vpn_iscx_multiclass.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VPN multiclass classification using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: multiclass classification of VPN\n",
    "\n",
    "Raw data from: ISCX-VPN-2016  [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016."
   ]
  },
  {
   "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/vpn_iscx_multiclass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>134.170.25.26.</td>\n",
       "      <td>10.8.8.178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10.8.8.178.</td>\n",
       "      <td>134.170.25.26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>54269</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10.8.8.178.</td>\n",
       "      <td>157.56.52.13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>56909</td>\n",
       "      <td>...</td>\n",
       "      <td>-341097.010413</td>\n",
       "      <td>0.608031</td>\n",
       "      <td>0.434314</td>\n",
       "      <td>0.233900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.388248</td>\n",
       "      <td>2292.470299</td>\n",
       "      <td>-0.371507</td>\n",
       "      <td>0.007280</td>\n",
       "      <td>CHAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>157.56.52.13.</td>\n",
       "      <td>10.8.8.178</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>40011</td>\n",
       "      <td>...</td>\n",
       "      <td>89822.936222</td>\n",
       "      <td>0.094477</td>\n",
       "      <td>0.492656</td>\n",
       "      <td>0.284938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.541214</td>\n",
       "      <td>138.490636</td>\n",
       "      <td>0.142047</td>\n",
       "      <td>0.165118</td>\n",
       "      <td>CHAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10.8.8.178.</td>\n",
       "      <td>134.170.25.26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>54269</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHAT</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>23073</th>\n",
       "      <td>23073</td>\n",
       "      <td>74.125.226.56</td>\n",
       "      <td>10.8.8.138</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1751</td>\n",
       "      <td>1673</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>11037.684045</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.508077</td>\n",
       "      <td>0.289115</td>\n",
       "      <td>0.004800</td>\n",
       "      <td>1.217957</td>\n",
       "      <td>0.081599</td>\n",
       "      <td>-0.204229</td>\n",
       "      <td>0.991587</td>\n",
       "      <td>FileTrasfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23074</th>\n",
       "      <td>23074</td>\n",
       "      <td>74.125.226.56.</td>\n",
       "      <td>10.8.8.138</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>4779.019623</td>\n",
       "      <td>0.597104</td>\n",
       "      <td>0.569086</td>\n",
       "      <td>0.275657</td>\n",
       "      <td>0.253250</td>\n",
       "      <td>2.251129</td>\n",
       "      <td>29.391239</td>\n",
       "      <td>0.918039</td>\n",
       "      <td>0.116570</td>\n",
       "      <td>FileTrasfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23075</th>\n",
       "      <td>23075</td>\n",
       "      <td>74.125.226.56.</td>\n",
       "      <td>10.8.8.138</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FileTrasfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23076</th>\n",
       "      <td>23076</td>\n",
       "      <td>10.8.8.138.</td>\n",
       "      <td>74.125.226.56</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>51142</td>\n",
       "      <td>...</td>\n",
       "      <td>6458.036444</td>\n",
       "      <td>0.044215</td>\n",
       "      <td>0.564764</td>\n",
       "      <td>0.279904</td>\n",
       "      <td>0.207302</td>\n",
       "      <td>2.285338</td>\n",
       "      <td>48.245202</td>\n",
       "      <td>0.917641</td>\n",
       "      <td>0.079507</td>\n",
       "      <td>FileTrasfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23077</th>\n",
       "      <td>23077</td>\n",
       "      <td>10.8.8.138.</td>\n",
       "      <td>74.125.226.56</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>51143</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FileTrasfer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23078 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          DST_IP         SRC_IP  PACKETS  PACKETS_REV  BYTES  \\\n",
       "0               0  134.170.25.26.     10.8.8.178        1            0      0   \n",
       "1               1     10.8.8.178.  134.170.25.26        1            0      1   \n",
       "2               2     10.8.8.178.   157.56.52.13        3            0      2   \n",
       "3               3   157.56.52.13.     10.8.8.178        3            0      4   \n",
       "4               4     10.8.8.178.  134.170.25.26        1            0    149   \n",
       "...           ...             ...            ...      ...          ...    ...   \n",
       "23073       23073   74.125.226.56     10.8.8.138        4            3   1751   \n",
       "23074       23074  74.125.226.56.     10.8.8.138       14            0    887   \n",
       "23075       23075  74.125.226.56.     10.8.8.138        4            0      0   \n",
       "23076       23076     10.8.8.138.  74.125.226.56       12            0    289   \n",
       "23077       23077     10.8.8.138.  74.125.226.56        2            0      0   \n",
       "\n",
       "       BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  ...  \\\n",
       "0              0  1.433357e+09  1.433357e+09       443  ...   \n",
       "1              0  1.433357e+09  1.433357e+09     54269  ...   \n",
       "2              0  1.433357e+09  1.433357e+09     56909  ...   \n",
       "3              0  1.433357e+09  1.433357e+09     40011  ...   \n",
       "4              0  1.433357e+09  1.433357e+09     54269  ...   \n",
       "...          ...           ...           ...       ...  ...   \n",
       "23073       1673  1.432323e+09  1.432323e+09       443  ...   \n",
       "23074          0  1.432323e+09  1.432323e+09       443  ...   \n",
       "23075          0  1.432323e+09  1.432323e+09       443  ...   \n",
       "23076          0  1.432323e+09  1.432323e+09     51142  ...   \n",
       "23077          0  1.432323e+09  1.432323e+09     51143  ...   \n",
       "\n",
       "       SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0                    NaN          0.006667                NaN   \n",
       "1                    NaN          0.006667                NaN   \n",
       "2         -341097.010413          0.608031           0.434314   \n",
       "3           89822.936222          0.094477           0.492656   \n",
       "4                    NaN          0.006667                NaN   \n",
       "...                  ...               ...                ...   \n",
       "23073       11037.684045          0.006667           0.508077   \n",
       "23074        4779.019623          0.597104           0.569086   \n",
       "23075                NaN          0.006667                NaN   \n",
       "23076        6458.036444          0.044215           0.564764   \n",
       "23077                NaN          0.006667                NaN   \n",
       "\n",
       "       SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0                  NaN             NaN             NaN            NaN   \n",
       "1                  NaN             NaN             NaN            NaN   \n",
       "2             0.233900             NaN       55.388248    2292.470299   \n",
       "3             0.284938             NaN       10.541214     138.490636   \n",
       "4                  NaN             NaN             NaN            NaN   \n",
       "...                ...             ...             ...            ...   \n",
       "23073         0.289115        0.004800        1.217957       0.081599   \n",
       "23074         0.275657        0.253250        2.251129      29.391239   \n",
       "23075              NaN             NaN             NaN            NaN   \n",
       "23076         0.279904        0.207302        2.285338      48.245202   \n",
       "23077              NaN             NaN             NaN            NaN   \n",
       "\n",
       "       SPECTRAL_BANDWIDTH  PERIODICITY_SCDF        LABEL  \n",
       "0                0.000000               NaN         CHAT  \n",
       "1                0.000000               NaN         CHAT  \n",
       "2               -0.371507          0.007280         CHAT  \n",
       "3                0.142047          0.165118         CHAT  \n",
       "4                0.000000               NaN         CHAT  \n",
       "...                   ...               ...          ...  \n",
       "23073           -0.204229          0.991587  FileTrasfer  \n",
       "23074            0.918039          0.116570  FileTrasfer  \n",
       "23075            0.000000               NaN  FileTrasfer  \n",
       "23076            0.917641          0.079507  FileTrasfer  \n",
       "23077            0.000000               NaN  FileTrasfer  \n",
       "\n",
       "[23078 rows x 84 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "]\n",
    "\n",
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 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>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>134.170.25.26.</td>\n",
       "      <td>10.8.8.178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>54269</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>CHAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10.8.8.178.</td>\n",
       "      <td>134.170.25.26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>54269</td>\n",
       "      <td>443</td>\n",
       "      <td>61</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>CHAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10.8.8.178.</td>\n",
       "      <td>157.56.52.13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>56909</td>\n",
       "      <td>40011</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.967213</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.673992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>7262.666667</td>\n",
       "      <td>6.666667e-01</td>\n",
       "      <td>1.632993e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340934</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>7.299830</td>\n",
       "      <td>0.488201</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.411289</td>\n",
       "      <td>21.411289</td>\n",
       "      <td>10.705644</td>\n",
       "      <td>10.705644</td>\n",
       "      <td>0.488201</td>\n",
       "      <td>20.923088</td>\n",
       "      <td>0.511667</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-671.921077</td>\n",
       "      <td>47.390194</td>\n",
       "      <td>0.979537</td>\n",
       "      <td>0.608031</td>\n",
       "      <td>0.855600</td>\n",
       "      <td>9.630094</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4278.000802</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.382317e+07</td>\n",
       "      <td>-341097.010413</td>\n",
       "      <td>0.608031</td>\n",
       "      <td>0.434314</td>\n",
       "      <td>0.233900</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>55.388248</td>\n",
       "      <td>2292.470299</td>\n",
       "      <td>-0.371507</td>\n",
       "      <td>True</td>\n",
       "      <td>CHAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>157.56.52.13.</td>\n",
       "      <td>10.8.8.178</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>40011</td>\n",
       "      <td>56909</td>\n",
       "      <td>61</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.967742</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>1.639344</td>\n",
       "      <td>1</td>\n",
       "      <td>1.639344</td>\n",
       "      <td>61.340579</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>3721.333333</td>\n",
       "      <td>3.333333e-01</td>\n",
       "      <td>8.164966e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.336774</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>7.119728</td>\n",
       "      <td>0.218230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.140954</td>\n",
       "      <td>21.140954</td>\n",
       "      <td>10.570477</td>\n",
       "      <td>10.570477</td>\n",
       "      <td>0.218230</td>\n",
       "      <td>20.922724</td>\n",
       "      <td>0.505215</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>184</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.349542</td>\n",
       "      <td>10.273650</td>\n",
       "      <td>0.189440</td>\n",
       "      <td>0.331487</td>\n",
       "      <td>0.974617</td>\n",
       "      <td>0.250358</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4873.086578</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.265305e+06</td>\n",
       "      <td>89822.936222</td>\n",
       "      <td>0.094477</td>\n",
       "      <td>0.492656</td>\n",
       "      <td>0.284938</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>10.541214</td>\n",
       "      <td>138.490636</td>\n",
       "      <td>0.142047</td>\n",
       "      <td>True</td>\n",
       "      <td>CHAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10.8.8.178.</td>\n",
       "      <td>134.170.25.26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>54269</td>\n",
       "      <td>443</td>\n",
       "      <td>209</td>\n",
       "      <td>209.0</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>209.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>209</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>209</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>CHAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23073</th>\n",
       "      <td>23073</td>\n",
       "      <td>74.125.226.56</td>\n",
       "      <td>10.8.8.138</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1751</td>\n",
       "      <td>1673</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>54576</td>\n",
       "      <td>549</td>\n",
       "      <td>354.0</td>\n",
       "      <td>600.748700</td>\n",
       "      <td>360899</td>\n",
       "      <td>0.045009</td>\n",
       "      <td>98.5</td>\n",
       "      <td>897.0</td>\n",
       "      <td>89</td>\n",
       "      <td>1410</td>\n",
       "      <td>1321</td>\n",
       "      <td>1410</td>\n",
       "      <td>109.425993</td>\n",
       "      <td>491</td>\n",
       "      <td>89.435337</td>\n",
       "      <td>781.601104</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>-1.433212</td>\n",
       "      <td>2.152314</td>\n",
       "      <td>0.257603</td>\n",
       "      <td>6.589060e-10</td>\n",
       "      <td>8.540397e-10</td>\n",
       "      <td>-0.128366</td>\n",
       "      <td>1.347383</td>\n",
       "      <td>2.521641</td>\n",
       "      <td>0.898227</td>\n",
       "      <td>0.504721</td>\n",
       "      <td>False</td>\n",
       "      <td>0.796548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.613228</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.168102</td>\n",
       "      <td>0.210011</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.248443</td>\n",
       "      <td>0.274126</td>\n",
       "      <td>0.045688</td>\n",
       "      <td>0.019216</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.207983</td>\n",
       "      <td>0.219670</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3844</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.234791</td>\n",
       "      <td>0.294739</td>\n",
       "      <td>0.210896</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.241995</td>\n",
       "      <td>0.007854</td>\n",
       "      <td>0.23479</td>\n",
       "      <td>1209.972945</td>\n",
       "      <td>2475.858896</td>\n",
       "      <td>6.098856e+04</td>\n",
       "      <td>11037.684045</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.508077</td>\n",
       "      <td>0.289115</td>\n",
       "      <td>0.004800</td>\n",
       "      <td>1.217957</td>\n",
       "      <td>0.081599</td>\n",
       "      <td>-0.204229</td>\n",
       "      <td>True</td>\n",
       "      <td>FileTrasfer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23074</th>\n",
       "      <td>23074</td>\n",
       "      <td>74.125.226.56.</td>\n",
       "      <td>10.8.8.138</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51142</td>\n",
       "      <td>123</td>\n",
       "      <td>60.0</td>\n",
       "      <td>139.935700</td>\n",
       "      <td>19583</td>\n",
       "      <td>0.064410</td>\n",
       "      <td>60.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>60</td>\n",
       "      <td>577</td>\n",
       "      <td>517</td>\n",
       "      <td>60</td>\n",
       "      <td>113.768861</td>\n",
       "      <td>82</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>182.759600</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.450207</td>\n",
       "      <td>2.208157</td>\n",
       "      <td>2.253750</td>\n",
       "      <td>1.303336e-07</td>\n",
       "      <td>1.465247e-07</td>\n",
       "      <td>-1.625000</td>\n",
       "      <td>7.981362</td>\n",
       "      <td>2.093069</td>\n",
       "      <td>0.549744</td>\n",
       "      <td>0.521654</td>\n",
       "      <td>False</td>\n",
       "      <td>0.711683</td>\n",
       "      <td>0.005316</td>\n",
       "      <td>0.739488</td>\n",
       "      <td>0.180433</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>32.746354</td>\n",
       "      <td>0.421261</td>\n",
       "      <td>0.415704</td>\n",
       "      <td>45.847472</td>\n",
       "      <td>181.487506</td>\n",
       "      <td>13.960577</td>\n",
       "      <td>0.207535</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>45.223991</td>\n",
       "      <td>0.308698</td>\n",
       "      <td>False</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.739488</td>\n",
       "      <td>1487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038569</td>\n",
       "      <td>0.262924</td>\n",
       "      <td>0.076995</td>\n",
       "      <td>0.995033</td>\n",
       "      <td>0.116796</td>\n",
       "      <td>0.046058</td>\n",
       "      <td>0.11473</td>\n",
       "      <td>583.982290</td>\n",
       "      <td>1747.538451</td>\n",
       "      <td>1.717066e+04</td>\n",
       "      <td>4779.019623</td>\n",
       "      <td>0.597104</td>\n",
       "      <td>0.569086</td>\n",
       "      <td>0.275657</td>\n",
       "      <td>0.253250</td>\n",
       "      <td>2.251129</td>\n",
       "      <td>29.391239</td>\n",
       "      <td>0.918039</td>\n",
       "      <td>True</td>\n",
       "      <td>FileTrasfer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23075</th>\n",
       "      <td>23075</td>\n",
       "      <td>74.125.226.56.</td>\n",
       "      <td>10.8.8.138</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51143</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.499995</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>5.220416</td>\n",
       "      <td>10.221076</td>\n",
       "      <td>0.219642</td>\n",
       "      <td>10.440947</td>\n",
       "      <td>10.440947</td>\n",
       "      <td>3.480316</td>\n",
       "      <td>0.219871</td>\n",
       "      <td>0.219642</td>\n",
       "      <td>10.001434</td>\n",
       "      <td>0.347982</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>0.219642</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>FileTrasfer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23076</th>\n",
       "      <td>23076</td>\n",
       "      <td>10.8.8.138.</td>\n",
       "      <td>74.125.226.56</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>51142</td>\n",
       "      <td>443</td>\n",
       "      <td>84</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.519227</td>\n",
       "      <td>2072</td>\n",
       "      <td>-0.297105</td>\n",
       "      <td>60.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>60</td>\n",
       "      <td>213</td>\n",
       "      <td>153</td>\n",
       "      <td>60</td>\n",
       "      <td>54.189556</td>\n",
       "      <td>32</td>\n",
       "      <td>38.095238</td>\n",
       "      <td>94.707004</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.527250</td>\n",
       "      <td>4.217998</td>\n",
       "      <td>1.393118</td>\n",
       "      <td>8.835561e-07</td>\n",
       "      <td>1.015129e-06</td>\n",
       "      <td>-0.230769</td>\n",
       "      <td>5.449900</td>\n",
       "      <td>1.584963</td>\n",
       "      <td>0.442114</td>\n",
       "      <td>0.545926</td>\n",
       "      <td>False</td>\n",
       "      <td>0.630211</td>\n",
       "      <td>0.022116</td>\n",
       "      <td>0.728843</td>\n",
       "      <td>0.210067</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>38.124458</td>\n",
       "      <td>0.466022</td>\n",
       "      <td>0.431921</td>\n",
       "      <td>91.056439</td>\n",
       "      <td>181.487463</td>\n",
       "      <td>16.498860</td>\n",
       "      <td>0.207628</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>45.226207</td>\n",
       "      <td>0.364808</td>\n",
       "      <td>False</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.728843</td>\n",
       "      <td>769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065482</td>\n",
       "      <td>0.390564</td>\n",
       "      <td>0.077193</td>\n",
       "      <td>0.994835</td>\n",
       "      <td>0.170900</td>\n",
       "      <td>0.068661</td>\n",
       "      <td>0.11864</td>\n",
       "      <td>854.498574</td>\n",
       "      <td>2088.203664</td>\n",
       "      <td>2.041723e+04</td>\n",
       "      <td>6458.036444</td>\n",
       "      <td>0.044215</td>\n",
       "      <td>0.564764</td>\n",
       "      <td>0.279904</td>\n",
       "      <td>0.207302</td>\n",
       "      <td>2.285338</td>\n",
       "      <td>48.245202</td>\n",
       "      <td>0.917641</td>\n",
       "      <td>True</td>\n",
       "      <td>FileTrasfer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23077</th>\n",
       "      <td>23077</td>\n",
       "      <td>10.8.8.138.</td>\n",
       "      <td>74.125.226.56</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>1.432323e+09</td>\n",
       "      <td>51143</td>\n",
       "      <td>443</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>5.110671</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>10.221341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>FileTrasfer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23078 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          DST_IP         SRC_IP  PACKETS  PACKETS_REV  BYTES  \\\n",
       "0               0  134.170.25.26.     10.8.8.178        1            0      0   \n",
       "1               1     10.8.8.178.  134.170.25.26        1            0      1   \n",
       "2               2     10.8.8.178.   157.56.52.13        3            0      2   \n",
       "3               3   157.56.52.13.     10.8.8.178        3            0      4   \n",
       "4               4     10.8.8.178.  134.170.25.26        1            0    149   \n",
       "...           ...             ...            ...      ...          ...    ...   \n",
       "23073       23073   74.125.226.56     10.8.8.138        4            3   1751   \n",
       "23074       23074  74.125.226.56.     10.8.8.138       14            0    887   \n",
       "23075       23075  74.125.226.56.     10.8.8.138        4            0      0   \n",
       "23076       23076     10.8.8.138.  74.125.226.56       12            0    289   \n",
       "23077       23077     10.8.8.138.  74.125.226.56        2            0      0   \n",
       "\n",
       "       BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  MEAN  \\\n",
       "0              0  1.433357e+09  1.433357e+09       443     54269    60   \n",
       "1              0  1.433357e+09  1.433357e+09     54269       443    61   \n",
       "2              0  1.433357e+09  1.433357e+09     56909     40011    60   \n",
       "3              0  1.433357e+09  1.433357e+09     40011     56909    61   \n",
       "4              0  1.433357e+09  1.433357e+09     54269       443   209   \n",
       "...          ...           ...           ...       ...       ...   ...   \n",
       "23073       1673  1.432323e+09  1.432323e+09       443     54576   549   \n",
       "23074          0  1.432323e+09  1.432323e+09       443     51142   123   \n",
       "23075          0  1.432323e+09  1.432323e+09       443     51143    60   \n",
       "23076          0  1.432323e+09  1.432323e+09     51142       443    84   \n",
       "23077          0  1.432323e+09  1.432323e+09     51143       443    60   \n",
       "\n",
       "       MEDIAN       STDEV     VAR  BURSTINESS     Q1     Q3  MIN   MAX  \\\n",
       "0        60.0   60.000000       0    0.000000   60.0   60.0   60    60   \n",
       "1        61.0   61.000000       0    0.000000   61.0   61.0   61    61   \n",
       "2        60.0    1.000000       2   -0.967213   60.0   61.0   60    62   \n",
       "3        62.0    1.000000       1   -0.967742   61.0   62.0   60    62   \n",
       "4       209.0  209.000000       0    0.000000  209.0  209.0  209   209   \n",
       "...       ...         ...     ...         ...    ...    ...  ...   ...   \n",
       "23073   354.0  600.748700  360899    0.045009   98.5  897.0   89  1410   \n",
       "23074    60.0  139.935700   19583    0.064410   60.0  108.0   60   577   \n",
       "23075    60.0    0.000000       0   -1.000000   60.0   60.0   60    60   \n",
       "23076    60.0   45.519227    2072   -0.297105   60.0   99.0   60   213   \n",
       "23077    60.0    0.000000       0   -1.000000   60.0   60.0   60    60   \n",
       "\n",
       "       MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                  0    60                100.000000                   0   \n",
       "1                  0    61                100.000000                   0   \n",
       "2                  2    60                  1.666667                   0   \n",
       "3                  2    62                  1.639344                   1   \n",
       "4                  0   209                100.000000                   0   \n",
       "...              ...   ...                       ...                 ...   \n",
       "23073           1321  1410                109.425993                 491   \n",
       "23074            517    60                113.768861                  82   \n",
       "23075              0    60                  0.000000                   0   \n",
       "23076            153    60                 54.189556                  32   \n",
       "23077              0    60                  0.000000                   0   \n",
       "\n",
       "       PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0               0.000000         60.000000            0.000000   \n",
       "1               0.000000         61.000000            0.000000   \n",
       "2               0.000000         60.673992            0.000000   \n",
       "3               1.639344         61.340579            0.333333   \n",
       "4               0.000000        209.000000            0.000000   \n",
       "...                  ...               ...                 ...   \n",
       "23073          89.435337        781.601104            0.714286   \n",
       "23074          66.666667        182.759600            0.857143   \n",
       "23075           0.000000         60.000000            0.000000   \n",
       "23076          38.095238         94.707004            0.666667   \n",
       "23077           0.000000         60.000000            0.000000   \n",
       "\n",
       "       PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                0.000000              0.000000              2.000000   \n",
       "1                0.000000              0.000000              2.000000   \n",
       "2                0.333333              0.000000            120.000000   \n",
       "3                0.666667             -1.000000            121.000000   \n",
       "4                0.000000              0.000000              2.000000   \n",
       "...                   ...                   ...                   ...   \n",
       "23073            0.285714             -1.433212              2.152314   \n",
       "23074            0.142857              0.450207              2.208157   \n",
       "23075            0.000000              0.000000              0.000000   \n",
       "23076            0.333333              0.527250              4.217998   \n",
       "23077            0.000000              0.000000              0.000000   \n",
       "\n",
       "       FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                 -3.000000                0.000000e+00   \n",
       "1                 -3.000000                0.000000e+00   \n",
       "2               7262.666667                6.666667e-01   \n",
       "3               3721.333333                3.333333e-01   \n",
       "4                 -3.000000                0.000000e+00   \n",
       "...                     ...                         ...   \n",
       "23073              0.257603                6.589060e-10   \n",
       "23074              2.253750                1.303336e-07   \n",
       "23075              0.000000                0.000000e+00   \n",
       "23076              1.393118                8.835561e-07   \n",
       "23077              0.000000                0.000000e+00   \n",
       "\n",
       "       FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                   -0.000000e+00         0.000000  0.000000  0.000000   \n",
       "1                   -0.000000e+00         0.000000  0.000000  0.000000   \n",
       "2                    1.632993e+00         1.000000  5.333333  0.918296   \n",
       "3                    8.164966e-01         1.000000  1.000000  0.918296   \n",
       "4                   -0.000000e+00         0.000000  0.000000  0.000000   \n",
       "...                           ...              ...       ...       ...   \n",
       "23073                8.540397e-10        -0.128366  1.347383  2.521641   \n",
       "23074                1.465247e-07        -1.625000  7.981362  2.093069   \n",
       "23075                0.000000e+00         0.000000  0.000000  0.000000   \n",
       "23076                1.015129e-06        -0.230769  5.449900  1.584963   \n",
       "23077                0.000000e+00         0.000000  0.000000  0.000000   \n",
       "\n",
       "       SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0            0.000000        1.000000                  False   0.301030   \n",
       "1            0.000000        1.000000                  False   0.301030   \n",
       "2            0.579380        0.535744                  False   0.477121   \n",
       "3            0.579380        0.535744                  False   0.477121   \n",
       "4            0.000000        1.000000                  False   0.301030   \n",
       "...               ...             ...                    ...        ...   \n",
       "23073        0.898227        0.504721                  False   0.796548   \n",
       "23074        0.549744        0.521654                  False   0.711683   \n",
       "23075        0.000000        1.000000                  False   0.301030   \n",
       "23076        0.442114        0.545926                  False   0.630211   \n",
       "23077        0.000000        1.000000                  False   0.301030   \n",
       "\n",
       "       NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                 0.000000          0.000000           0.500000   \n",
       "1                 0.000000          0.000000           0.500000   \n",
       "2                 0.000000          0.000000           0.340934   \n",
       "3                 0.000000          0.000000           0.336774   \n",
       "4                 0.000000          0.000000           0.500000   \n",
       "...                    ...               ...                ...   \n",
       "23073             0.000000          0.000000           0.613228   \n",
       "23074             0.005316          0.739488           0.180433   \n",
       "23075             0.000000          0.000000           0.499995   \n",
       "23076             0.022116          0.728843           0.210067   \n",
       "23077             0.000000          0.000000           0.500000   \n",
       "\n",
       "       AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                      0.875000          0.000000            0.000000   \n",
       "1                      0.875000          0.000000            0.000000   \n",
       "2                      0.750000          7.299830            0.488201   \n",
       "3                      0.750000          7.119728            0.218230   \n",
       "4                      0.875000          0.000000            0.000000   \n",
       "...                         ...               ...                 ...   \n",
       "23073                  0.291667          0.168102            0.210011   \n",
       "23074                  0.687500         32.746354            0.421261   \n",
       "23075                  0.875000          5.220416           10.221076   \n",
       "23076                  0.750000         38.124458            0.466022   \n",
       "23077                  0.875000          5.110671           10.221341   \n",
       "\n",
       "       Q1_SCALED_TIME  Q3_SCALED_TIME    DURATION  MEAN_DIFFTIMES  \\\n",
       "0            0.000000        0.000000    0.000000        0.000000   \n",
       "1            0.000000        0.000000    0.000000        0.000000   \n",
       "2            0.000000       21.411289   21.411289       10.705644   \n",
       "3            0.000000       21.140954   21.140954       10.570477   \n",
       "4            0.000000        0.000000    0.000000        0.000000   \n",
       "...               ...             ...         ...             ...   \n",
       "23073        0.000660        0.248443    0.274126        0.045688   \n",
       "23074        0.415704       45.847472  181.487506       13.960577   \n",
       "23075        0.219642       10.440947   10.440947        3.480316   \n",
       "23076        0.431921       91.056439  181.487463       16.498860   \n",
       "23077        0.000000       10.221341   10.221341       10.221341   \n",
       "\n",
       "       MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0              0.000000       0.000000       0.000000               0.000000   \n",
       "1              0.000000       0.000000       0.000000               0.000000   \n",
       "2             10.705644       0.488201      20.923088               0.511667   \n",
       "3             10.570477       0.218230      20.922724               0.505215   \n",
       "4              0.000000       0.000000       0.000000               0.000000   \n",
       "...                 ...            ...            ...                    ...   \n",
       "23073          0.019216       0.000660       0.207983               0.219670   \n",
       "23074          0.207535       0.000169      45.223991               0.308698   \n",
       "23075          0.219871       0.219642      10.001434               0.347982   \n",
       "23076          0.207628       0.000038      45.226207               0.364808   \n",
       "23077         10.221341      10.221341      10.221341               1.000000   \n",
       "\n",
       "       SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0           False          0.000000       False        0.0   \n",
       "1           False          0.000000       False        0.0   \n",
       "2           False          1.000000       False        0.0   \n",
       "3           False          1.000000       False        0.0   \n",
       "4           False          0.000000       False        0.0   \n",
       "...           ...               ...         ...        ...   \n",
       "23073       False          2.000000       False        0.0   \n",
       "23074       False          1.384615       False        0.0   \n",
       "23075       False          0.000000       False        0.0   \n",
       "23076       False          0.909091       False        0.0   \n",
       "23077       False          0.000000       False        0.0   \n",
       "\n",
       "       CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                 0.000000                 60    1.000000        False    0   \n",
       "1                 0.000000                 61    1.000000        False    0   \n",
       "2                 0.000000                182    1.000000        False    0   \n",
       "3                 0.000000                184    1.000000        False    0   \n",
       "4                 0.000000                209    1.000000        False    0   \n",
       "...                    ...                ...         ...          ...  ...   \n",
       "23073             0.000000               3844    0.571429        False    0   \n",
       "23074             0.739488               1487    1.000000        False    0   \n",
       "23075             0.000000                120    1.000000         True   60   \n",
       "23076             0.728843                769    1.000000        False    0   \n",
       "23077             0.000000                 60    1.000000        False    0   \n",
       "\n",
       "           TIME   MIN_POWER  MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0      0.000000   -1.000000  -1.000000       -1.000000       -1.000000   \n",
       "1      0.000000   -1.000000  -1.000000       -1.000000       -1.000000   \n",
       "2      0.000000 -671.921077  47.390194        0.979537        0.608031   \n",
       "3      0.000000   -1.349542  10.273650        0.189440        0.331487   \n",
       "4      0.000000   -1.000000  -1.000000       -1.000000       -1.000000   \n",
       "...         ...         ...        ...             ...             ...   \n",
       "23073  0.000000    0.234791   0.294739        0.210896        0.006667   \n",
       "23074  0.000000    0.038569   0.262924        0.076995        0.995033   \n",
       "23075  0.219642   -1.000000  -1.000000       -1.000000       -1.000000   \n",
       "23076  0.000000    0.065482   0.390564        0.077193        0.994835   \n",
       "23077  0.000000   -1.000000  -1.000000       -1.000000       -1.000000   \n",
       "\n",
       "       POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0       -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "1       -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "2        0.855600   9.630094     1.00000      4278.000802         -1.000000   \n",
       "3        0.974617   0.250358     1.00000      4873.086578         -1.000000   \n",
       "4       -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "...           ...        ...         ...              ...               ...   \n",
       "23073    0.241995   0.007854     0.23479      1209.972945       2475.858896   \n",
       "23074    0.116796   0.046058     0.11473       583.982290       1747.538451   \n",
       "23075   -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "23076    0.170900   0.068661     0.11864       854.498574       2088.203664   \n",
       "23077   -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "\n",
       "       SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0          -1.000000e+00          -1.000000          0.006667   \n",
       "1          -1.000000e+00          -1.000000          0.006667   \n",
       "2           2.382317e+07     -341097.010413          0.608031   \n",
       "3           3.265305e+06       89822.936222          0.094477   \n",
       "4          -1.000000e+00          -1.000000          0.006667   \n",
       "...                  ...                ...               ...   \n",
       "23073       6.098856e+04       11037.684045          0.006667   \n",
       "23074       1.717066e+04        4779.019623          0.597104   \n",
       "23075      -1.000000e+00          -1.000000          0.006667   \n",
       "23076       2.041723e+04        6458.036444          0.044215   \n",
       "23077      -1.000000e+00          -1.000000          0.006667   \n",
       "\n",
       "       SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0              -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "1              -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "2               0.434314         0.233900       -1.000000       55.388248   \n",
       "3               0.492656         0.284938       -1.000000       10.541214   \n",
       "4              -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "...                  ...              ...             ...             ...   \n",
       "23073           0.508077         0.289115        0.004800        1.217957   \n",
       "23074           0.569086         0.275657        0.253250        2.251129   \n",
       "23075          -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "23076           0.564764         0.279904        0.207302        2.285338   \n",
       "23077          -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "\n",
       "       SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF        LABEL  \\\n",
       "0          -1.000000            0.000000             False         CHAT   \n",
       "1          -1.000000            0.000000             False         CHAT   \n",
       "2        2292.470299           -0.371507              True         CHAT   \n",
       "3         138.490636            0.142047              True         CHAT   \n",
       "4          -1.000000            0.000000             False         CHAT   \n",
       "...              ...                 ...               ...          ...   \n",
       "23073       0.081599           -0.204229              True  FileTrasfer   \n",
       "23074      29.391239            0.918039              True  FileTrasfer   \n",
       "23075      -1.000000            0.000000             False  FileTrasfer   \n",
       "23076      48.245202            0.917641              True  FileTrasfer   \n",
       "23077      -1.000000            0.000000             False  FileTrasfer   \n",
       "\n",
       "       LABEL_code  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "...           ...  \n",
       "23073           2  \n",
       "23074           2  \n",
       "23075           2  \n",
       "23076           2  \n",
       "23077           2  \n",
       "\n",
       "[23078 rows x 85 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>0</th>\n",
       "      <td>CHAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>EMAIL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>FileTrasfer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22318</th>\n",
       "      <td>P2P</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15686</th>\n",
       "      <td>STREAMING</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>VOIP</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             LABEL  LABEL_code\n",
       "0             CHAT           0\n",
       "117          EMAIL           1\n",
       "3626   FileTrasfer           2\n",
       "22318          P2P           3\n",
       "15686    STREAMING           4\n",
       "578           VOIP           5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"LABEL\", \"LABEL_code\"]].drop_duplicates().sort_values(\"LABEL_code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CHAT', 'EMAIL', 'FileTrasfer', 'P2P', 'STREAMING', 'VOIP']"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23078, 77)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 3000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    8691\n",
       "0.0    2766\n",
       "2.0    1049\n",
       "4.0     843\n",
       "3.0     430\n",
       "1.0     275\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    3726\n",
       "0.0    1186\n",
       "2.0     449\n",
       "4.0     361\n",
       "3.0     184\n",
       "1.0     118\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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='weighted')\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, 20, 3),\n",
    "        'gamma': hp.uniform ('gamma', 1,10),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,5),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 50, 450, 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 [03:25<00:00,  2.05s/trial, best loss: -0.9381500885062855]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7881772277371246, 'gamma': 4.777740484357047, 'max_depth': 21.0, 'min_child_weight': 20.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3223649294186349}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, 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",
      "        CHAT     0.9011    0.9216    0.9112      1186\n",
      "       EMAIL     0.9175    0.7542    0.8279       118\n",
      " FileTrasfer     0.8033    0.7639    0.7831       449\n",
      "         P2P     0.9474    0.8804    0.9127       184\n",
      "   STREAMING     0.8871    0.9141    0.9004       361\n",
      "        VOIP     0.9698    0.9745    0.9722      3726\n",
      "\n",
      "    accuracy                         0.9376      6024\n",
      "   macro avg     0.9044    0.8681    0.8846      6024\n",
      "weighted avg     0.9372    0.9376    0.9371      6024\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='weighted')\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",
      "        CHAT     0.9259    0.9275    0.9267      1186\n",
      "       EMAIL     0.9510    0.8220    0.8818       118\n",
      " FileTrasfer     0.8377    0.7817    0.8088       449\n",
      "         P2P     0.9309    0.9511    0.9409       184\n",
      "   STREAMING     0.8816    0.9280    0.9042       361\n",
      "        VOIP     0.9736    0.9791    0.9763      3726\n",
      "\n",
      "    accuracy                         0.9472      6024\n",
      "   macro avg     0.9168    0.8982    0.9064      6024\n",
      "weighted avg     0.9468    0.9472    0.9468      6024\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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        CHAT     0.9252    0.9252    0.9252       615\n",
      "       EMAIL     0.9365    0.8676    0.9008        68\n",
      " FileTrasfer     0.7937    0.7468    0.7696       237\n",
      "         P2P     0.9684    0.9293    0.9485        99\n",
      "   STREAMING     0.8525    0.8966    0.8739       174\n",
      "        VOIP     0.9714    0.9790    0.9752      1807\n",
      "\n",
      "    accuracy                         0.9407      3000\n",
      "   macro avg     0.9080    0.8908    0.8989      3000\n",
      "weighted avg     0.9401    0.9407    0.9403      3000\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",
    "report = classification_report(y_validation, y_pred, target_names=classifications_array, digits=4)\n",
    "print(report)\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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"VPN-ISCX-multiclass\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\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": 31,
   "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_def = 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_def]\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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3005709, 0.1264793, 0.06897452, 0.3933004, 0.067946374, 0.042728353]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF       0.000000\n",
       "PERIODICITY            0.000000\n",
       "CNT_NZ_DISTRIBUTION    0.000000\n",
       "CNT_ZEROS              0.000000\n",
       "SIG_SPACES             0.000000\n",
       "                         ...   \n",
       "MIN_DIFFTIMES          0.036627\n",
       "VAR                    0.050067\n",
       "MIN_POWER              0.051801\n",
       "SPECTRAL_ROLLOFF       0.053550\n",
       "POWER_MODE             0.151434\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.002634631935507059,\n",
       " 'MEAN': 0.0032892378512769938,\n",
       " 'DIRECTIONS': 0.003481148276478052,\n",
       " 'PERCENT_BELOW_MEAN': 0.0036691497080028057,\n",
       " 'PACKETS': 0.004005896858870983,\n",
       " 'SPECTRAL_CREST': 0.00403002230450511,\n",
       " 'MAX_POWER_FREQ': 0.004030315205454826,\n",
       " 'GALTON_SKEWNESS': 0.004491055384278297,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0045867799781262875,\n",
       " 'TIME': 0.004690993577241898,\n",
       " 'Q3_SCALED_TIME': 0.00479487981647253,\n",
       " 'TIME_DISTRIBUTION': 0.005004911683499813,\n",
       " 'P_BENFORD': 0.005060912109911442,\n",
       " 'MIN': 0.005174332298338413,\n",
       " 'STDEV': 0.0053658438846468925,\n",
       " 'ROOT_MEAN_SQUARE': 0.0056589520536363125,\n",
       " 'SPECTRAL_ENERGY': 0.005867436528205872,\n",
       " 'MAX_DIFFTIMES': 0.006026970688253641,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0060988483019173145,\n",
       " 'MEAN_SCALED_TIME': 0.0063852714374661446,\n",
       " 'MODE': 0.006518321577459574,\n",
       " 'BURSTINESS': 0.0065203155390918255,\n",
       " 'POWER_MEAN': 0.006542160641402006,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.006652273237705231,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.006898118648678064,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.006930292118340731,\n",
       " 'SPECTRAL_ENTROPY': 0.006952105090022087,\n",
       " 'BYTES': 0.0070935641415417194,\n",
       " 'Q1_SCALED_TIME': 0.007123309653252363,\n",
       " 'SCALED_ENTROPY': 0.007211670279502869,\n",
       " 'Q1': 0.0073362085968256,\n",
       " 'MIN_MINUS_MAX': 0.007502111606299877,\n",
       " 'BYTES_REV': 0.0075659784488379955,\n",
       " 'SPECTRAL_SLOPE': 0.007610313128679991,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.008115548640489578,\n",
       " 'NORMAL_DISTRIBUTION': 0.008191143162548542,\n",
       " 'BIGGEST_CNT_1_SEC': 0.008990258909761906,\n",
       " 'TRANSIENTS': 0.009285470470786095,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.009647891856729984,\n",
       " 'SPECTRAL_KURTOSIS': 0.009693966247141361,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.009819543920457363,\n",
       " 'MEDIAN_SCALED_TIME': 0.010062390938401222,\n",
       " 'PERCENT_ABOVE_MEAN': 0.010328496806323528,\n",
       " 'MAX_POWER': 0.010947335511446,\n",
       " 'SPECTRAL_SPREAD': 0.01128081139177084,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.011817216873168945,\n",
       " 'MIN_POWER_FREQ': 0.012164119631052017,\n",
       " 'MAX': 0.012412224896252155,\n",
       " 'SPECTRAL_CENTROID': 0.012858282774686813,\n",
       " 'HURST_EXPONENT': 0.013160168193280697,\n",
       " 'AVERAGE_DISPERSION': 0.013356114737689495,\n",
       " 'POWER_STD': 0.014867516234517097,\n",
       " 'MEDIAN_DIFFTIMES': 0.01530208345502615,\n",
       " 'DURATION': 0.015620875172317028,\n",
       " 'PERCENT_DEVIATION': 0.0160668957978487,\n",
       " 'MEAN_DIFFTIMES': 0.01642119698226452,\n",
       " 'Q3': 0.016688326373696327,\n",
       " 'KURTOSIS': 0.016795525327324867,\n",
       " 'SWITCHING_METRIC': 0.01957978866994381,\n",
       " 'VAL': 0.02191872149705887,\n",
       " 'SPECTRAL_FLUX': 0.022449692711234093,\n",
       " 'ENTROPY': 0.023448871448636055,\n",
       " 'PACKETS_REV': 0.02406291291117668,\n",
       " 'MEDIAN': 0.027458474040031433,\n",
       " 'CNT_DISTRIBUTION': 0.03090510703623295,\n",
       " 'MIN_DIFFTIMES': 0.036626771092414856,\n",
       " 'VAR': 0.05006687715649605,\n",
       " 'MIN_POWER': 0.05180075019598007,\n",
       " 'SPECTRAL_ROLLOFF': 0.05355026200413704,\n",
       " 'POWER_MODE': 0.15143392980098724}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, cycles = 25, best_hyperparams=None):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features, selected_features, verbose=False)\n",
    "    X_dos=df_features[selected_features]  # Features\n",
    "    y_dos=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_dos, y_dos, test_size=0.30, stratify=y_dos) # 70% training and 30% test\n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        F1 = f1_score(y_test, y_pred, average='macro')\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    print(F1)\n",
    "\n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    \n",
    "    report = classification_report(y_validation,y_pred)\n",
    "    return report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [02:14<00:00,  1.35s/trial, best loss: -0.9303991632875537]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.11979390452599004, 'gamma': 1.3691682126255973, 'max_depth': 21.0, 'min_child_weight': 3.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5164713433115986}\n",
      "0.8794369802640195\n",
      "100%|██████████| 100/100 [01:54<00:00,  1.14s/trial, best loss: -0.9467427079073832]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.3552121530556301, 'gamma': 1.0336725090167898, 'max_depth': 18.0, 'min_child_weight': 6.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.07830505923788728}\n",
      "0.8965068105155064\n",
      "100%|██████████| 100/100 [02:21<00:00,  1.42s/trial, best loss: -0.9382727973051201]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4374148477063704, 'gamma': 1.49700079783369, 'max_depth': 21.0, 'min_child_weight': 5.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.306473651591038}\n",
      "0.8897691166332612\n",
      "100%|██████████| 100/100 [01:27<00:00,  1.14trial/s, best loss: -0.9183524607458663]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9752648126618929, 'gamma': 1.9558561863371888, 'max_depth': 21.0, 'min_child_weight': 6.0, 'n_estimators': 150.0, 'reg_alpha': 5.0, 'reg_lambda': 0.13353621372899954}\n",
      "0.8485285801561858\n",
      "100%|██████████| 100/100 [02:02<00:00,  1.23s/trial, best loss: -0.940185316410915]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.30920326687864713, 'gamma': 1.0054055446085282, 'max_depth': 21.0, 'min_child_weight': 9.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.15369246676645515}\n",
      "0.8860569376541227\n",
      "100%|██████████| 100/100 [01:59<00:00,  1.20s/trial, best loss: -0.9464590042078971]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6710979074218913, 'gamma': 1.0284488018726372, 'max_depth': 9.0, 'min_child_weight': 1.0, 'n_estimators': 450.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5158953603034919}\n",
      "0.8983343649904872\n",
      "100%|██████████| 100/100 [02:06<00:00,  1.26s/trial, best loss: -0.9449599172382878]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7157309499396532, 'gamma': 1.5870224304456129, 'max_depth': 15.0, 'min_child_weight': 10.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7565356326314085}\n",
      "0.8937273223359008\n",
      "100%|██████████| 100/100 [01:28<00:00,  1.13trial/s, best loss: -0.927720836886486]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.36321807978293574, 'gamma': 1.015204525616593, 'max_depth': 21.0, 'min_child_weight': 18.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4527493771175162}\n",
      "0.8757010288251305\n",
      "100%|██████████| 100/100 [01:34<00:00,  1.05trial/s, best loss: -0.9344335765129455]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.964414248373003, 'gamma': 1.8034382268219022, 'max_depth': 18.0, 'min_child_weight': 6.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6064389395693367}\n",
      "0.8807929903087666\n",
      "100%|██████████| 100/100 [02:14<00:00,  1.34s/trial, best loss: -0.9313481849897104]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.16834225922085513, 'gamma': 2.5323093049468475, 'max_depth': 21.0, 'min_child_weight': 7.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8892658739084831}\n",
      "0.8797052598509314\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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = \"VPN-ISCX-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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}