",
       "0                  0           0      147.32.80.9   147.32.84.165        4   \n",
       "1                  1           1    147.32.84.255   147.32.84.165       38   \n",
       "2                  2           2    147.32.84.255   147.32.84.165        6   \n",
       "3                  3           3   147.32.84.165.  60.190.222.139        4   \n",
       "4                  4           4  60.190.222.139.   147.32.84.165       10   \n",
       "...              ...         ...              ...             ...      ...   \n",
       "176635         11618       11618   64.142.97.114.   147.32.84.165        2   \n",
       "176636         11619       11619   147.32.84.165.  95.130.170.231        1   \n",
       "176637         11620       11620  95.130.170.231.   147.32.84.165        2   \n",
       "176638         11621       11621   147.32.84.165.  199.59.164.114        2   \n",
       "176639         11622       11622  199.59.164.114.   147.32.84.165        4   \n",
       "\n",
       "        PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  \\\n",
       "0                 2    134        558  1.312967e+09  1.312967e+09        53   \n",
       "1                 0   2476          0  1.312967e+09  1.312967e+09       137   \n",
       "2                 0   1160          0  1.312967e+09  1.312967e+09       138   \n",
       "3                 0    249          0  1.312967e+09  1.312967e+09      1039   \n",
       "4                 0    138          0  1.312967e+09  1.312967e+09     65520   \n",
       "...             ...    ...        ...           ...           ...       ...   \n",
       "176635            0      0          0  1.313558e+09  1.313558e+09     20346   \n",
       "176636            0      0          0  1.313563e+09  1.313563e+09        80   \n",
       "176637            0      0          0  1.313563e+09  1.313563e+09     29083   \n",
       "176638            0      0          0  1.313565e+09  1.313565e+09     40924   \n",
       "176639            0      0          0  1.313565e+09  1.313565e+09        80   \n",
       "\n",
       "        SRC_PORT  MEAN  MEDIAN       STDEV    VAR  BURSTINESS      Q1      Q3  \\\n",
       "0           1025   175   105.0  171.542998  29427   -0.009976   87.75  144.00   \n",
       "1            137   125   128.0    6.633250     44   -0.899216  128.00  128.00   \n",
       "2            138   253   261.0   11.874342    141   -0.910340  243.75  261.00   \n",
       "3          65520   122    60.0  124.498996  15500    0.010138   60.00  122.25   \n",
       "4           1039    73    60.0   20.248457    411   -0.565710   60.00   80.00   \n",
       "...          ...   ...     ...         ...    ...         ...     ...     ...   \n",
       "176635     10000    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "176636     29083    60    60.0   60.000000      0    0.000000   60.00   60.00   \n",
       "176637        80    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "176638        80    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "176639     40924    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "\n",
       "        MIN  MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0        82  521            439    82                 98.024570   \n",
       "1       110  128             18   128                  5.306600   \n",
       "2       238  261             23   261                  4.693416   \n",
       "3        60  309            249    60                102.048357   \n",
       "4        60  109             49    60                 27.737612   \n",
       "...     ...  ...            ...   ...                       ...   \n",
       "176635   60   60              0    60                  0.000000   \n",
       "176636   60   60              0    60                100.000000   \n",
       "176637   60   60              0    60                  0.000000   \n",
       "176638   60   60              0    60                  0.000000   \n",
       "176639   60   60              0    60                  0.000000   \n",
       "\n",
       "        AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                      115          65.714286        235.084382   \n",
       "1                        4           3.200000        125.329881   \n",
       "2                       10           3.952569        253.565245   \n",
       "3                       93          76.229508        163.003834   \n",
       "4                       16          21.917808         76.264015   \n",
       "...                    ...                ...               ...   \n",
       "176635                   0           0.000000         60.000000   \n",
       "176636                   0           0.000000         60.000000   \n",
       "176637                   0           0.000000         60.000000   \n",
       "176638                   0           0.000000         60.000000   \n",
       "176639                   0           0.000000         60.000000   \n",
       "\n",
       "        PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                 0.833333            0.166667              0.542138   \n",
       "1                 0.157895            0.842105             -0.452267   \n",
       "2                 0.333333            0.666667             -0.673722   \n",
       "3                 0.750000            0.250000              0.497996   \n",
       "4                 0.600000            0.400000              0.642024   \n",
       "...                    ...                 ...                   ...   \n",
       "176635            0.000000            0.000000              0.000000   \n",
       "176636            0.000000            0.000000              0.000000   \n",
       "176637            0.000000            0.000000              0.000000   \n",
       "176638            0.000000            0.000000              0.000000   \n",
       "176639            0.000000            0.000000              0.000000   \n",
       "\n",
       "        PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                   2.448366              0.787684   \n",
       "1                  37.236651             22.461333   \n",
       "2                  41.939166             27.184983   \n",
       "3                   2.457851              0.025423   \n",
       "4                   7.852450              1.469404   \n",
       "...                      ...                   ...   \n",
       "176635              0.000000              0.000000   \n",
       "176636              2.000000             -3.000000   \n",
       "176637              0.000000              0.000000   \n",
       "176638              0.000000              0.000000   \n",
       "176639              0.000000              0.000000   \n",
       "\n",
       "        FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                     6.603281e-08                9.041915e-08   \n",
       "1                     5.409893e-04                5.634804e-04   \n",
       "2                     1.990903e-04                2.726156e-04   \n",
       "3                     1.295515e-07                2.243898e-07   \n",
       "4                     9.636386e-05                1.142735e-04   \n",
       "...                            ...                         ...   \n",
       "176635                0.000000e+00                0.000000e+00   \n",
       "176636                0.000000e+00               -0.000000e+00   \n",
       "176637                0.000000e+00                0.000000e+00   \n",
       "176638                0.000000e+00                0.000000e+00   \n",
       "176639                0.000000e+00                0.000000e+00   \n",
       "\n",
       "        GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0             -2.102222  2.796482  1.918296        0.742098        0.411985   \n",
       "1              0.000000  4.164066  0.629249        0.119904        0.737466   \n",
       "2             -0.072464  0.986151  0.918296        0.355245        0.584406   \n",
       "3             -0.991968  1.318585  0.811278        0.405639        0.217397   \n",
       "4             -0.300000  2.103153  1.370951        0.412697        0.628359   \n",
       "...                 ...       ...       ...             ...             ...   \n",
       "176635         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176636         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176637         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176638         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176639         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "\n",
       "        BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                       False   0.698970                  0.0   \n",
       "1                       False   0.458925                  0.0   \n",
       "2                       False   0.477121                  0.0   \n",
       "3                       False   0.477121                  0.0   \n",
       "4                       False   0.602060                  0.0   \n",
       "...                       ...        ...                  ...   \n",
       "176635                  False   0.301030                  0.0   \n",
       "176636                  False   0.301030                  0.0   \n",
       "176637                  False   0.301030                  0.0   \n",
       "176638                  False   0.301030                  0.0   \n",
       "176639                  False   0.301030                  0.0   \n",
       "\n",
       "        CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                    0.0           0.497004                  0.416667   \n",
       "1                    0.0           0.428112                  0.828125   \n",
       "2                    0.0           0.666641                  0.750000   \n",
       "3                    0.0           0.339969                  0.791667   \n",
       "4                    0.0           0.283852                  0.708333   \n",
       "...                  ...                ...                       ...   \n",
       "176635               0.0           0.500000                  0.875000   \n",
       "176636               0.0                NaN                  0.875000   \n",
       "176637               0.0           0.500000                  0.875000   \n",
       "176638               0.0           0.500000                  0.875000   \n",
       "176639               0.0           0.522727                  0.875000   \n",
       "\n",
       "        MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               1.131372            2.255602        0.000006        2.255614   \n",
       "1               5.330970            4.501289        1.916961        8.436423   \n",
       "2               2.675396            4.012946        0.000012        4.013236   \n",
       "3               0.866065            0.611001        0.305781        2.547480   \n",
       "4               0.843926            0.313772        0.313485        0.619253   \n",
       "...                  ...                 ...             ...             ...   \n",
       "176635          0.000005            0.000010        0.000000        0.000010   \n",
       "176636          0.000000            0.000000        0.000000        0.000000   \n",
       "176637          0.000005            0.000009        0.000000        0.000009   \n",
       "176638          0.000017            0.000033        0.000000        0.000033   \n",
       "176639          0.000022            0.000037        0.000009        0.000042   \n",
       "\n",
       "         DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        2.276383        0.455277          0.000619       0.000006   \n",
       "1       12.452268        0.336548          0.000015       0.000000   \n",
       "2        4.013247        0.802649          0.000012       0.000011   \n",
       "3        2.547480        0.849160          0.305781       0.305220   \n",
       "4        2.973125        0.330347          0.000010       0.000007   \n",
       "...           ...             ...               ...            ...   \n",
       "176635   0.000010        0.000010          0.000010       0.000010   \n",
       "176636   0.000000             NaN               NaN            NaN   \n",
       "176637   0.000009        0.000009          0.000009       0.000009   \n",
       "176638   0.000033        0.000033          0.000033       0.000033   \n",
       "176639   0.000042        0.000014          0.000009       0.000005   \n",
       "\n",
       "        MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            2.254977               0.201899       False          1.200000   \n",
       "1            1.763265               0.190866       False          0.324324   \n",
       "2            4.012922               0.200016       False          0.800000   \n",
       "3            1.936479               0.438507       False          0.666667   \n",
       "4            2.353862               0.140343       False          0.666667   \n",
       "...               ...                    ...         ...               ...   \n",
       "176635       0.000010               1.000000       False          0.000000   \n",
       "176636            NaN                    NaN       False          0.000000   \n",
       "176637       0.000009               1.000000       False          0.000000   \n",
       "176638       0.000033               1.000000       False          0.000000   \n",
       "176639       0.000028               0.497175       False          0.000000   \n",
       "\n",
       "        TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0            False        0.0                  0.0               1052   \n",
       "1            False        0.0                  0.0               4756   \n",
       "2            False        0.0                  0.0               1520   \n",
       "3            False        0.0                  0.0                489   \n",
       "4            False        0.0                  0.0                738   \n",
       "...            ...        ...                  ...                ...   \n",
       "176635       False        0.0                  0.0                120   \n",
       "176636       False        0.0                  0.0                 60   \n",
       "176637       False        0.0                  0.0                120   \n",
       "176638       False        0.0                  0.0                120   \n",
       "176639       False        0.0                  0.0                240   \n",
       "\n",
       "        DIRECTIONS  PERIODICITY  VAL      TIME   MIN_POWER    MAX_POWER  \\\n",
       "0         0.666667        False    0  0.000000 -453.441475  1109.026765   \n",
       "1         1.000000        False    0  0.000000    0.000119     0.208772   \n",
       "2         1.000000        False    0  0.000000 -318.548520    35.248517   \n",
       "3         1.000000        False    0  0.000000    0.191909     0.999687   \n",
       "4         1.000000        False    0  0.000000    0.213947     0.898465   \n",
       "...            ...          ...  ...       ...         ...          ...   \n",
       "176635    1.000000        False    0  0.000000         NaN          NaN   \n",
       "176636    1.000000        False    0  0.000000         NaN          NaN   \n",
       "176637    1.000000        False    0  0.000000         NaN          NaN   \n",
       "176638    1.000000        False    0  0.000000         NaN          NaN   \n",
       "176639    1.000000         True   60  0.000009         NaN          NaN   \n",
       "\n",
       "        MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  \\\n",
       "0             0.320957        0.885171    0.261034  18.320460     1.12845   \n",
       "1             0.899673        0.069644    0.057344   0.066138     0.00012   \n",
       "2             0.499956        0.010243    0.164230   5.155446     0.24979   \n",
       "3             0.406980        0.006667    0.664189   0.305992     0.99968   \n",
       "4             0.434197        0.999801    0.683757   0.202726     0.89372   \n",
       "...                ...             ...         ...        ...         ...   \n",
       "176635             NaN             NaN         NaN        NaN     0.00000   \n",
       "176636             NaN             NaN         NaN        NaN     0.00000   \n",
       "176637             NaN             NaN         NaN        NaN     0.00000   \n",
       "176638             NaN             NaN         NaN        NaN     0.00000   \n",
       "176639             NaN             NaN         NaN        NaN     0.00000   \n",
       "\n",
       "        SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0           1305.172416               NaN       1.385326e+07   \n",
       "1            286.717791        923.599485       1.316854e+04   \n",
       "2            821.149907               NaN       1.577782e+07   \n",
       "3           3320.944008       1404.233487       6.634079e+03   \n",
       "4           3418.782649       1624.178589       1.353230e+04   \n",
       "...                 ...               ...                ...   \n",
       "176635              NaN               NaN                NaN   \n",
       "176636              NaN               NaN                NaN   \n",
       "176637              NaN               NaN                NaN   \n",
       "176638              NaN               NaN                NaN   \n",
       "176639              NaN               NaN                NaN   \n",
       "\n",
       "        SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           207900.070205          0.885171           0.205123   \n",
       "1             5314.872532          0.038851           0.241710   \n",
       "2          -271216.457937          0.010243           0.393857   \n",
       "3            -1060.499747          0.006667           0.407122   \n",
       "4            -4837.676869          0.187056           0.482592   \n",
       "...                   ...               ...                ...   \n",
       "176635                NaN          0.006667                NaN   \n",
       "176636                NaN          0.006667                NaN   \n",
       "176637                NaN          0.006667                NaN   \n",
       "176638                NaN          0.006667                NaN   \n",
       "176639                NaN          0.006667                NaN   \n",
       "\n",
       "        SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0              0.417904             NaN     4248.583372    4544.559854   \n",
       "1              0.223803       -1.333903        3.640725       0.786195   \n",
       "2              0.271931             NaN      214.629001    1171.605316   \n",
       "3              0.273574       -0.362061        1.505125       2.539462   \n",
       "4              0.285750       -0.098411        1.314013       2.857491   \n",
       "...                 ...             ...             ...            ...   \n",
       "176635              NaN             NaN             NaN            NaN   \n",
       "176636              NaN             NaN             NaN            NaN   \n",
       "176637              NaN             NaN             NaN            NaN   \n",
       "176638              NaN             NaN             NaN            NaN   \n",
       "176639              NaN             NaN             NaN            NaN   \n",
       "\n",
       "        SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 0.564213          0.003325  neris  \n",
       "1                -0.830029          0.046606  clear  \n",
       "2                -0.489713          0.011965  clear  \n",
       "3                -0.400313          0.010620  clear  \n",
       "4                 0.565604          0.021624  clear  \n",
       "...                    ...               ...    ...  \n",
       "176635            0.000000               NaN  clear  \n",
       "176636            0.000000               NaN  clear  \n",
       "176637            0.000000               NaN  clear  \n",
       "176638            0.000000               NaN  clear  \n",
       "176639            0.000000               NaN  clear  \n",
       "\n",
       "[176640 rows x 85 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
    "    \"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",
    "    \"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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "\n",
    "def handle_df(df):\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')\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = handle_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</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",
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       "      <td>115</td>\n",
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       "      <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",
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       "      <td>9.041915e-08</td>\n",
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       "      <td>1.918296</td>\n",
       "      <td>0.742098</td>\n",
       "      <td>0.411985</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698970</td>\n",
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       "      <td>0.000006</td>\n",
       "      <td>2.254977</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1052</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1109.026765</td>\n",
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       "      <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>-1.000000</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>neris</td>\n",
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       "      <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>0.000000</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>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>0.000000</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>-1.000000</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>-1.000000</td>\n",
       "      <td>214.629001</td>\n",
       "      <td>1171.605316</td>\n",
       "      <td>-0.489713</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>0.000000</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>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>0.000000</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>clear</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176635</th>\n",
       "      <td>11618</td>\n",
       "      <td>11618</td>\n",
       "      <td>64.142.97.114.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313558e+09</td>\n",
       "      <td>1.313558e+09</td>\n",
       "      <td>20346</td>\n",
       "      <td>10000</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</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>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</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>120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176636</th>\n",
       "      <td>11619</td>\n",
       "      <td>11619</td>\n",
       "      <td>147.32.84.165.</td>\n",
       "      <td>95.130.170.231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.313563e+09</td>\n",
       "      <td>1.313563e+09</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>60</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176637</th>\n",
       "      <td>11620</td>\n",
       "      <td>11620</td>\n",
       "      <td>95.130.170.231.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.313563e+09</td>\n",
       "      <td>1.313563e+09</td>\n",
       "      <td>29083</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176638</th>\n",
       "      <td>11621</td>\n",
       "      <td>11621</td>\n",
       "      <td>147.32.84.165.</td>\n",
       "      <td>199.59.164.114</td>\n",
       "      <td>2</td>\n",
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       "      <td>1.313565e+09</td>\n",
       "      <td>1.313565e+09</td>\n",
       "      <td>40924</td>\n",
       "      <td>80</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176639</th>\n",
       "      <td>11622</td>\n",
       "      <td>11622</td>\n",
       "      <td>199.59.164.114.</td>\n",
       "      <td>147.32.84.165</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.313565e+09</td>\n",
       "      <td>1.313565e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>40924</td>\n",
       "      <td>60</td>\n",
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       "      <td>0.000005</td>\n",
       "      <td>0.000028</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>240</td>\n",
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       "      <td>True</td>\n",
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       "      <td>-1.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>176640 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0.1  Unnamed: 0           DST_IP          SRC_IP  PACKETS  \\\n",
       "0                  0           0      147.32.80.9   147.32.84.165        4   \n",
       "1                  1           1    147.32.84.255   147.32.84.165       38   \n",
       "2                  2           2    147.32.84.255   147.32.84.165        6   \n",
       "3                  3           3   147.32.84.165.  60.190.222.139        4   \n",
       "4                  4           4  60.190.222.139.   147.32.84.165       10   \n",
       "...              ...         ...              ...             ...      ...   \n",
       "176635         11618       11618   64.142.97.114.   147.32.84.165        2   \n",
       "176636         11619       11619   147.32.84.165.  95.130.170.231        1   \n",
       "176637         11620       11620  95.130.170.231.   147.32.84.165        2   \n",
       "176638         11621       11621   147.32.84.165.  199.59.164.114        2   \n",
       "176639         11622       11622  199.59.164.114.   147.32.84.165        4   \n",
       "\n",
       "        PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  \\\n",
       "0                 2    134        558  1.312967e+09  1.312967e+09        53   \n",
       "1                 0   2476          0  1.312967e+09  1.312967e+09       137   \n",
       "2                 0   1160          0  1.312967e+09  1.312967e+09       138   \n",
       "3                 0    249          0  1.312967e+09  1.312967e+09      1039   \n",
       "4                 0    138          0  1.312967e+09  1.312967e+09     65520   \n",
       "...             ...    ...        ...           ...           ...       ...   \n",
       "176635            0      0          0  1.313558e+09  1.313558e+09     20346   \n",
       "176636            0      0          0  1.313563e+09  1.313563e+09        80   \n",
       "176637            0      0          0  1.313563e+09  1.313563e+09     29083   \n",
       "176638            0      0          0  1.313565e+09  1.313565e+09     40924   \n",
       "176639            0      0          0  1.313565e+09  1.313565e+09        80   \n",
       "\n",
       "        SRC_PORT  MEAN  MEDIAN       STDEV    VAR  BURSTINESS      Q1      Q3  \\\n",
       "0           1025   175   105.0  171.542998  29427   -0.009976   87.75  144.00   \n",
       "1            137   125   128.0    6.633250     44   -0.899216  128.00  128.00   \n",
       "2            138   253   261.0   11.874342    141   -0.910340  243.75  261.00   \n",
       "3          65520   122    60.0  124.498996  15500    0.010138   60.00  122.25   \n",
       "4           1039    73    60.0   20.248457    411   -0.565710   60.00   80.00   \n",
       "...          ...   ...     ...         ...    ...         ...     ...     ...   \n",
       "176635     10000    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "176636     29083    60    60.0   60.000000      0    0.000000   60.00   60.00   \n",
       "176637        80    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "176638        80    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "176639     40924    60    60.0    0.000000      0   -1.000000   60.00   60.00   \n",
       "\n",
       "        MIN  MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0        82  521            439    82                 98.024570   \n",
       "1       110  128             18   128                  5.306600   \n",
       "2       238  261             23   261                  4.693416   \n",
       "3        60  309            249    60                102.048357   \n",
       "4        60  109             49    60                 27.737612   \n",
       "...     ...  ...            ...   ...                       ...   \n",
       "176635   60   60              0    60                  0.000000   \n",
       "176636   60   60              0    60                100.000000   \n",
       "176637   60   60              0    60                  0.000000   \n",
       "176638   60   60              0    60                  0.000000   \n",
       "176639   60   60              0    60                  0.000000   \n",
       "\n",
       "        AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                      115          65.714286        235.084382   \n",
       "1                        4           3.200000        125.329881   \n",
       "2                       10           3.952569        253.565245   \n",
       "3                       93          76.229508        163.003834   \n",
       "4                       16          21.917808         76.264015   \n",
       "...                    ...                ...               ...   \n",
       "176635                   0           0.000000         60.000000   \n",
       "176636                   0           0.000000         60.000000   \n",
       "176637                   0           0.000000         60.000000   \n",
       "176638                   0           0.000000         60.000000   \n",
       "176639                   0           0.000000         60.000000   \n",
       "\n",
       "        PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                 0.833333            0.166667              0.542138   \n",
       "1                 0.157895            0.842105             -0.452267   \n",
       "2                 0.333333            0.666667             -0.673722   \n",
       "3                 0.750000            0.250000              0.497996   \n",
       "4                 0.600000            0.400000              0.642024   \n",
       "...                    ...                 ...                   ...   \n",
       "176635            0.000000            0.000000              0.000000   \n",
       "176636            0.000000            0.000000              0.000000   \n",
       "176637            0.000000            0.000000              0.000000   \n",
       "176638            0.000000            0.000000              0.000000   \n",
       "176639            0.000000            0.000000              0.000000   \n",
       "\n",
       "        PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                   2.448366              0.787684   \n",
       "1                  37.236651             22.461333   \n",
       "2                  41.939166             27.184983   \n",
       "3                   2.457851              0.025423   \n",
       "4                   7.852450              1.469404   \n",
       "...                      ...                   ...   \n",
       "176635              0.000000              0.000000   \n",
       "176636              2.000000             -3.000000   \n",
       "176637              0.000000              0.000000   \n",
       "176638              0.000000              0.000000   \n",
       "176639              0.000000              0.000000   \n",
       "\n",
       "        FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                     6.603281e-08                9.041915e-08   \n",
       "1                     5.409893e-04                5.634804e-04   \n",
       "2                     1.990903e-04                2.726156e-04   \n",
       "3                     1.295515e-07                2.243898e-07   \n",
       "4                     9.636386e-05                1.142735e-04   \n",
       "...                            ...                         ...   \n",
       "176635                0.000000e+00                0.000000e+00   \n",
       "176636                0.000000e+00               -0.000000e+00   \n",
       "176637                0.000000e+00                0.000000e+00   \n",
       "176638                0.000000e+00                0.000000e+00   \n",
       "176639                0.000000e+00                0.000000e+00   \n",
       "\n",
       "        GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0             -2.102222  2.796482  1.918296        0.742098        0.411985   \n",
       "1              0.000000  4.164066  0.629249        0.119904        0.737466   \n",
       "2             -0.072464  0.986151  0.918296        0.355245        0.584406   \n",
       "3             -0.991968  1.318585  0.811278        0.405639        0.217397   \n",
       "4             -0.300000  2.103153  1.370951        0.412697        0.628359   \n",
       "...                 ...       ...       ...             ...             ...   \n",
       "176635         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176636         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176637         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176638         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "176639         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "\n",
       "        BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                       False   0.698970                  0.0   \n",
       "1                       False   0.458925                  0.0   \n",
       "2                       False   0.477121                  0.0   \n",
       "3                       False   0.477121                  0.0   \n",
       "4                       False   0.602060                  0.0   \n",
       "...                       ...        ...                  ...   \n",
       "176635                  False   0.301030                  0.0   \n",
       "176636                  False   0.301030                  0.0   \n",
       "176637                  False   0.301030                  0.0   \n",
       "176638                  False   0.301030                  0.0   \n",
       "176639                  False   0.301030                  0.0   \n",
       "\n",
       "        CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                    0.0           0.497004                  0.416667   \n",
       "1                    0.0           0.428112                  0.828125   \n",
       "2                    0.0           0.666641                  0.750000   \n",
       "3                    0.0           0.339969                  0.791667   \n",
       "4                    0.0           0.283852                  0.708333   \n",
       "...                  ...                ...                       ...   \n",
       "176635               0.0           0.500000                  0.875000   \n",
       "176636               0.0           0.500000                  0.875000   \n",
       "176637               0.0           0.500000                  0.875000   \n",
       "176638               0.0           0.500000                  0.875000   \n",
       "176639               0.0           0.522727                  0.875000   \n",
       "\n",
       "        MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               1.131372            2.255602        0.000006        2.255614   \n",
       "1               5.330970            4.501289        1.916961        8.436423   \n",
       "2               2.675396            4.012946        0.000012        4.013236   \n",
       "3               0.866065            0.611001        0.305781        2.547480   \n",
       "4               0.843926            0.313772        0.313485        0.619253   \n",
       "...                  ...                 ...             ...             ...   \n",
       "176635          0.000005            0.000010        0.000000        0.000010   \n",
       "176636          0.000000            0.000000        0.000000        0.000000   \n",
       "176637          0.000005            0.000009        0.000000        0.000009   \n",
       "176638          0.000017            0.000033        0.000000        0.000033   \n",
       "176639          0.000022            0.000037        0.000009        0.000042   \n",
       "\n",
       "         DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        2.276383        0.455277          0.000619       0.000006   \n",
       "1       12.452268        0.336548          0.000015       0.000000   \n",
       "2        4.013247        0.802649          0.000012       0.000011   \n",
       "3        2.547480        0.849160          0.305781       0.305220   \n",
       "4        2.973125        0.330347          0.000010       0.000007   \n",
       "...           ...             ...               ...            ...   \n",
       "176635   0.000010        0.000010          0.000010       0.000010   \n",
       "176636   0.000000        0.000000          0.000000       0.000000   \n",
       "176637   0.000009        0.000009          0.000009       0.000009   \n",
       "176638   0.000033        0.000033          0.000033       0.000033   \n",
       "176639   0.000042        0.000014          0.000009       0.000005   \n",
       "\n",
       "        MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            2.254977               0.201899       False          1.200000   \n",
       "1            1.763265               0.190866       False          0.324324   \n",
       "2            4.012922               0.200016       False          0.800000   \n",
       "3            1.936479               0.438507       False          0.666667   \n",
       "4            2.353862               0.140343       False          0.666667   \n",
       "...               ...                    ...         ...               ...   \n",
       "176635       0.000010               1.000000       False          0.000000   \n",
       "176636       0.000000               0.000000       False          0.000000   \n",
       "176637       0.000009               1.000000       False          0.000000   \n",
       "176638       0.000033               1.000000       False          0.000000   \n",
       "176639       0.000028               0.497175       False          0.000000   \n",
       "\n",
       "        TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0            False        0.0                  0.0               1052   \n",
       "1            False        0.0                  0.0               4756   \n",
       "2            False        0.0                  0.0               1520   \n",
       "3            False        0.0                  0.0                489   \n",
       "4            False        0.0                  0.0                738   \n",
       "...            ...        ...                  ...                ...   \n",
       "176635       False        0.0                  0.0                120   \n",
       "176636       False        0.0                  0.0                 60   \n",
       "176637       False        0.0                  0.0                120   \n",
       "176638       False        0.0                  0.0                120   \n",
       "176639       False        0.0                  0.0                240   \n",
       "\n",
       "        DIRECTIONS  PERIODICITY  VAL      TIME   MIN_POWER    MAX_POWER  \\\n",
       "0         0.666667        False    0  0.000000 -453.441475  1109.026765   \n",
       "1         1.000000        False    0  0.000000    0.000119     0.208772   \n",
       "2         1.000000        False    0  0.000000 -318.548520    35.248517   \n",
       "3         1.000000        False    0  0.000000    0.191909     0.999687   \n",
       "4         1.000000        False    0  0.000000    0.213947     0.898465   \n",
       "...            ...          ...  ...       ...         ...          ...   \n",
       "176635    1.000000        False    0  0.000000   -1.000000    -1.000000   \n",
       "176636    1.000000        False    0  0.000000   -1.000000    -1.000000   \n",
       "176637    1.000000        False    0  0.000000   -1.000000    -1.000000   \n",
       "176638    1.000000        False    0  0.000000   -1.000000    -1.000000   \n",
       "176639    1.000000         True   60  0.000009   -1.000000    -1.000000   \n",
       "\n",
       "        MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  \\\n",
       "0             0.320957        0.885171    0.261034  18.320460     1.12845   \n",
       "1             0.899673        0.069644    0.057344   0.066138     0.00012   \n",
       "2             0.499956        0.010243    0.164230   5.155446     0.24979   \n",
       "3             0.406980        0.006667    0.664189   0.305992     0.99968   \n",
       "4             0.434197        0.999801    0.683757   0.202726     0.89372   \n",
       "...                ...             ...         ...        ...         ...   \n",
       "176635       -1.000000       -1.000000         NaN        NaN     0.00000   \n",
       "176636       -1.000000       -1.000000         NaN        NaN     0.00000   \n",
       "176637       -1.000000       -1.000000         NaN        NaN     0.00000   \n",
       "176638       -1.000000       -1.000000         NaN        NaN     0.00000   \n",
       "176639       -1.000000       -1.000000         NaN        NaN     0.00000   \n",
       "\n",
       "        SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0           1305.172416         -1.000000       1.385326e+07   \n",
       "1            286.717791        923.599485       1.316854e+04   \n",
       "2            821.149907         -1.000000       1.577782e+07   \n",
       "3           3320.944008       1404.233487       6.634079e+03   \n",
       "4           3418.782649       1624.178589       1.353230e+04   \n",
       "...                 ...               ...                ...   \n",
       "176635        -1.000000         -1.000000      -1.000000e+00   \n",
       "176636        -1.000000         -1.000000      -1.000000e+00   \n",
       "176637        -1.000000         -1.000000      -1.000000e+00   \n",
       "176638        -1.000000         -1.000000      -1.000000e+00   \n",
       "176639        -1.000000         -1.000000      -1.000000e+00   \n",
       "\n",
       "        SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           207900.070205          0.885171           0.205123   \n",
       "1             5314.872532          0.038851           0.241710   \n",
       "2          -271216.457937          0.010243           0.393857   \n",
       "3            -1060.499747          0.006667           0.407122   \n",
       "4            -4837.676869          0.187056           0.482592   \n",
       "...                   ...               ...                ...   \n",
       "176635          -1.000000          0.006667          -1.000000   \n",
       "176636          -1.000000          0.006667          -1.000000   \n",
       "176637          -1.000000          0.006667          -1.000000   \n",
       "176638          -1.000000          0.006667          -1.000000   \n",
       "176639          -1.000000          0.006667          -1.000000   \n",
       "\n",
       "        SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0              0.417904       -1.000000     4248.583372    4544.559854   \n",
       "1              0.223803       -1.333903        3.640725       0.786195   \n",
       "2              0.271931       -1.000000      214.629001    1171.605316   \n",
       "3              0.273574       -0.362061        1.505125       2.539462   \n",
       "4              0.285750       -0.098411        1.314013       2.857491   \n",
       "...                 ...             ...             ...            ...   \n",
       "176635        -1.000000       -1.000000       -1.000000      -1.000000   \n",
       "176636        -1.000000       -1.000000       -1.000000      -1.000000   \n",
       "176637        -1.000000       -1.000000       -1.000000      -1.000000   \n",
       "176638        -1.000000       -1.000000       -1.000000      -1.000000   \n",
       "176639        -1.000000       -1.000000       -1.000000      -1.000000   \n",
       "\n",
       "        SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0                 0.564213              True  neris           3  \n",
       "1                -0.830029              True  clear           0  \n",
       "2                -0.489713              True  clear           0  \n",
       "3                -0.400313              True  clear           0  \n",
       "4                 0.565604              True  clear           0  \n",
       "...                    ...               ...    ...         ...  \n",
       "176635            0.000000             False  clear           0  \n",
       "176636            0.000000             False  clear           0  \n",
       "176637            0.000000             False  clear           0  \n",
       "176638            0.000000             False  clear           0  \n",
       "176639            0.000000             False  clear           0  \n",
       "\n",
       "[176640 rows x 86 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160181</th>\n",
       "      <td>donbot</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94631</th>\n",
       "      <td>fast_flux</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neris</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165017</th>\n",
       "      <td>qvod</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50019</th>\n",
       "      <td>rbot</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LABEL  LABEL_code\n",
       "1           clear           0\n",
       "160181     donbot           1\n",
       "94631   fast_flux           2\n",
       "0           neris           3\n",
       "165017       qvod           4\n",
       "50019        rbot           5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"LABEL\", \"LABEL_code\"]].drop_duplicates().sort_values(\"LABEL_code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clear', 'donbot', 'fast_flux', 'neris', 'qvod', 'rbot']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifications_array = []\n",
    "for index, row in df[[\"LABEL\", \"LABEL_code\"]].drop_duplicates().sort_values(\"LABEL_code\").iterrows():\n",
    "    classifications_array.append(row[0])\n",
    "classifications_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(176640, 74)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 25000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_features[features]\n",
    "y = df_features.LABEL_code\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    99464\n",
       "3.0     3834\n",
       "2.0     2619\n",
       "4.0      178\n",
       "5.0       36\n",
       "1.0       17\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    42627\n",
       "3.0     1644\n",
       "2.0     1123\n",
       "4.0       76\n",
       "5.0       15\n",
       "1.0        7\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    23482\n",
       "3.0      854\n",
       "2.0      625\n",
       "4.0       32\n",
       "5.0        4\n",
       "1.0        3\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation[\"LABEL_code\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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,average='macro')\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy, \"  \", space)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 5),\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', 50, 300, 50),\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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [1:12:59<00:00, 43.80s/trial, best loss: -0.8663217865191184] \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9726692639013753, 'gamma': 1.835123595733879, 'max_depth': 30.0, 'min_child_weight': 3.0, 'n_estimators': 300.0, 'reg_alpha': 1.0, 'reg_lambda': 0.681007253845867}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned_old(best_hyperparams, X_train=None, X_test=None, y_train=None, y_test=None):\n",
    "      if X_train is None:\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",
    "      \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",
    "      print(classification_report(y_test, y_pred, target_names=classifications_array, digits=4))\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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       clear     0.9996    1.0000    0.9998     42627\n",
      "      donbot     1.0000    0.7143    0.8333         7\n",
      "   fast_flux     0.9478    0.9546    0.9512      1123\n",
      "       neris     0.9634    0.9611    0.9622      1644\n",
      "        qvod     1.0000    0.9211    0.9589        76\n",
      "        rbot     1.0000    0.2000    0.3333        15\n",
      "\n",
      "    accuracy                         0.9970     45492\n",
      "   macro avg     0.9851    0.7918    0.8398     45492\n",
      "weighted avg     0.9970    0.9970    0.9969     45492\n",
      "\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_old(best_hyperparams, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned_old(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    report = 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",
    "        tmp_f1 = 0\n",
    "        tmp_str = \"\"\n",
    "        #Split on train and test\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y) # 70% training and 30% test\n",
    "        # sm = SMOTE(random_state=100)\n",
    "        # X_train, y_train = sm.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = sm.fit_resample(X_test, y_test)\n",
    "        \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, ) # eval_set=evaluation, eval_metric=[\"auc\",\"error\",\"logloss\"], verbose=False\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        F1 = f1_score(y_test, y_pred, average='micro')\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            report = classification_report(y_test, y_pred, target_names=classifications_array, digits=4)\n",
    "        \n",
    "    print(\"\")\n",
    "    print(report)\n",
    "    return best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       clear     0.9996    0.9999    0.9998     42627\n",
      "      donbot     1.0000    0.8571    0.9231         7\n",
      "   fast_flux     0.9648    0.9510    0.9578      1123\n",
      "       neris     0.9593    0.9751    0.9671      1644\n",
      "        qvod     0.9848    0.8553    0.9155        76\n",
      "        rbot     1.0000    0.2000    0.3333        15\n",
      "\n",
      "    accuracy                         0.9973     45492\n",
      "   macro avg     0.9848    0.8064    0.8494     45492\n",
      "weighted avg     0.9973    0.9973    0.9972     45492\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned_old(best_hyperparams, cycles=25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       clear     0.9997    1.0000    0.9999     23482\n",
      "      donbot     1.0000    0.6667    0.8000         3\n",
      "   fast_flux     0.9479    0.9600    0.9539       625\n",
      "       neris     0.9682    0.9625    0.9654       854\n",
      "        qvod     1.0000    0.8438    0.9153        32\n",
      "        rbot     0.5000    0.2500    0.3333         4\n",
      "\n",
      "    accuracy                         0.9973     25000\n",
      "   macro avg     0.9026    0.7805    0.8279     25000\n",
      "weighted avg     0.9973    0.9973    0.9973     25000\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",
    "\n",
    "print(classification_report(y_validation, y_pred, target_names=classifications_array, digits=4))\n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.31927288, 0.09996917, 0.12628278, 0.24097803, 0.15956329, 0.12628278]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF            0.000000\n",
       "CNT_NZ_DISTRIBUTION         0.000000\n",
       "CNT_ZEROS                   0.000000\n",
       "AREA_VALUES_DISTRIBUTION    0.000977\n",
       "PERCENT_DEVIATION           0.001092\n",
       "                              ...   \n",
       "MIN_POWER_FREQ              0.042232\n",
       "SPECTRAL_KURTOSIS           0.043519\n",
       "DIRECTIONS                  0.093584\n",
       "ENTROPY                     0.095457\n",
       "PACKETS_REV                 0.103285\n",
       "Length: 73, dtype: float32"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0009774329373613,\n",
       " 'PERCENT_DEVIATION': 0.0010920957429334521,\n",
       " 'HURST_EXPONENT': 0.001112982165068388,\n",
       " 'CNT_DISTRIBUTION': 0.001128194504417479,\n",
       " 'STDEV': 0.0016313515370711684,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0021014700178056955,\n",
       " 'TIME': 0.0023403239902108908,\n",
       " 'SPECTRAL_SPREAD': 0.0023814330343157053,\n",
       " 'Q3_SCALED_TIME': 0.002477538539096713,\n",
       " 'GALTON_SKEWNESS': 0.0028354532551020384,\n",
       " 'DURATION': 0.002909103175625205,\n",
       " 'Q1_SCALED_TIME': 0.003135307924821973,\n",
       " 'MEAN': 0.00321191712282598,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0033271494321525097,\n",
       " 'SPECTRAL_SKEWNESS': 0.0034543629735708237,\n",
       " 'BURSTINESS': 0.00347386347129941,\n",
       " 'BYTES': 0.0034937409218400717,\n",
       " 'PERCENT_BELOW_MEAN': 0.003977338317781687,\n",
       " 'MAX_DIFFTIMES': 0.004226309712976217,\n",
       " 'SPECTRAL_ENTROPY': 0.004253336228430271,\n",
       " 'SPECTRAL_CENTROID': 0.004259820096194744,\n",
       " 'MAX_POWER_FREQ': 0.004281984642148018,\n",
       " 'MAX': 0.0045292191207408905,\n",
       " 'P_BENFORD': 0.00532112829387188,\n",
       " 'MIN': 0.006394880823791027,\n",
       " 'BENFORD_LAW_PRESENTED': 0.006452550180256367,\n",
       " 'BYTES_REV': 0.006971529684960842,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.007342368829995394,\n",
       " 'MEDIAN': 0.0073635028675198555,\n",
       " 'AVERAGE_DISPERSION': 0.007565689738839865,\n",
       " 'SIG_SPACES': 0.0075760651379823685,\n",
       " 'VAR': 0.007576653268188238,\n",
       " 'BIGGEST_CNT_1_SEC': 0.007613523863255978,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.007815424352884293,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.007907223887741566,\n",
       " 'MEAN_SCALED_TIME': 0.008351976051926613,\n",
       " 'ROOT_MEAN_SQUARE': 0.008401893079280853,\n",
       " 'SWITCHING_METRIC': 0.008453959599137306,\n",
       " 'PERCENT_ABOVE_MEAN': 0.008513911627233028,\n",
       " 'PERIODICITY': 0.008574621751904488,\n",
       " 'SPECTRAL_ROLLOFF': 0.00882494542747736,\n",
       " 'Q1': 0.009773753583431244,\n",
       " 'TRANSIENTS': 0.009940298274159431,\n",
       " 'SPECTRAL_BANDWIDTH': 0.011364984326064587,\n",
       " 'MEAN_DIFFTIMES': 0.011397725902497768,\n",
       " 'MODE': 0.01139825489372015,\n",
       " 'SPECTRAL_SLOPE': 0.011919787153601646,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.012063334695994854,\n",
       " 'PACKETS': 0.012532028369605541,\n",
       " 'SPECTRAL_FLUX': 0.01258564181625843,\n",
       " 'MAX_POWER': 0.0138321528211236,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.014747053384780884,\n",
       " 'TIME_DISTRIBUTION': 0.016628896817564964,\n",
       " 'SPECTRAL_CREST': 0.017473584040999413,\n",
       " 'MEDIAN_SCALED_TIME': 0.017680557444691658,\n",
       " 'MEDIAN_DIFFTIMES': 0.01878036931157112,\n",
       " 'MIN_DIFFTIMES': 0.018946951255202293,\n",
       " 'SPECTRAL_ENERGY': 0.01903911866247654,\n",
       " 'MIN_MINUS_MAX': 0.01914084143936634,\n",
       " 'Q3': 0.02036282978951931,\n",
       " 'VAL': 0.021480372175574303,\n",
       " 'NORMAL_DISTRIBUTION': 0.022312728688120842,\n",
       " 'SCALED_ENTROPY': 0.0265914686024189,\n",
       " 'KURTOSIS': 0.026740634813904762,\n",
       " 'MIN_POWER': 0.041556019335985184,\n",
       " 'MIN_POWER_FREQ': 0.04223194718360901,\n",
       " 'SPECTRAL_KURTOSIS': 0.043518904596567154,\n",
       " 'DIRECTIONS': 0.09358413517475128,\n",
       " 'ENTROPY': 0.09545662999153137,\n",
       " 'PACKETS_REV': 0.10328548401594162}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, cycles = 25, best_hyperparams=None):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features, selected_features, verbose=False)\n",
    "    X_dos=df_features[selected_features]  # Features\n",
    "    y_dos=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_dos, y_dos, test_size=0.30, stratify=y_dos) # 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",
    "        F1 = f1_score(y_test, y_pred, average='macro')\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    print(F1)\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",
    "    \n",
    "    report = classification_report(y_validation,y_pred)\n",
    "    return report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [04:50<00:00,  2.91s/trial, best loss: -0.775350981610924]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.29799712452370675, 'gamma': 7.736781862898647, 'max_depth': 15.0, 'min_child_weight': 4.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.27276607255984164}\n",
      "0.771664093960117\n",
      "100%|██████████| 100/100 [08:17<00:00,  4.97s/trial, best loss: -0.8449072337527003]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8524223633429038, 'gamma': 1.178992403662464, 'max_depth': 5.0, 'min_child_weight': 7.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.2812688525319942}\n",
      "0.8762224221800894\n",
      "100%|██████████| 100/100 [10:25<00:00,  6.25s/trial, best loss: -0.8361549042343324]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.07691893449564205, 'gamma': 2.833491157589, 'max_depth': 10.0, 'min_child_weight': 11.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5476381510290043}\n",
      "0.7835537739846002\n",
      "100%|██████████| 100/100 [11:24<00:00,  6.85s/trial, best loss: -0.8685889993487573]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5776452515835873, 'gamma': 1.5374082188758589, 'max_depth': 15.0, 'min_child_weight': 2.0, 'n_estimators': 300.0, 'reg_alpha': 1.0, 'reg_lambda': 0.35042238723011404}\n",
      "0.803110083813908\n",
      "100%|██████████| 100/100 [09:29<00:00,  5.69s/trial, best loss: -0.8554168415947139]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5154036313081013, 'gamma': 1.1775284872596345, 'max_depth': 20.0, 'min_child_weight': 1.0, 'n_estimators': 100.0, 'reg_alpha': 1.0, 'reg_lambda': 0.8490432677694374}\n",
      "0.8047852464210307\n",
      "100%|██████████| 100/100 [05:10<00:00,  3.10s/trial, best loss: -0.8260193653371194]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8645942765772917, 'gamma': 8.15671480190521, 'max_depth': 10.0, 'min_child_weight': 7.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.1970166251776777}\n",
      "0.8051453474864722\n",
      "100%|██████████| 100/100 [11:43<00:00,  7.04s/trial, best loss: -0.8272206519062806]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6940616048019137, 'gamma': 1.5774328557870536, 'max_depth': 10.0, 'min_child_weight': 9.0, 'n_estimators': 50.0, 'reg_alpha': 0.0, 'reg_lambda': 0.41841837465726417}\n",
      "0.7650921667497815\n",
      "100%|██████████| 100/100 [07:40<00:00,  4.61s/trial, best loss: -0.8435034383112869]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6721846837317076, 'gamma': 7.490953528162827, 'max_depth': 25.0, 'min_child_weight': 0.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.20124726825396616}\n",
      "0.8271205148725804\n",
      "100%|██████████| 100/100 [39:08<00:00, 23.49s/trial, best loss: -0.8401360705389195]  \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6180352782446945, 'gamma': 7.02273575153274, 'max_depth': 25.0, 'min_child_weight': 8.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7920041518023708}\n",
      "0.8080384625502507\n",
      "100%|██████████| 100/100 [06:53<00:00,  4.14s/trial, best loss: -0.8650889572755874]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.20069641671590194, 'gamma': 1.0150378906487325, 'max_depth': 25.0, 'min_child_weight': 5.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5847733211172093}\n",
      "0.8684633830670717\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\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": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "FEATURE_SETS = [\"Botnet\", \"DoH\", \"DoS\", \"DNS\", \"TOR\", \"VPN\", \"ALL\", \"COMBO\", \"COMBO2\", \"Cryptomining\", ]\n",
    "\n",
    "import csv\n",
    "\n",
    "problem = \"Botnet-multiclass\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FEATURE_SETS):\n",
    "        tmp = report.split(\"weighted avg\")\n",
    "        tmp_2 = tmp[0].split(\"macro avg\")\n",
    "        accuracy = tmp_2[0].split(\"accuracy\")[1].split()[0]\n",
    "        _ = tmp_2[1].split()\n",
    "        m_precision = _[0]\n",
    "        m_recall = _[1]\n",
    "        m_f1 = _[2]\n",
    "        _ = tmp[1].split()\n",
    "        w_precision = _[0]\n",
    "        w_recall = _[1]\n",
    "        w_f1 = _[2]\n",
    "        writer.writerow([problem, f_set, accuracy, m_precision, m_recall, m_f1, w_precision, w_recall, w_f1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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