edge_iiot_mutliclass_timeseries_plugin.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Edge-IIoT IoT malware multiclass detection using Network Traffic Classification based on Single Flow Time Series Analysis   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/edge_iiot_multiclass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>224.0.0.251</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2940</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>5353</td>\n",
       "      <td>...</td>\n",
       "      <td>-261597.321119</td>\n",
       "      <td>0.199572</td>\n",
       "      <td>0.208451</td>\n",
       "      <td>0.054236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-70.042430</td>\n",
       "      <td>297845.871189</td>\n",
       "      <td>-0.000596</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>192.168.1.128.</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1883</td>\n",
       "      <td>...</td>\n",
       "      <td>-313387.471075</td>\n",
       "      <td>0.797161</td>\n",
       "      <td>0.867292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-249.116969</td>\n",
       "      <td>67780.547523</td>\n",
       "      <td>0.182773</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>192.168.1.101.</td>\n",
       "      <td>192.168.1.128</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>61823</td>\n",
       "      <td>...</td>\n",
       "      <td>7094.431505</td>\n",
       "      <td>0.851000</td>\n",
       "      <td>0.785438</td>\n",
       "      <td>0.200317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.158316</td>\n",
       "      <td>8.379427</td>\n",
       "      <td>0.884464</td>\n",
       "      <td>0.002473</td>\n",
       "      <td>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>192.168.1.128.</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1883</td>\n",
       "      <td>...</td>\n",
       "      <td>9179.647412</td>\n",
       "      <td>0.864509</td>\n",
       "      <td>0.816404</td>\n",
       "      <td>0.175113</td>\n",
       "      <td>1.055740</td>\n",
       "      <td>5.576106</td>\n",
       "      <td>9.812098</td>\n",
       "      <td>0.885259</td>\n",
       "      <td>0.002474</td>\n",
       "      <td>clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.168.1.101.</td>\n",
       "      <td>192.168.1.128</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>61824</td>\n",
       "      <td>...</td>\n",
       "      <td>2202.584636</td>\n",
       "      <td>0.745707</td>\n",
       "      <td>0.619200</td>\n",
       "      <td>0.272827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.684225</td>\n",
       "      <td>0.290936</td>\n",
       "      <td>0.883868</td>\n",
       "      <td>0.091092</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452657</th>\n",
       "      <td>1452657</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>192.168.0.170</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>268404.310201</td>\n",
       "      <td>0.619156</td>\n",
       "      <td>0.243761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6895.394550</td>\n",
       "      <td>3992.122541</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>XSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452658</th>\n",
       "      <td>1452658</td>\n",
       "      <td>172.217.19.42.</td>\n",
       "      <td>192.168.0.128</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2588</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>1588.889464</td>\n",
       "      <td>0.779480</td>\n",
       "      <td>0.480554</td>\n",
       "      <td>0.308518</td>\n",
       "      <td>-0.226912</td>\n",
       "      <td>3.135308</td>\n",
       "      <td>42.324922</td>\n",
       "      <td>0.176019</td>\n",
       "      <td>0.086074</td>\n",
       "      <td>XSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452659</th>\n",
       "      <td>1452659</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>172.217.19.42</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>53024</td>\n",
       "      <td>...</td>\n",
       "      <td>3899.614072</td>\n",
       "      <td>0.217055</td>\n",
       "      <td>0.371308</td>\n",
       "      <td>0.259434</td>\n",
       "      <td>-0.553421</td>\n",
       "      <td>3.704308</td>\n",
       "      <td>31.696439</td>\n",
       "      <td>-0.415412</td>\n",
       "      <td>0.076778</td>\n",
       "      <td>XSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452660</th>\n",
       "      <td>1452660</td>\n",
       "      <td>104.16.87.20.</td>\n",
       "      <td>192.168.0.128</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2552</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>4690.827782</td>\n",
       "      <td>0.664055</td>\n",
       "      <td>0.585775</td>\n",
       "      <td>0.261460</td>\n",
       "      <td>0.070717</td>\n",
       "      <td>2.625166</td>\n",
       "      <td>1.385119</td>\n",
       "      <td>-0.254293</td>\n",
       "      <td>0.017806</td>\n",
       "      <td>XSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452661</th>\n",
       "      <td>1452661</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>104.16.87.20</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>7010</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>35314</td>\n",
       "      <td>...</td>\n",
       "      <td>-5276.683787</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.469103</td>\n",
       "      <td>0.290925</td>\n",
       "      <td>-0.133063</td>\n",
       "      <td>1.199077</td>\n",
       "      <td>0.296332</td>\n",
       "      <td>-0.649839</td>\n",
       "      <td>0.162950</td>\n",
       "      <td>XSS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1452662 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP         SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0     224.0.0.251  192.168.1.101       60            0   \n",
       "1                 1  192.168.1.128.  192.168.1.101        7            0   \n",
       "2                 2  192.168.1.101.  192.168.1.128        6            0   \n",
       "3                 3  192.168.1.128.  192.168.1.101        8            0   \n",
       "4                 4  192.168.1.101.  192.168.1.128        7            0   \n",
       "...             ...             ...            ...      ...          ...   \n",
       "1452657     1452657  192.168.0.128.  192.168.0.170        7            0   \n",
       "1452658     1452658  172.217.19.42.  192.168.0.128       29            0   \n",
       "1452659     1452659  192.168.0.128.  172.217.19.42       22            0   \n",
       "1452660     1452660   104.16.87.20.  192.168.0.128       21            0   \n",
       "1452661     1452661  192.168.0.128.   104.16.87.20       15            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  ...  \\\n",
       "0         2940          0  1.640646e+09  1.640646e+09      5353  ...   \n",
       "1           46          0  1.640646e+09  1.640646e+09      1883  ...   \n",
       "2            4          0  1.640646e+09  1.640646e+09     61823  ...   \n",
       "3           46          0  1.640646e+09  1.640646e+09      1883  ...   \n",
       "4            4          0  1.640646e+09  1.640646e+09     61824  ...   \n",
       "...        ...        ...           ...           ...       ...  ...   \n",
       "1452657    102          0  1.638212e+09  1.638212e+09        80  ...   \n",
       "1452658   2588          0  1.638208e+09  1.638208e+09       443  ...   \n",
       "1452659   2118          0  1.638208e+09  1.638208e+09     53024  ...   \n",
       "1452660   2552          0  1.638212e+09  1.638212e+09       443  ...   \n",
       "1452661   7010          0  1.638212e+09  1.638212e+09     35314  ...   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -261597.321119          0.199572           0.208451   \n",
       "1           -313387.471075          0.797161           0.867292   \n",
       "2              7094.431505          0.851000           0.785438   \n",
       "3              9179.647412          0.864509           0.816404   \n",
       "4              2202.584636          0.745707           0.619200   \n",
       "...                    ...               ...                ...   \n",
       "1452657      268404.310201          0.619156           0.243761   \n",
       "1452658        1588.889464          0.779480           0.480554   \n",
       "1452659        3899.614072          0.217055           0.371308   \n",
       "1452660        4690.827782          0.664055           0.585775   \n",
       "1452661       -5276.683787          0.006667           0.469103   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.054236             NaN      -70.042430  297845.871189   \n",
       "1                    NaN             NaN     -249.116969   67780.547523   \n",
       "2               0.200317             NaN        4.158316       8.379427   \n",
       "3               0.175113        1.055740        5.576106       9.812098   \n",
       "4               0.272827             NaN        1.684225       0.290936   \n",
       "...                  ...             ...             ...            ...   \n",
       "1452657              NaN             NaN     6895.394550    3992.122541   \n",
       "1452658         0.308518       -0.226912        3.135308      42.324922   \n",
       "1452659         0.259434       -0.553421        3.704308      31.696439   \n",
       "1452660         0.261460        0.070717        2.625166       1.385119   \n",
       "1452661         0.290925       -0.133063        1.199077       0.296332   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.000596          0.000037  clear  \n",
       "1                  0.182773          0.000189  clear  \n",
       "2                  0.884464          0.002473  clear  \n",
       "3                  0.885259          0.002474  clear  \n",
       "4                  0.883868          0.091092  clear  \n",
       "...                     ...               ...    ...  \n",
       "1452657           -0.000199          0.002736    XSS  \n",
       "1452658            0.176019          0.086074    XSS  \n",
       "1452659           -0.415412          0.076778    XSS  \n",
       "1452660           -0.254293          0.017806    XSS  \n",
       "1452661           -0.649839          0.162950    XSS  \n",
       "\n",
       "[1452662 rows x 84 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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</th>\n",
       "      <th>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
       "      <th>PEARSON_SK1_SKEWNESS</th>\n",
       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>224.0.0.251</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>60</td>\n",
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       "      <td>1.640646e+09</td>\n",
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       "      <td>109.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.500009</td>\n",
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       "      <td>4.916481</td>\n",
       "      <td>5.085931</td>\n",
       "      <td>0.983224</td>\n",
       "      <td>False</td>\n",
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       "      <td>1308</td>\n",
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       "      <td>0.199572</td>\n",
       "      <td>-34.814260</td>\n",
       "      <td>1633.287018</td>\n",
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       "      <td>-174071.302265</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.457744e+07</td>\n",
       "      <td>-261597.321119</td>\n",
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       "      <td>-70.042430</td>\n",
       "      <td>297845.871189</td>\n",
       "      <td>-0.000596</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>192.168.1.128.</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>7</td>\n",
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       "      <td>60.0</td>\n",
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       "      <td>0.285714</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>12.011358</td>\n",
       "      <td>3.874170</td>\n",
       "      <td>3.767915e-04</td>\n",
       "      <td>4.883776e-04</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>2.798373</td>\n",
       "      <td>1.664498</td>\n",
       "      <td>0.592906</td>\n",
       "      <td>0.470985</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.496637</td>\n",
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       "      <td>0.020341</td>\n",
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       "      <td>0.000038</td>\n",
       "      <td>0.014798</td>\n",
       "      <td>0.229526</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>466</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-24101.201830</td>\n",
       "      <td>1611.718130</td>\n",
       "      <td>0.797360</td>\n",
       "      <td>0.980133</td>\n",
       "      <td>-6.469724</td>\n",
       "      <td>357.132637</td>\n",
       "      <td>0.21654</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>2.083880e+07</td>\n",
       "      <td>-313387.471075</td>\n",
       "      <td>0.797161</td>\n",
       "      <td>0.867292</td>\n",
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       "      <td>-1.000000</td>\n",
       "      <td>-249.116969</td>\n",
       "      <td>67780.547523</td>\n",
       "      <td>0.182773</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>192.168.1.101.</td>\n",
       "      <td>192.168.1.128</td>\n",
       "      <td>6</td>\n",
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       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>61823</td>\n",
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       "      <td>60.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.953945</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>2.357023</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.684979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.852814</td>\n",
       "      <td>2591.782055</td>\n",
       "      <td>2.357023e-01</td>\n",
       "      <td>3.227486e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.650022</td>\n",
       "      <td>0.251463</td>\n",
       "      <td>0.285759</td>\n",
       "      <td>False</td>\n",
       "      <td>0.467697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.413082</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.008261</td>\n",
       "      <td>0.004891</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.019974</td>\n",
       "      <td>0.019999</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.015083</td>\n",
       "      <td>0.265189</td>\n",
       "      <td>False</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.215011</td>\n",
       "      <td>4.683203</td>\n",
       "      <td>0.007263</td>\n",
       "      <td>0.891727</td>\n",
       "      <td>1.126226</td>\n",
       "      <td>1.375427</td>\n",
       "      <td>0.17333</td>\n",
       "      <td>5631.128620</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.799377e+04</td>\n",
       "      <td>7094.431505</td>\n",
       "      <td>0.851000</td>\n",
       "      <td>0.785438</td>\n",
       "      <td>0.200317</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.158316</td>\n",
       "      <td>8.379427</td>\n",
       "      <td>0.884464</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>192.168.1.128.</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1883</td>\n",
       "      <td>61824</td>\n",
       "      <td>65</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.908712</td>\n",
       "      <td>120</td>\n",
       "      <td>-0.712583</td>\n",
       "      <td>60.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>16.782634</td>\n",
       "      <td>7</td>\n",
       "      <td>10.769231</td>\n",
       "      <td>66.539462</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.458349</td>\n",
       "      <td>12.375430</td>\n",
       "      <td>6.731956</td>\n",
       "      <td>5.777512e-04</td>\n",
       "      <td>7.205823e-04</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.534284</td>\n",
       "      <td>1.548795</td>\n",
       "      <td>0.516265</td>\n",
       "      <td>0.442503</td>\n",
       "      <td>False</td>\n",
       "      <td>0.647879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.327577</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.010765</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.006289</td>\n",
       "      <td>0.012624</td>\n",
       "      <td>0.032861</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.020237</td>\n",
       "      <td>0.231973</td>\n",
       "      <td>False</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>526</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046638</td>\n",
       "      <td>5.651375</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.892919</td>\n",
       "      <td>1.013498</td>\n",
       "      <td>1.510552</td>\n",
       "      <td>0.10699</td>\n",
       "      <td>5067.492460</td>\n",
       "      <td>-6120.037809</td>\n",
       "      <td>2.583131e+04</td>\n",
       "      <td>9179.647412</td>\n",
       "      <td>0.864509</td>\n",
       "      <td>0.816404</td>\n",
       "      <td>0.175113</td>\n",
       "      <td>1.055740</td>\n",
       "      <td>5.576106</td>\n",
       "      <td>9.812098</td>\n",
       "      <td>0.885259</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.168.1.101.</td>\n",
       "      <td>192.168.1.128</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>61824</td>\n",
       "      <td>1883</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.953945</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>2.357023</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.587599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.852814</td>\n",
       "      <td>2203.344730</td>\n",
       "      <td>2.020305e-01</td>\n",
       "      <td>2.618615e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.142857</td>\n",
       "      <td>0.591673</td>\n",
       "      <td>0.210758</td>\n",
       "      <td>0.321324</td>\n",
       "      <td>False</td>\n",
       "      <td>0.443887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280033</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.009191</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.002935</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.020512</td>\n",
       "      <td>0.266698</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>424</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.003317</td>\n",
       "      <td>0.167746</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.891528</td>\n",
       "      <td>0.099598</td>\n",
       "      <td>0.041911</td>\n",
       "      <td>0.16774</td>\n",
       "      <td>497.991393</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>8.029687e+03</td>\n",
       "      <td>2202.584636</td>\n",
       "      <td>0.745707</td>\n",
       "      <td>0.619200</td>\n",
       "      <td>0.272827</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.684225</td>\n",
       "      <td>0.290936</td>\n",
       "      <td>0.883868</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>3</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>1452657</th>\n",
       "      <td>1452657</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>192.168.0.170</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>33090</td>\n",
       "      <td>74</td>\n",
       "      <td>60.0</td>\n",
       "      <td>38.548671</td>\n",
       "      <td>1486</td>\n",
       "      <td>-0.314987</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>162</td>\n",
       "      <td>102</td>\n",
       "      <td>60</td>\n",
       "      <td>52.092798</td>\n",
       "      <td>24</td>\n",
       "      <td>32.432432</td>\n",
       "      <td>82.673195</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.363177</td>\n",
       "      <td>4.202480</td>\n",
       "      <td>1.001827</td>\n",
       "      <td>9.975479e-06</td>\n",
       "      <td>1.292970e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.894584</td>\n",
       "      <td>0.591673</td>\n",
       "      <td>0.210758</td>\n",
       "      <td>0.321324</td>\n",
       "      <td>False</td>\n",
       "      <td>0.443887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.436614</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.013064</td>\n",
       "      <td>0.015008</td>\n",
       "      <td>0.004709</td>\n",
       "      <td>0.015576</td>\n",
       "      <td>0.029921</td>\n",
       "      <td>0.004987</td>\n",
       "      <td>0.004514</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.014345</td>\n",
       "      <td>0.347638</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>522</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-450.052190</td>\n",
       "      <td>1546.197851</td>\n",
       "      <td>0.619355</td>\n",
       "      <td>0.619156</td>\n",
       "      <td>0.224236</td>\n",
       "      <td>23.806847</td>\n",
       "      <td>-0.43733</td>\n",
       "      <td>1121.181565</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.793952e+07</td>\n",
       "      <td>268404.310201</td>\n",
       "      <td>0.619156</td>\n",
       "      <td>0.243761</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>6895.394550</td>\n",
       "      <td>3992.122541</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>XSS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452658</th>\n",
       "      <td>1452658</td>\n",
       "      <td>172.217.19.42.</td>\n",
       "      <td>192.168.0.128</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2588</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>53024</td>\n",
       "      <td>149</td>\n",
       "      <td>60.0</td>\n",
       "      <td>199.727314</td>\n",
       "      <td>39891</td>\n",
       "      <td>0.145464</td>\n",
       "      <td>60.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>60</td>\n",
       "      <td>676</td>\n",
       "      <td>616</td>\n",
       "      <td>60</td>\n",
       "      <td>134.045177</td>\n",
       "      <td>133</td>\n",
       "      <td>89.261745</td>\n",
       "      <td>246.553345</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.445608</td>\n",
       "      <td>1.937642</td>\n",
       "      <td>1.928618</td>\n",
       "      <td>3.029616e-08</td>\n",
       "      <td>3.197436e-08</td>\n",
       "      <td>-3.564103</td>\n",
       "      <td>5.253090</td>\n",
       "      <td>1.867960</td>\n",
       "      <td>0.384514</td>\n",
       "      <td>0.695716</td>\n",
       "      <td>False</td>\n",
       "      <td>0.645858</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.629863</td>\n",
       "      <td>0.207315</td>\n",
       "      <td>0.743421</td>\n",
       "      <td>50.200818</td>\n",
       "      <td>1.928644</td>\n",
       "      <td>0.583471</td>\n",
       "      <td>48.607712</td>\n",
       "      <td>242.147978</td>\n",
       "      <td>8.648142</td>\n",
       "      <td>0.086327</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>46.080018</td>\n",
       "      <td>0.187677</td>\n",
       "      <td>False</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.629863</td>\n",
       "      <td>3848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>0.648360</td>\n",
       "      <td>0.824379</td>\n",
       "      <td>0.097993</td>\n",
       "      <td>0.058425</td>\n",
       "      <td>0.11406</td>\n",
       "      <td>489.962718</td>\n",
       "      <td>1499.463717</td>\n",
       "      <td>1.357787e+04</td>\n",
       "      <td>1588.889464</td>\n",
       "      <td>0.779480</td>\n",
       "      <td>0.480554</td>\n",
       "      <td>0.308518</td>\n",
       "      <td>-0.226912</td>\n",
       "      <td>3.135308</td>\n",
       "      <td>42.324922</td>\n",
       "      <td>0.176019</td>\n",
       "      <td>True</td>\n",
       "      <td>XSS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452659</th>\n",
       "      <td>1452659</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>172.217.19.42</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>53024</td>\n",
       "      <td>443</td>\n",
       "      <td>156</td>\n",
       "      <td>60.0</td>\n",
       "      <td>173.651951</td>\n",
       "      <td>30155</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.25</td>\n",
       "      <td>60</td>\n",
       "      <td>668</td>\n",
       "      <td>608</td>\n",
       "      <td>60</td>\n",
       "      <td>111.315353</td>\n",
       "      <td>126</td>\n",
       "      <td>80.769231</td>\n",
       "      <td>230.664258</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.552830</td>\n",
       "      <td>2.349527</td>\n",
       "      <td>1.652482</td>\n",
       "      <td>5.208223e-08</td>\n",
       "      <td>5.597325e-08</td>\n",
       "      <td>-0.773672</td>\n",
       "      <td>5.007316</td>\n",
       "      <td>2.181899</td>\n",
       "      <td>0.489277</td>\n",
       "      <td>0.645573</td>\n",
       "      <td>False</td>\n",
       "      <td>0.710121</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>0.625969</td>\n",
       "      <td>0.227372</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>55.040139</td>\n",
       "      <td>1.852389</td>\n",
       "      <td>1.185360</td>\n",
       "      <td>94.780779</td>\n",
       "      <td>242.071271</td>\n",
       "      <td>11.527203</td>\n",
       "      <td>0.452215</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>46.270100</td>\n",
       "      <td>0.249129</td>\n",
       "      <td>False</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625969</td>\n",
       "      <td>2945</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.307517</td>\n",
       "      <td>0.740939</td>\n",
       "      <td>0.325527</td>\n",
       "      <td>0.083016</td>\n",
       "      <td>0.062044</td>\n",
       "      <td>0.08584</td>\n",
       "      <td>415.080357</td>\n",
       "      <td>1319.492035</td>\n",
       "      <td>1.540541e+04</td>\n",
       "      <td>3899.614072</td>\n",
       "      <td>0.217055</td>\n",
       "      <td>0.371308</td>\n",
       "      <td>0.259434</td>\n",
       "      <td>-0.553421</td>\n",
       "      <td>3.704308</td>\n",
       "      <td>31.696439</td>\n",
       "      <td>-0.415412</td>\n",
       "      <td>True</td>\n",
       "      <td>XSS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452660</th>\n",
       "      <td>1452660</td>\n",
       "      <td>104.16.87.20.</td>\n",
       "      <td>192.168.0.128</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2552</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>35314</td>\n",
       "      <td>181</td>\n",
       "      <td>60.0</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>42436</td>\n",
       "      <td>0.064599</td>\n",
       "      <td>60.0</td>\n",
       "      <td>152.00</td>\n",
       "      <td>60</td>\n",
       "      <td>577</td>\n",
       "      <td>517</td>\n",
       "      <td>60</td>\n",
       "      <td>113.812155</td>\n",
       "      <td>167</td>\n",
       "      <td>92.265193</td>\n",
       "      <td>270.862291</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.587379</td>\n",
       "      <td>2.344660</td>\n",
       "      <td>1.076383</td>\n",
       "      <td>5.991999e-08</td>\n",
       "      <td>6.463128e-08</td>\n",
       "      <td>-1.630435</td>\n",
       "      <td>2.540234</td>\n",
       "      <td>1.875146</td>\n",
       "      <td>0.426915</td>\n",
       "      <td>0.605187</td>\n",
       "      <td>False</td>\n",
       "      <td>0.681982</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.552930</td>\n",
       "      <td>0.721154</td>\n",
       "      <td>1.419733</td>\n",
       "      <td>1.116450</td>\n",
       "      <td>0.888133</td>\n",
       "      <td>2.421404</td>\n",
       "      <td>2.567657</td>\n",
       "      <td>0.128383</td>\n",
       "      <td>0.057662</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.701278</td>\n",
       "      <td>0.183070</td>\n",
       "      <td>False</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009247</td>\n",
       "      <td>0.623643</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.745508</td>\n",
       "      <td>0.237563</td>\n",
       "      <td>0.186311</td>\n",
       "      <td>0.18379</td>\n",
       "      <td>1187.815753</td>\n",
       "      <td>1962.317406</td>\n",
       "      <td>1.280615e+04</td>\n",
       "      <td>4690.827782</td>\n",
       "      <td>0.664055</td>\n",
       "      <td>0.585775</td>\n",
       "      <td>0.261460</td>\n",
       "      <td>0.070717</td>\n",
       "      <td>2.625166</td>\n",
       "      <td>1.385119</td>\n",
       "      <td>-0.254293</td>\n",
       "      <td>True</td>\n",
       "      <td>XSS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452661</th>\n",
       "      <td>1452661</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>104.16.87.20</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>7010</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>35314</td>\n",
       "      <td>443</td>\n",
       "      <td>527</td>\n",
       "      <td>91.0</td>\n",
       "      <td>555.999101</td>\n",
       "      <td>309135</td>\n",
       "      <td>0.026777</td>\n",
       "      <td>60.0</td>\n",
       "      <td>836.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1420</td>\n",
       "      <td>1360</td>\n",
       "      <td>60</td>\n",
       "      <td>105.502676</td>\n",
       "      <td>486</td>\n",
       "      <td>92.220114</td>\n",
       "      <td>752.733153</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.839929</td>\n",
       "      <td>2.679860</td>\n",
       "      <td>0.378354</td>\n",
       "      <td>1.939351e-09</td>\n",
       "      <td>2.161836e-09</td>\n",
       "      <td>-0.203608</td>\n",
       "      <td>1.589497</td>\n",
       "      <td>2.365596</td>\n",
       "      <td>0.605493</td>\n",
       "      <td>0.582157</td>\n",
       "      <td>False</td>\n",
       "      <td>0.832303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.635707</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.708593</td>\n",
       "      <td>2.319056</td>\n",
       "      <td>0.805496</td>\n",
       "      <td>2.478584</td>\n",
       "      <td>2.687704</td>\n",
       "      <td>0.191979</td>\n",
       "      <td>0.072780</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.773057</td>\n",
       "      <td>0.248337</td>\n",
       "      <td>False</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7910</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088505</td>\n",
       "      <td>0.206090</td>\n",
       "      <td>0.656505</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.171874</td>\n",
       "      <td>0.034039</td>\n",
       "      <td>0.20608</td>\n",
       "      <td>859.370832</td>\n",
       "      <td>2156.663490</td>\n",
       "      <td>1.474349e+04</td>\n",
       "      <td>-5276.683787</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.469103</td>\n",
       "      <td>0.290925</td>\n",
       "      <td>-0.133063</td>\n",
       "      <td>1.199077</td>\n",
       "      <td>0.296332</td>\n",
       "      <td>-0.649839</td>\n",
       "      <td>True</td>\n",
       "      <td>XSS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1452662 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP         SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0     224.0.0.251  192.168.1.101       60            0   \n",
       "1                 1  192.168.1.128.  192.168.1.101        7            0   \n",
       "2                 2  192.168.1.101.  192.168.1.128        6            0   \n",
       "3                 3  192.168.1.128.  192.168.1.101        8            0   \n",
       "4                 4  192.168.1.101.  192.168.1.128        7            0   \n",
       "...             ...             ...            ...      ...          ...   \n",
       "1452657     1452657  192.168.0.128.  192.168.0.170        7            0   \n",
       "1452658     1452658  172.217.19.42.  192.168.0.128       29            0   \n",
       "1452659     1452659  192.168.0.128.  172.217.19.42       22            0   \n",
       "1452660     1452660   104.16.87.20.  192.168.0.128       21            0   \n",
       "1452661     1452661  192.168.0.128.   104.16.87.20       15            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0         2940          0  1.640646e+09  1.640646e+09      5353      5353   \n",
       "1           46          0  1.640646e+09  1.640646e+09      1883     61823   \n",
       "2            4          0  1.640646e+09  1.640646e+09     61823      1883   \n",
       "3           46          0  1.640646e+09  1.640646e+09      1883     61824   \n",
       "4            4          0  1.640646e+09  1.640646e+09     61824      1883   \n",
       "...        ...        ...           ...           ...       ...       ...   \n",
       "1452657    102          0  1.638212e+09  1.638212e+09        80     33090   \n",
       "1452658   2588          0  1.638208e+09  1.638208e+09       443     53024   \n",
       "1452659   2118          0  1.638208e+09  1.638208e+09     53024       443   \n",
       "1452660   2552          0  1.638212e+09  1.638212e+09       443     35314   \n",
       "1452661   7010          0  1.638212e+09  1.638212e+09     35314       443   \n",
       "\n",
       "         MEAN  MEDIAN       STDEV     VAR  BURSTINESS     Q1      Q3  MIN  \\\n",
       "0         109   109.0    0.000000       0   -1.000000  109.0  109.00  109   \n",
       "1          66    60.0   11.489125     133   -0.703465   60.0   68.00   60   \n",
       "2          60    60.0    1.414214       3   -0.953945   60.0   60.00   60   \n",
       "3          65    60.0   10.908712     120   -0.712583   60.0   65.00   60   \n",
       "4          60    60.0    1.414214       2   -0.953945   60.0   60.00   60   \n",
       "...       ...     ...         ...     ...         ...    ...     ...  ...   \n",
       "1452657    74    60.0   38.548671    1486   -0.314987   60.0   60.00   60   \n",
       "1452658   149    60.0  199.727314   39891    0.145464   60.0   99.00   60   \n",
       "1452659   156    60.0  173.651951   30155    0.053547   60.0  168.25   60   \n",
       "1452660   181    60.0  206.000000   42436    0.064599   60.0  152.00   60   \n",
       "1452661   527    91.0  555.999101  309135    0.026777   60.0  836.00   60   \n",
       "\n",
       "          MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0         109              0   109                  0.000000   \n",
       "1          90             30    60                 17.407766   \n",
       "2          64              4    60                  2.357023   \n",
       "3          90             30    60                 16.782634   \n",
       "4          64              4    60                  2.357023   \n",
       "...       ...            ...   ...                       ...   \n",
       "1452657   162            102    60                 52.092798   \n",
       "1452658   676            616    60                134.045177   \n",
       "1452659   668            608    60                111.315353   \n",
       "1452660   577            517    60                113.812155   \n",
       "1452661  1420           1360    60                105.502676   \n",
       "\n",
       "         AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                         0           0.000000        109.000000   \n",
       "1                         8          12.121212         67.421913   \n",
       "2                         0           0.000000         60.684979   \n",
       "3                         7          10.769231         66.539462   \n",
       "4                         0           0.000000         60.587599   \n",
       "...                     ...                ...               ...   \n",
       "1452657                  24          32.432432         82.673195   \n",
       "1452658                 133          89.261745        246.553345   \n",
       "1452659                 126          80.769231        230.664258   \n",
       "1452660                 167          92.265193        270.862291   \n",
       "1452661                 486          92.220114        752.733153   \n",
       "\n",
       "         PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                  0.000000            0.000000              0.000000   \n",
       "1                  0.714286            0.285714              0.522233   \n",
       "2                  0.000000            0.166667              0.000000   \n",
       "3                  0.750000            0.250000              0.458349   \n",
       "4                  0.000000            0.142857              0.000000   \n",
       "...                     ...                 ...                   ...   \n",
       "1452657            0.857143            0.142857              0.363177   \n",
       "1452658            0.827586            0.172414              0.445608   \n",
       "1452659            0.727273            0.272727              0.552830   \n",
       "1452660            0.761905            0.238095              0.587379   \n",
       "1452661            0.533333            0.466667              0.839929   \n",
       "\n",
       "         PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    0.000000              0.000000   \n",
       "1                   12.011358              3.874170   \n",
       "2                   84.852814           2591.782055   \n",
       "3                   12.375430              6.731956   \n",
       "4                   84.852814           2203.344730   \n",
       "...                       ...                   ...   \n",
       "1452657              4.202480              1.001827   \n",
       "1452658              1.937642              1.928618   \n",
       "1452659              2.349527              1.652482   \n",
       "1452660              2.344660              1.076383   \n",
       "1452661              2.679860              0.378354   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      0.000000e+00                0.000000e+00   \n",
       "1                      3.767915e-04                4.883776e-04   \n",
       "2                      2.357023e-01                3.227486e-01   \n",
       "3                      5.777512e-04                7.205823e-04   \n",
       "4                      2.020305e-01                2.618615e-01   \n",
       "...                             ...                         ...   \n",
       "1452657                9.975479e-06                1.292970e-05   \n",
       "1452658                3.029616e-08                3.197436e-08   \n",
       "1452659                5.208223e-08                5.597325e-08   \n",
       "1452660                5.991999e-08                6.463128e-08   \n",
       "1452661                1.939351e-09                2.161836e-09   \n",
       "\n",
       "         GALTON_SKEWNESS   KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000   0.000000  0.000000        0.000000        1.000000   \n",
       "1              -0.500000   2.798373  1.664498        0.592906        0.470985   \n",
       "2               0.000000  10.666667  0.650022        0.251463        0.285759   \n",
       "3              -1.000000   3.534284  1.548795        0.516265        0.442503   \n",
       "4               0.000000   9.142857  0.591673        0.210758        0.321324   \n",
       "...                  ...        ...       ...             ...             ...   \n",
       "1452657         0.000000   3.894584  0.591673        0.210758        0.321324   \n",
       "1452658        -3.564103   5.253090  1.867960        0.384514        0.695716   \n",
       "1452659        -0.773672   5.007316  2.181899        0.489277        0.645573   \n",
       "1452660        -1.630435   2.540234  1.875146        0.426915        0.605187   \n",
       "1452661        -0.203608   1.589497  2.365596        0.605493        0.582157   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.301030             0.004354   \n",
       "1                        False   0.665736             0.000000   \n",
       "2                        False   0.467697             0.000000   \n",
       "3                        False   0.647879             0.000000   \n",
       "4                        False   0.443887             0.000000   \n",
       "...                        ...        ...                  ...   \n",
       "1452657                  False   0.443887             0.000000   \n",
       "1452658                  False   0.645858             0.001000   \n",
       "1452659                  False   0.710121             0.001530   \n",
       "1452660                  False   0.681982             0.000000   \n",
       "1452661                  False   0.832303             0.000000   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                0.666667           0.500009                  0.875000   \n",
       "1                0.000000           0.496637                  0.687500   \n",
       "2                0.000000           0.413082                  0.825000   \n",
       "3                0.000000           0.327577                  0.725000   \n",
       "4                0.000000           0.280033                  0.833333   \n",
       "...                   ...                ...                       ...   \n",
       "1452657          0.000000           0.436614                  0.833333   \n",
       "1452658          0.629863           0.207315                  0.743421   \n",
       "1452659          0.625969           0.227372                  0.854167   \n",
       "1452660          0.000000           0.552930                  0.721154   \n",
       "1452661          0.000000           0.635707                  0.375000   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0              147.520426          150.018016       75.009759      225.027113   \n",
       "1                0.010121            0.005157        0.000364        0.020341   \n",
       "2                0.008261            0.004891        0.000358        0.019974   \n",
       "3                0.010765            0.012346        0.006289        0.012624   \n",
       "4                0.009191            0.006278        0.002946        0.012311   \n",
       "...                   ...                 ...             ...             ...   \n",
       "1452657          0.013064            0.015008        0.004709        0.015576   \n",
       "1452658         50.200818            1.928644        0.583471       48.607712   \n",
       "1452659         55.040139            1.852389        1.185360       94.780779   \n",
       "1452660          1.419733            1.116450        0.888133        2.421404   \n",
       "1452661          1.708593            2.319056        0.805496        2.478584   \n",
       "\n",
       "           DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        295.035825        5.000607          5.000450       4.916481   \n",
       "1          0.020379        0.003397          0.000446       0.000038   \n",
       "2          0.019999        0.004000          0.000546       0.000025   \n",
       "3          0.032861        0.004694          0.002932       0.000019   \n",
       "4          0.032823        0.005470          0.002935       0.000352   \n",
       "...             ...             ...               ...            ...   \n",
       "1452657    0.029921        0.004987          0.004514       0.000033   \n",
       "1452658  242.147978        8.648142          0.086327       0.000025   \n",
       "1452659  242.071271       11.527203          0.452215       0.000023   \n",
       "1452660    2.567657        0.128383          0.057662       0.000070   \n",
       "1452661    2.687704        0.191979          0.072780       0.000069   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0             5.085931               0.983224       False          0.000000   \n",
       "1             0.014798               0.229526       False          1.333333   \n",
       "2             0.015083               0.265189       False          0.800000   \n",
       "3             0.020237               0.231973       False          1.142857   \n",
       "4             0.020512               0.266698       False          0.666667   \n",
       "...                ...                    ...         ...               ...   \n",
       "1452657       0.014345               0.347638       False          0.666667   \n",
       "1452658      46.080018               0.187677       False          0.928571   \n",
       "1452659      46.270100               0.249129       False          1.238095   \n",
       "1452660       0.701278               0.183070       False          1.100000   \n",
       "1452661       0.773057               0.248337       False          1.571429   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0             0.666667               1308   \n",
       "1             False        0.0             0.000000                466   \n",
       "2             False        0.0             0.000000                364   \n",
       "3             False        0.0             0.000000                526   \n",
       "4             False        0.0             0.000000                424   \n",
       "...             ...        ...                  ...                ...   \n",
       "1452657        True        0.0             0.000000                522   \n",
       "1452658       False        0.0             0.629863               3848   \n",
       "1452659        True        0.0             0.625969               2945   \n",
       "1452660       False        0.0             0.000000               3812   \n",
       "1452661        True        0.0             0.000000               7910   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME     MIN_POWER    MAX_POWER  \\\n",
       "0               1.0         True  109  5.002997 -98298.111034  2438.475406   \n",
       "1               1.0        False    0  0.000000 -24101.201830  1611.718130   \n",
       "2               1.0        False    0  0.000000     -0.215011     4.683203   \n",
       "3               1.0        False    0  0.000000      0.046638     5.651375   \n",
       "4               1.0        False    0  0.000000     -0.003317     0.167746   \n",
       "...             ...          ...  ...       ...           ...          ...   \n",
       "1452657         1.0        False    0  0.000000   -450.052190  1546.197851   \n",
       "1452658         1.0        False    0  0.000000      0.000015     0.307237   \n",
       "1452659         1.0        False    0  0.000000      0.000031     0.307517   \n",
       "1452660         1.0        False    0  0.000000      0.009247     0.623643   \n",
       "1452661         1.0        False    0  0.000000      0.088505     0.206090   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN    POWER_STD  POWER_MODE  \\\n",
       "0              0.200168        0.199572  -34.814260  1633.287018     0.00000   \n",
       "1              0.797360        0.980133   -6.469724   357.132637     0.21654   \n",
       "2              0.007263        0.891727    1.126226     1.375427     0.17333   \n",
       "3              0.007660        0.892919    1.013498     1.510552     0.10699   \n",
       "4              0.007660        0.891528    0.099598     0.041911     0.16774   \n",
       "...                 ...             ...         ...          ...         ...   \n",
       "1452657        0.619355        0.619156    0.224236    23.806847    -0.43733   \n",
       "1452658        0.648360        0.824379    0.097993     0.058425     0.11406   \n",
       "1452659        0.740939        0.325527    0.083016     0.062044     0.08584   \n",
       "1452660        0.999801        0.745508    0.237563     0.186311     0.18379   \n",
       "1452661        0.656505        0.006667    0.171874     0.034039     0.20608   \n",
       "\n",
       "         SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0         -174071.302265         -1.000000       1.457744e+07   \n",
       "1          -32348.621952         -1.000000       2.083880e+07   \n",
       "2            5631.128620         -1.000000       1.799377e+04   \n",
       "3            5067.492460      -6120.037809       2.583131e+04   \n",
       "4             497.991393         -1.000000       8.029687e+03   \n",
       "...                  ...               ...                ...   \n",
       "1452657      1121.181565         -1.000000       1.793952e+07   \n",
       "1452658       489.962718       1499.463717       1.357787e+04   \n",
       "1452659       415.080357       1319.492035       1.540541e+04   \n",
       "1452660      1187.815753       1962.317406       1.280615e+04   \n",
       "1452661       859.370832       2156.663490       1.474349e+04   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -261597.321119          0.199572           0.208451   \n",
       "1           -313387.471075          0.797161           0.867292   \n",
       "2              7094.431505          0.851000           0.785438   \n",
       "3              9179.647412          0.864509           0.816404   \n",
       "4              2202.584636          0.745707           0.619200   \n",
       "...                    ...               ...                ...   \n",
       "1452657      268404.310201          0.619156           0.243761   \n",
       "1452658        1588.889464          0.779480           0.480554   \n",
       "1452659        3899.614072          0.217055           0.371308   \n",
       "1452660        4690.827782          0.664055           0.585775   \n",
       "1452661       -5276.683787          0.006667           0.469103   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.054236       -1.000000      -70.042430  297845.871189   \n",
       "1              -1.000000       -1.000000     -249.116969   67780.547523   \n",
       "2               0.200317       -1.000000        4.158316       8.379427   \n",
       "3               0.175113        1.055740        5.576106       9.812098   \n",
       "4               0.272827       -1.000000        1.684225       0.290936   \n",
       "...                  ...             ...             ...            ...   \n",
       "1452657        -1.000000       -1.000000     6895.394550    3992.122541   \n",
       "1452658         0.308518       -0.226912        3.135308      42.324922   \n",
       "1452659         0.259434       -0.553421        3.704308      31.696439   \n",
       "1452660         0.261460        0.070717        2.625166       1.385119   \n",
       "1452661         0.290925       -0.133063        1.199077       0.296332   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0                 -0.000596              True  clear           3  \n",
       "1                  0.182773              True  clear           3  \n",
       "2                  0.884464              True  clear           3  \n",
       "3                  0.885259              True  clear           3  \n",
       "4                  0.883868              True  clear           3  \n",
       "...                     ...               ...    ...         ...  \n",
       "1452657           -0.000199              True    XSS           1  \n",
       "1452658            0.176019              True    XSS           1  \n",
       "1452659           -0.415412              True    XSS           1  \n",
       "1452660           -0.254293              True    XSS           1  \n",
       "1452661           -0.649839              True    XSS           1  \n",
       "\n",
       "[1452662 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>1436840</th>\n",
       "      <td>SQL_injection</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450551</th>\n",
       "      <td>XSS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331195</th>\n",
       "      <td>backdoor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>clear</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331243</th>\n",
       "      <td>ddos</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345068</th>\n",
       "      <td>mitm</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345167</th>\n",
       "      <td>os_fingerprinting</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345445</th>\n",
       "      <td>password_attack</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416700</th>\n",
       "      <td>port_scanning</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436782</th>\n",
       "      <td>ransomware</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442698</th>\n",
       "      <td>uploading_attack</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445332</th>\n",
       "      <td>vulnerability_scanner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         LABEL  LABEL_code\n",
       "1436840          SQL_injection           0\n",
       "1450551                    XSS           1\n",
       "1331195               backdoor           2\n",
       "0                        clear           3\n",
       "1331243                   ddos           4\n",
       "1345068                   mitm           5\n",
       "1345167      os_fingerprinting           6\n",
       "1345445        password_attack           7\n",
       "1416700          port_scanning           8\n",
       "1436782             ransomware           9\n",
       "1442698       uploading_attack          10\n",
       "1445332  vulnerability_scanner          11"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"LABEL\", \"LABEL_code\"]].drop_duplicates().sort_values(\"LABEL_code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SQL_injection',\n",
       " 'XSS',\n",
       " 'backdoor',\n",
       " 'clear',\n",
       " 'ddos',\n",
       " 'mitm',\n",
       " 'os_fingerprinting',\n",
       " 'password_attack',\n",
       " 'port_scanning',\n",
       " 'ransomware',\n",
       " 'uploading_attack',\n",
       " 'vulnerability_scanner']"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1452662, 74)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 250000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     771449\n",
       "7.0      41215\n",
       "8.0      11713\n",
       "4.0       8036\n",
       "0.0       3429\n",
       "11.0      3016\n",
       "10.0      1502\n",
       "1.0       1230\n",
       "6.0        156\n",
       "5.0         55\n",
       "9.0         33\n",
       "2.0         29\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     330622\n",
       "7.0      17664\n",
       "8.0       5019\n",
       "4.0       3444\n",
       "0.0       1470\n",
       "11.0      1292\n",
       "10.0       644\n",
       "1.0        527\n",
       "6.0         67\n",
       "5.0         24\n",
       "9.0         14\n",
       "2.0         12\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     229124\n",
       "7.0      12376\n",
       "8.0       3350\n",
       "4.0       2345\n",
       "0.0        959\n",
       "11.0       911\n",
       "10.0       488\n",
       "1.0        354\n",
       "6.0         55\n",
       "5.0         20\n",
       "9.0         11\n",
       "2.0          7\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation[\"LABEL_code\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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, 9, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 10, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 50, 200, 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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [11:10<00:00,  6.70s/trial, best loss: -0.8040772037389706]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9392621131757972, 'gamma': 1.6233898884896047, 'max_depth': 8.0, 'min_child_weight': 1.0, 'n_estimators': 160.0, 'reg_alpha': 0.0, 'reg_lambda': 0.0021867641592837606}\n"
     ]
    }
   ],
   "source": [
    "df_to_tune = df_features.sample(n=100000)\n",
    "best_hyperparams = tunne_together_XGBoost_classification(df_to_tune, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       precision    recall  f1-score   support\n",
      "\n",
      "        SQL_injection     0.9932    0.9952    0.9942      1470\n",
      "                  XSS     1.0000    0.9943    0.9971       527\n",
      "             backdoor     1.0000    0.9167    0.9565        12\n",
      "                clear     1.0000    1.0000    1.0000    330622\n",
      "                 ddos     0.9980    0.9997    0.9988      3444\n",
      "                 mitm     0.9130    0.8750    0.8936        24\n",
      "    os_fingerprinting     0.0000    0.0000    0.0000        67\n",
      "      password_attack     0.9999    0.9998    0.9999     17664\n",
      "        port_scanning     0.9864    0.9986    0.9925      5019\n",
      "           ransomware     1.0000    0.7857    0.8800        14\n",
      "     uploading_attack     1.0000    0.9938    0.9969       644\n",
      "vulnerability_scanner     0.9900    0.9915    0.9907      1292\n",
      "\n",
      "             accuracy                         0.9997    360799\n",
      "            macro avg     0.9067    0.8792    0.8917    360799\n",
      "         weighted avg     0.9995    0.9997    0.9996    360799\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": 22,
   "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='macro')\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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "        SQL_injection     0.9946    0.9946    0.9946      1470\n",
      "                  XSS     0.9962    0.9981    0.9972       527\n",
      "             backdoor     1.0000    1.0000    1.0000        12\n",
      "                clear     1.0000    1.0000    1.0000    330622\n",
      "                 ddos     0.9985    0.9983    0.9984      3444\n",
      "                 mitm     1.0000    1.0000    1.0000        24\n",
      "    os_fingerprinting     0.0000    0.0000    0.0000        67\n",
      "      password_attack     0.9999    0.9998    0.9998     17664\n",
      "        port_scanning     0.9866    0.9986    0.9926      5019\n",
      "           ransomware     1.0000    1.0000    1.0000        14\n",
      "     uploading_attack     1.0000    0.9984    0.9992       644\n",
      "vulnerability_scanner     0.9930    0.9923    0.9926      1292\n",
      "\n",
      "             accuracy                         0.9997    360799\n",
      "            macro avg     0.9141    0.9150    0.9145    360799\n",
      "         weighted avg     0.9995    0.9997    0.9996    360799\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       precision    recall  f1-score   support\n",
      "\n",
      "        SQL_injection     0.9886    0.9917    0.9901       959\n",
      "                  XSS     1.0000    0.9915    0.9957       354\n",
      "             backdoor     1.0000    0.8571    0.9231         7\n",
      "                clear     1.0000    1.0000    1.0000    229124\n",
      "                 ddos     0.9966    0.9991    0.9979      2345\n",
      "                 mitm     1.0000    0.8500    0.9189        20\n",
      "    os_fingerprinting     0.0000    0.0000    0.0000        55\n",
      "      password_attack     0.9996    0.9998    0.9997     12376\n",
      "        port_scanning     0.9830    0.9991    0.9910      3350\n",
      "           ransomware     1.0000    1.0000    1.0000        11\n",
      "     uploading_attack     0.9980    1.0000    0.9990       488\n",
      "vulnerability_scanner     0.9933    0.9835    0.9884       911\n",
      "\n",
      "             accuracy                         0.9996    250000\n",
      "            macro avg     0.9132    0.8893    0.9003    250000\n",
      "         weighted avg     0.9994    0.9996    0.9995    250000\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IoT-egde_iiot-multiclass\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    report = classification_report(y_validation, y_pred, target_names=classifications_array, digits=4)\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, \"-\", accuracy, m_precision, m_recall, m_f1, w_precision, w_recall, w_f1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.27237788, 0.09259601, 0.0062893615, 0.08705115, 0.03556181, 0.0062893615]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BENFORD_LAW_PRESENTED    0.000000\n",
       "SIG_SPACES               0.000000\n",
       "CNT_ZEROS                0.000000\n",
       "SPECTRAL_SLOPE           0.000121\n",
       "NORMAL_DISTRIBUTION      0.000163\n",
       "                           ...   \n",
       "SPECTRAL_KURTOSIS        0.032690\n",
       "SCALED_ENTROPY           0.056774\n",
       "MEDIAN_SCALED_TIME       0.064668\n",
       "CNT_DISTRIBUTION         0.243945\n",
       "HURST_EXPONENT           0.256243\n",
       "Length: 73, dtype: float32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'SPECTRAL_SLOPE': 0.00012074251571903005,\n",
       " 'NORMAL_DISTRIBUTION': 0.00016324235184583813,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.00024175012367777526,\n",
       " 'ENTROPY': 0.0002736171300057322,\n",
       " 'VAL': 0.0003326934820506722,\n",
       " 'TIME': 0.00036072859074920416,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.00039601107710041106,\n",
       " 'SPECTRAL_ENERGY': 0.00041187278111465275,\n",
       " 'SPECTRAL_CENTROID': 0.0004663839063141495,\n",
       " 'PACKETS_REV': 0.0005150043871253729,\n",
       " 'MEAN_SCALED_TIME': 0.0005921369302086532,\n",
       " 'MODE': 0.0006092541152611375,\n",
       " 'BYTES_REV': 0.0006735775386914611,\n",
       " 'MIN': 0.0007675645174458623,\n",
       " 'PERIODICITY': 0.0008657326688989997,\n",
       " 'DIRECTIONS': 0.0009083311888389289,\n",
       " 'MIN_DIFFTIMES': 0.0009127558441832662,\n",
       " 'TIME_DISTRIBUTION': 0.0009555065771564841,\n",
       " 'Q3_SCALED_TIME': 0.0010021654888987541,\n",
       " 'MIN_POWER': 0.0011035213246941566,\n",
       " 'SPECTRAL_SPREAD': 0.0011128612095490098,\n",
       " 'MEAN': 0.0011829251889139414,\n",
       " 'GALTON_SKEWNESS': 0.0011852130992338061,\n",
       " 'DURATION': 0.0012245486723259091,\n",
       " 'SPECTRAL_ENTROPY': 0.001299031195230782,\n",
       " 'SPECTRAL_CREST': 0.001326177385635674,\n",
       " 'PERCENT_DEVIATION': 0.001373549341224134,\n",
       " 'PERIODICITY_SCDF': 0.001529900124296546,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0015720557421445847,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0019535452593117952,\n",
       " 'PERCENT_BELOW_MEAN': 0.0022675201762467623,\n",
       " 'SPECTRAL_BANDWIDTH': 0.002388415392488241,\n",
       " 'Q1': 0.0025035846047103405,\n",
       " 'PACKETS': 0.0025237202644348145,\n",
       " 'BYTES': 0.002577059203758836,\n",
       " 'SPECTRAL_FLUX': 0.0025793039239943027,\n",
       " 'MEAN_DIFFTIMES': 0.0027197005692869425,\n",
       " 'MAX_POWER_FREQ': 0.0029770496767014265,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.003243353683501482,\n",
       " 'AVERAGE_DISPERSION': 0.003878073999658227,\n",
       " 'BURSTINESS': 0.004007178824394941,\n",
       " 'VAR': 0.0040894257836043835,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.004152875859290361,\n",
       " 'Q1_SCALED_TIME': 0.00426887022331357,\n",
       " 'P_BENFORD': 0.004420898854732513,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.004686455242335796,\n",
       " 'SPECTRAL_SKEWNESS': 0.0054427217692136765,\n",
       " 'SPECTRAL_ROLLOFF': 0.006513002794235945,\n",
       " 'MEDIAN_DIFFTIMES': 0.006592642515897751,\n",
       " 'SWITCHING_METRIC': 0.006863269489258528,\n",
       " 'MAX_DIFFTIMES': 0.009042849764227867,\n",
       " 'BIGGEST_CNT_1_SEC': 0.01122771855443716,\n",
       " 'MAX_POWER': 0.011912994086742401,\n",
       " 'ROOT_MEAN_SQUARE': 0.012090899050235748,\n",
       " 'Q3': 0.01353402342647314,\n",
       " 'MEDIAN': 0.01400571409612894,\n",
       " 'TRANSIENTS': 0.014761586673557758,\n",
       " 'KURTOSIS': 0.015162494964897633,\n",
       " 'MIN_POWER_FREQ': 0.015177547000348568,\n",
       " 'MIN_MINUS_MAX': 0.01614738069474697,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.016876449808478355,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.018860606476664543,\n",
       " 'STDEV': 0.01921951211988926,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.024345763027668,\n",
       " 'MAX': 0.029187189415097237,\n",
       " 'SPECTRAL_KURTOSIS': 0.03268963098526001,\n",
       " 'SCALED_ENTROPY': 0.05677371472120285,\n",
       " 'MEDIAN_SCALED_TIME': 0.06466828286647797,\n",
       " 'CNT_DISTRIBUTION': 0.24394471943378448,\n",
       " 'HURST_EXPONENT': 0.256243497133255}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, cycles = 5, best_hyperparams=None):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(200000), 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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [22:04<00:00, 13.25s/trial, best loss: -0.777580163242621]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7618625767539282, 'gamma': 2.438009814547964, 'max_depth': 8.0, 'min_child_weight': 1.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.0022219119523410047}\n",
      "0.8830880539722589\n",
      "100%|██████████| 100/100 [12:23<00:00,  7.44s/trial, best loss: -0.8156303962480239]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.1327355775015746, 'gamma': 1.9356003218516116, 'max_depth': 8.0, 'min_child_weight': 2.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9349674851910655}\n",
      "0.900595894531585\n",
      "100%|██████████| 100/100 [11:55<00:00,  7.16s/trial, best loss: -0.890668439525529]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.16419672418124157, 'gamma': 1.9137278562673379, 'max_depth': 6.0, 'min_child_weight': 2.0, 'n_estimators': 50.0, 'reg_alpha': 0.0, 'reg_lambda': 0.0964616995516604}\n",
      "0.8939066929434856\n",
      "100%|██████████| 100/100 [10:12<00:00,  6.13s/trial, best loss: -0.8594381326267593]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5559051868181638, 'gamma': 1.026897616615025, 'max_depth': 8.0, 'min_child_weight': 1.0, 'n_estimators': 50.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6598834114770483}\n",
      "0.8886416041235728\n",
      "100%|██████████| 100/100 [14:00<00:00,  8.40s/trial, best loss: -0.7430157357789077]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.26547923598262885, 'gamma': 4.339664704031918, 'max_depth': 6.0, 'min_child_weight': 4.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5616093115693406}\n",
      "0.8719160528553974\n",
      "100%|██████████| 100/100 [15:11<00:00,  9.11s/trial, best loss: -0.792204430837597]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2419623713114908, 'gamma': 1.441978076344415, 'max_depth': 8.0, 'min_child_weight': 3.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.924155546910726}\n",
      "0.8936699925389048\n",
      "100%|██████████| 100/100 [13:13<00:00,  7.93s/trial, best loss: -0.659124494908525]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7799052846598122, 'gamma': 3.8031864021203505, 'max_depth': 6.0, 'min_child_weight': 5.0, 'n_estimators': 100.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3797869814211862}\n",
      "0.8918297994011838\n",
      "100%|██████████| 100/100 [04:14<00:00,  2.55s/trial, best loss: -0.7230644611477915]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2796115781083122, 'gamma': 5.589686438519393, 'max_depth': 6.0, 'min_child_weight': 7.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.20875387002779555}\n",
      "0.8938277676721557\n",
      "100%|██████████| 100/100 [10:10<00:00,  6.11s/trial, best loss: -0.7941812932818747]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4093202496164381, 'gamma': 1.6749271930946064, 'max_depth': 6.0, 'min_child_weight': 5.0, 'n_estimators': 100.0, 'reg_alpha': 0.0, 'reg_lambda': 0.47955112927415516}\n",
      "0.8926942043104775\n",
      "100%|██████████| 100/100 [13:19<00:00,  8.00s/trial, best loss: -0.8960344050975245]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6774434941205182, 'gamma': 5.707497294742144, 'max_depth': 6.0, 'min_child_weight': 1.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.38039928357780206}\n",
      "0.898361594715572\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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = \"IoT-egde_iiot-multiclass\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        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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