04_2_feature_selection_for_all_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ryk\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data importance list:\n",
      "all_data \n",
      "                              importance\n",
      "Features                               \n",
      "Bwd Packet Length Std          0.246627\n",
      "Flow Bytes/s                   0.178777\n",
      "Total Length of Fwd Packets    0.102417\n",
      "Fwd Packet Length Std          0.063889\n",
      "Flow IAT Std                   0.009898\n",
      "Flow IAT Min                   0.006946\n",
      "Fwd IAT Total                  0.005121\n",
      "Flow Duration                  0.004150\n",
      "Bwd Packet Length Max          0.004007\n",
      "Flow IAT Max                   0.003579\n",
      "Flow IAT Mean                  0.003266\n",
      "Total Length of Bwd Packets    0.001305\n",
      "Fwd Packet Length Min          0.000670\n",
      "Bwd Packet Length Mean         0.000582\n",
      "Flow Packets/s                 0.000541\n",
      "Fwd Packet Length Mean         0.000526\n",
      "Total Backward Packets         0.000169\n",
      "Total Fwd Packets              0.000138\n",
      "Fwd Packet Length Max          0.000125\n",
      "Bwd Packet Length Min          0.000084 \n",
      "\n",
      "\n",
      "\n",
      "all_data=[\"Bwd Packet Length Std\",\"Flow Bytes/s\",\"Total Length of Fwd Packets\",\"Fwd Packet Length Std\",\"Flow IAT Std\"]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "mission accomplished!\n",
      "Total operation time: =  25929.417772769928 secomds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  \"all_data.csv\" file is required for the operation of the program.\n",
    "##  \"all_data.csv\" file must be located in the same directory as the program.\n",
    "\n",
    "\n",
    "##  the purpose of this code is to determine which features to use in the machine learning phase.\n",
    "##  for this purpose, the importance weights of the attacks are calculated.\n",
    "##  this calculation was made using sklearn-RandomForestRegressor.\n",
    "\n",
    "##  the some codes parts used for calculation and graphing are taken from the following site.\n",
    "##  http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import sklearn as sk\n",
    "import time\n",
    "seconds = time.time()\n",
    "\n",
    "def folder(f_name): #this function creates a folder named \"feaure_pics\" in the program directory.\n",
    "    try:\n",
    "        if not os.path.exists(f_name):\n",
    "            os.makedirs(f_name)\n",
    "    except OSError:\n",
    "        print (\"The folder could not be created!\")\n",
    "        \n",
    "\n",
    "# CSV files names:\n",
    "csv_files=[\"all_data.csv\"]# It creates a list of file names in the \"attacks\" folder.\n",
    "\n",
    "# Headers of column\n",
    "main_labels=[\"Flow Duration\",\"Total Fwd Packets\",   \"Total Backward Packets\",\"Total Length of Fwd Packets\",\"Total Length of Bwd Packets\",\"Fwd Packet Length Max\",\"Fwd Packet Length Min\",\n",
    "   \"Fwd Packet Length Mean\",\"Fwd Packet Length Std\",\"Bwd Packet Length Max\",\"Bwd Packet Length Min\",\"Bwd Packet Length Mean\",\"Bwd Packet Length Std\",\n",
    "   \"Flow Bytes/s\",\"Flow Packets/s\",\"Flow IAT Mean\",\"Flow IAT Std\",\"Flow IAT Max\",\"Flow IAT Min\",\"Fwd IAT Total\",\"Fwd IAT Mean\",\"Fwd IAT Std\",\"Fwd IAT Max\",\n",
    "   \"Fwd IAT Min\",\"Bwd IAT Total\",\"Bwd IAT Mean\",\"Bwd IAT Std\",\"Bwd IAT Max\",\"Bwd IAT Min\",\"Fwd PSH Flags\",\"Bwd PSH Flags\",\"Fwd URG Flags\",\"Bwd URG Flags\",\n",
    "   \"Fwd Header Length\",\"Bwd Header Length\",\"Fwd Packets/s\",\"Bwd Packets/s\",\"Min Packet Length\",\"Max Packet Length\",\"Packet Length Mean\",\"Packet Length Std\",\n",
    "   \"Packet Length Variance\",\"FIN Flag Count\",\"SYN Flag Count\",\"RST Flag Count\",\"PSH Flag Count\",\"ACK Flag Count\",\"URG Flag Count\",\"CWE Flag Count\",\n",
    "   \"ECE Flag Count\",\"Down/Up Ratio\",\"Average Packet Size\",\"Avg Fwd Segment Size\",\"Avg Bwd Segment Size\",\"Fwd Avg Bytes/Bulk\",\n",
    "   \"Fwd Avg Packets/Bulk\",\"Fwd Avg Bulk Rate\",\"Bwd Avg Bytes/Bulk\",\"Bwd Avg Packets/Bulk\",\"Bwd Avg Bulk Rate\",\"Subflow Fwd Packets\",\"Subflow Fwd Bytes\",\n",
    "   \"Subflow Bwd Packets\",\"Subflow Bwd Bytes\",\"Init_Win_bytes_forward\",\"Init_Win_bytes_backward\",\"act_data_pkt_fwd\",\n",
    "   \"min_seg_size_forward\",\"Active Mean\",\"Active Std\",\"Active Max\",\"Active Min\",\n",
    "    \"Idle Mean\",\"Idle Std\",\"Idle Max\", \"Idle Min\",\"Label\"]\n",
    "\n",
    "ths = open(\"importance_list_all_data.csv\", \"w\")\n",
    "folder(\"./feaure_pics/\")\n",
    "for j in csv_files:\n",
    "    df=pd.read_csv(j,usecols=main_labels)\n",
    "    df=df.fillna(0)\n",
    "    attack_or_not=[]\n",
    "    for i in df[\"Label\"]:#it changes the normal label to \"1\" and the attack tag to \"0\" for use in the machine learning algorithm\n",
    "        if i ==\"BENIGN\":\n",
    "            attack_or_not.append(1)\n",
    "        else:\n",
    "            attack_or_not.append(0)           \n",
    "    df[\"Label\"]=attack_or_not\n",
    "\n",
    "    y = df[\"Label\"].values\n",
    "    del df[\"Label\"]\n",
    "    X = df.values\n",
    "   \n",
    "\n",
    "    #computing the feature importances\n",
    "    forest = sk.ensemble.RandomForestRegressor(n_estimators=250,random_state=0)\n",
    "    forest.fit(X, y)\n",
    "    importances = forest.feature_importances_\n",
    "    std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n",
    "                 axis=0)\n",
    "    indices = np.argsort(importances)[::-1]\n",
    "    refclasscol=list(df.columns.values)\n",
    "    impor_bars = pd.DataFrame({'Features':refclasscol[0:20],'importance':importances[0:20]})\n",
    "    impor_bars = impor_bars.sort_values('importance',ascending=False).set_index('Features')\n",
    "    plt.rcParams['figure.figsize'] = (10, 5)\n",
    "    impor_bars.plot.bar();\n",
    "    #printing the feature importances  \n",
    "    count=0\n",
    "    fea_ture=j[0:-4]+\"=[\"\n",
    "    for i in impor_bars.index:\n",
    "        fea_ture=fea_ture+\"\\\"\"+str(i)+\"\\\",\"\n",
    "        count+=1\n",
    "        if count==5:\n",
    "            fea_ture=fea_ture[0:-1]+\"]\"\n",
    "            break     \n",
    "    print(j[0:-4],\"importance list:\")\n",
    "    print(j[0:-4],\"\\n\",impor_bars.head(20),\"\\n\\n\\n\")\n",
    "    print(fea_ture)\n",
    "    plt.title(j[0:-4]+\" Attack - Feature Importance\")\n",
    "    plt.ylabel('Importance')\n",
    "    plt.savefig(\"./feaure_pics/\"+j[0:-4]+\".pdf\",bbox_inches='tight', papertype = 'a4', orientation = 'portrait', format = 'pdf')\n",
    "    ths.write((  fea_ture ) )\n",
    "    plt.tight_layout()\n",
    "    #plt.show()\n",
    "    print(\"-----------------------------------------------------------------------------------------------\\n\\n\\n\\n\")\n",
    "\n",
    "    \n",
    "print(\"mission accomplished!\")\n",
    "print(\"Total operation time: = \",time.time()- seconds ,\"secomds\")\n",
    "ths.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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