05_3_machine_learning_implementation_with_7_feature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "all_data          Naive Bayes        0.82            0.8             0.82            0.81            1.6258         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          QDA                0.38            0.83            0.38            0.41            1.9025         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          Random Forest      0.94            0.95            0.94            0.94            20.512         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          ID3                0.95            0.95            0.95            0.95            11.5528        \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          AdaBoost           0.94            0.94            0.94            0.94            144.1661       \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          MLP                0.84            0.82            0.84            0.79            51.7993        \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          Nearest Neighbors  0.97            0.97            0.97            0.97            1038.2531      \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mission accomplished!\n",
      "Total operation time: =  12714.621414899826 seconds\n"
     ]
    }
   ],
   "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",
    "##  the purpose of this program is to apply machine learning algorithms to the dataset and observe the performance of algorithms.\n",
    "##  the algorithms used are:Naive Bayes, QDA, Random Forest, ID3, AdaBoost, MLP, Nearest Neighbors\n",
    "##  As the program display output data include: file name, machine learning algorithm name, accuracy,Precision, Recall, F1-score,Time\n",
    "##  the program will create a CSV file that prints the results and a folder containing graphics.\n",
    "\n",
    "##  the some codes parts used for calculation and graphing are taken from the following site.\n",
    "##  http://scikit-learn.org\n",
    "\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "            \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import csv\n",
    "import time\n",
    "import warnings\n",
    "import math\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "result=\"./results/results_3.csv\" #a CSV file is named in which the results are saved.\n",
    "csv_files=[\"all_data.csv\"]# CSV files names: #The names of the dataset files (csv_files).\n",
    "path=\"\"\n",
    "repetition=10\n",
    "\n",
    "\n",
    "def folder(f_name): #this function creates a folder named \"results\" and \"result_graph_1\" 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",
    "folder_name=\"./results/\"\n",
    "folder(folder_name)\n",
    "folder_name=\"./results/result_graph_3/\"\n",
    "folder(folder_name)\n",
    "\n",
    "\n",
    "#The machine learning algorithms to be used are defined in a dictionary (ml_list).\n",
    "ml_list={\n",
    "\"Naive Bayes\":GaussianNB(),\n",
    "\"QDA\":QDA(),\n",
    "\"Random Forest\":RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "\"ID3\" :DecisionTreeClassifier(max_depth=5,criterion=\"entropy\"),\n",
    "\"AdaBoost\":AdaBoostClassifier(),\n",
    "\"MLP\":MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500),\n",
    "\"Nearest Neighbors\":KNeighborsClassifier(3)}\n",
    "\n",
    "\n",
    "\n",
    "#list of all columns to be imported\n",
    "# the 7 features with the highest importance weight selected by the file \"04_2_feature_selection_for_attack_files.py\" are used here. (+ Label Feature)\n",
    "\n",
    "features={\"all_data\":[\"Bwd Packet Length Std\", \"Flow Bytes/s\", \"Total Length of Fwd Packets\", \"Fwd Packet Length Std\",\n",
    "     \"Flow IAT Std\", \"Flow IAT Min\", \"Fwd IAT Total\",\"Label\"]}\n",
    "\n",
    "seconds=time.time()#time stamp for all processing time\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "with open(result, \"w\", newline=\"\",encoding=\"utf-8\") as f:#a CSV file is created to save the results obtained.\n",
    "    wrt = csv.writer(f)\n",
    "    wrt.writerow([\"File\",\"ML algorithm\",\"accuracy\",\"Precision\", \"Recall\" , \"F1-score\",\"Time\"])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for j in csv_files: #this loop runs on the list containing the filenames.Operations are repeated for all attack files\n",
    "    print ('%-17s %-17s  %-15s %-15s %-15s %-15s %-15s' % (\"File\",\"ML algorithm\",\"accuracy\",\"Precision\", \"Recall\" , \"F1-score\",\"Time\"))# print output header   \n",
    "    feature_list=list(features[j[0:-4]])\n",
    "    df=pd.read_csv(path+j,usecols=feature_list)#read an attack file.\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",
    "    \n",
    "    y = df[\"Label\"] #this section separates the label and the data into two separate pieces, as Label=y Data=X \n",
    "    del df[\"Label\"]\n",
    "    feature_list.remove('Label')\n",
    "    X = df[feature_list]\n",
    "\n",
    "    \n",
    "    for ii in ml_list: #this loop runs on the list containing the machine learning algorithm names. Operations are repeated for all the 7 algorithm\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        t_time=[]\n",
    "        for i in range(repetition): # This loop allows cross-validation and machine learning algorithm to be repeated 10 times\n",
    "            second=time.time()#time stamp for processing time\n",
    "\n",
    "            # cross-validation\n",
    "            X_train, X_test, y_train, y_test = train_test_split(X, y,#  data (X) and labels (y) are divided into 2 parts to be sent to the machine learning algorithm (80% train,%20 test). \n",
    "                test_size = 0.20, random_state = repetition)#  So, in total there are 4 tracks: training data(X_train), training tag (y_train), test data(X_test) and test tag(y_test).\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "            clf = ml_list[ii]#choose algorithm from ml_list dictionary                                                                          \n",
    "            clf.fit(X_train, y_train)\n",
    "            predict =clf.predict(X_test)\n",
    "        \n",
    "            #makes \"classification report\" and assigns the precision, f-measure, and recall values.s.    \n",
    "            \n",
    "            f_1=f1_score(y_test, predict, average='macro')\n",
    "            pr=precision_score(y_test, predict, average='macro')\n",
    "            rc=recall_score(y_test, predict, average='macro')\n",
    "\n",
    "            precision.append(float(pr))\n",
    "            recall.append(float(rc))\n",
    "            f1.append(float(f_1))\n",
    "            accuracy.append(clf.score(X_test, y_test))\n",
    "            t_time.append(float((time.time()-second)) )\n",
    "\n",
    "\n",
    "            \n",
    "        print ('%-17s %-17s  %-15s %-15s %-15s %-15s %-15s' % (j[0:-4],ii,str(round(np.mean(accuracy),2)),str(round(np.mean(precision),2)), \n",
    "            str(round(np.mean(recall),2)),str(round(np.mean(f1),2)),str(round(np.mean(t_time),4))))#the result of the ten repetitions is printed on the screen.\n",
    "\n",
    "        with open(result, \"a\", newline=\"\",encoding=\"utf-8\") as f: # all the values found are saved in the opened file.\n",
    "            wrt = csv.writer(f)\n",
    "            for i in range(0,len(t_time)):\n",
    "                wrt.writerow([j[0:-4],ii,accuracy[i],precision[i],recall[i],f1[i],t_time[i]])#file name, algorithm name, precision, recall and f-measure are writed in CSV file\n",
    "   \n",
    "     # In this section, Box graphics are created for the results of machine learning algorithms and saved in the feaure_graph folder.\n",
    "        plt.boxplot(f1)\n",
    "        plt.title(\"All Dataset - \" +str(ii))\n",
    "        plt.ylabel('F-measure')\n",
    "        plt.savefig(folder_name+j[0:-4]+str(ii)+\".pdf\",bbox_inches='tight', papertype = 'a4', orientation = 'portrait', format = 'pdf')\n",
    "        plt.show()# you can remove the # sign if you want to see the graphics simultaneously\n",
    "\n",
    "print(\"mission accomplished!\")\n",
    "print(\"Total operation time: = \",time.time()- seconds ,\"seconds\")\n"
   ]
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