05_4_machine_learning_implementation_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "all_data          Naive Bayes        0.87            0.86            0.87            0.86            1.8255         \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                0.88            0.87            0.88            0.86            2.3696         \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                0.87            0.82            0.87            0.83            59.6933        \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            0.94            0.94            0.94            19.0899        \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            9.5107         \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            135.2455       \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            1626.8325      \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: =  18561.56910943985 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",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\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",
    "\n",
    "def folder(f_name): #this function creates a folder named \"feaure_graph\" 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",
    "result=\"./results/results_Final.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_Final/\"\n",
    "folder(folder_name)\n",
    "\n",
    "\n",
    "\n",
    "# the 20 features selected by the file \"04_2_feature_selection_for_attack_files.py\" are used here. (+ Label Feature)\n",
    "usecols=[\"Bwd Packet Length Std\",\"Flow Bytes/s\",\"Total Length of Fwd Packets\",\"Fwd Packet Length Std\",\"Flow IAT Std\",\n",
    "\"Flow IAT Min\",\"Fwd IAT Total\",\"Flow Duration\",\"Bwd Packet Length Max\",\"Flow IAT Max\",\"Flow IAT Mean\",\"Total Length of Bwd Packets\",\n",
    "\"Fwd Packet Length Min\",\"Bwd Packet Length Mean\",\"Flow Packets/s\",\"Fwd Packet Length Mean\",\"Total Backward Packets\",\"Total Fwd Packets\",\n",
    "\"Fwd Packet Length Max\",\"Bwd Packet Length Min\",'Label']\n",
    "\n",
    "\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",
    "\"MLP\":MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500),\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",
    "\"Nearest Neighbors\":KNeighborsClassifier(3)}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# the features to be used for Random Forest,ID3,AdaBoost,Nearest Neighbors is defined in a list(others).\n",
    "# the first 7 of the features created by the file \"04_2_feature_selection_for_attack_files.py\" are used here.\n",
    "others=[\"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\"]\n",
    "\n",
    "#In this part different sets of properties for machine learning methods are defined as follows:\n",
    "#For \"Naive Bayes\", \"QDA\", and \"MLP\", each method has a different feature list.\n",
    "#other algorithms (Random Forest, ID3, AdaBoost, and Nearest Neighbors) use\n",
    "#the first 7 of the features created by the file \"04_2_feature_selection_for_attack_files.py\" are used here.\n",
    "algorithms_features={\"Naive Bayes\":['Bwd Packet Length Std', 'Total Length of Fwd Packets', 'Flow IAT Min', 'Fwd Packet Length Min', 'Flow Packets/s', 'Fwd Packet Length Mean'] ,\n",
    "\"QDA\":['Bwd Packet Length Std', 'Flow Bytes/s', 'Total Length of Fwd Packets', 'Flow IAT Min'], \n",
    "\"MLP\":['Bwd Packet Length Std', 'Flow Bytes/s', 'Total Length of Fwd Packets', 'Fwd Packet Length Std',\n",
    "'Flow IAT Min', 'Bwd Packet Length Max','Fwd Packet Length Min', 'Bwd Packet Length Mean',\n",
    "'Total Backward Packets', 'Total Fwd Packets', 'Fwd Packet Length Max', 'Bwd Packet Length Min'],\n",
    "\n",
    "\n",
    "#these algorithms use the features in the \"others\" list\n",
    "\"Random Forest\":others,\n",
    "\"ID3\" :others,\n",
    "\"AdaBoost\":others,\n",
    "\"Nearest Neighbors\":others}\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",
    "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=usecols\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",
    "    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",
    "        X = df[algorithms_features[ii]]\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. \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",
    "\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",
    "\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",
    "            \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 avarage 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",
    "\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",
    "\n",
    "\n"
   ]
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