├── CMakeLists.txt ├── LICENSE ├── README.md ├── dataset-scripts ├── .directory ├── dataset-concatenate.py ├── dataset-preprocessing.py └── dataset-stats.py ├── joblibs ├── clf_ab.joblib ├── clf_bnb.joblib ├── clf_dt.joblib ├── clf_gnb.joblib ├── clf_rf.joblib ├── clf_svc.joblib ├── old-joblibs │ ├── clf_ab.joblib │ ├── clf_bnb.joblib │ ├── clf_dt.joblib │ ├── clf_gnb.joblib │ ├── clf_rf.joblib │ ├── clf_svc.joblib │ └── scaler.joblib └── scaler.joblib ├── jupyter-notebooks ├── .directory ├── .ipynb_checkpoints │ └── formatter-checkpoint.ipynb ├── AdaBoost.csv ├── Bernoulli NB.csv ├── Decision Tree.csv ├── Gaussian NB.csv ├── Linear SVC.csv ├── Random Forest.csv ├── formatter.ipynb ├── results.txt └── results_gs.txt ├── ml_classifiers.cc ├── ml_classifiers.h ├── ml_classifiers.py └── tmp ├── timeouted_connections.txt └── timeouted_connections_results.txt /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required ( VERSION 3.4.3 ) 2 | project ( ml_classifiers CXX ) 3 | 4 | set (CMAKE_CXX_STANDARD 11) 5 | set (CMAKE_CXX_STANDARD_REQUIRED ON) 6 | set (CMAKE_CXX_EXTENSIONS OFF) 7 | 8 | set (CMAKE_INCLUDE_PATH ${CMAKE_INCLUDE_PATH} "/home/lnutimura/snort_src/boost_1_67_0") 9 | #set (CMAKE_INCLUDE_PATH ${CMAKE_INCLUDE_PATH} "/usr/include/python3.7") 10 | 11 | if ( APPLE ) 12 | set ( CMAKE_MACOSX_RPATH OFF ) 13 | endif ( APPLE ) 14 | 15 | include ( FindPkgConfig ) 16 | pkg_search_module ( SNORT3 REQUIRED snort>=3 ) 17 | 18 | add_library ( 19 | ml_classifiers MODULE 20 | ml_classifiers.cc 21 | ml_classifiers.h 22 | ) 23 | 24 | if ( APPLE ) 25 | set_target_properties ( 26 | ml_classifiers 27 | PROPERTIES 28 | LINK_FLAGS "-undefined dynamic_lookup" 29 | ) 30 | endif ( APPLE ) 31 | 32 | set_target_properties ( 33 | ml_classifiers 34 | PROPERTIES 35 | PREFIX "" 36 | ) 37 | 38 | find_package ( Python3 COMPONENTS Interpreter Development ) 39 | if ( Python3_FOUND ) 40 | find_package ( Boost COMPONENTS python${Python3_VERSION_MAJOR}${Python3_VERSION_MINOR} ) 41 | endif ( Python3_FOUND ) 42 | 43 | message ( "[*] PYTHON_LIBRARY_DIRS: ${Python3_LIBRARY_DIRS}" ) 44 | message ( "[*] PYTHON_LIBRARIES: ${Python3_LIBRARIES}" ) 45 | message ( "[*] PYTHON_EXECUTABLE: ${Python3_EXECUTABLE}" ) 46 | message ( "[*] PYTHON_INCLUDE_DIRS: ${Python3_INCLUDE_DIRS}" ) 47 | 48 | message ( "[*] BOOST_LIBRARY_DIRS: ${Boost_LIBRARY_DIRS}" ) 49 | message ( "[*] BOOST_LIBRARIES: ${Boost_LIBRARIES}" ) 50 | message ( "[*] BOOST_INCLUDE_DIRS: ${Boost_INCLUDE_DIRS}" ) 51 | 52 | include_directories ( ${Python3_INCLUDE_DIRS} ${Boost_INCLUDE_DIRS} ) 53 | target_link_libraries ( ml_classifiers ${Python3_LIBRARIES} ${Boost_LIBRARIES} ) 54 | 55 | target_include_directories ( 56 | ml_classifiers PUBLIC 57 | ${SNORT3_INCLUDE_DIRS} 58 | ) 59 | 60 | install ( 61 | TARGETS ml_classifiers 62 | LIBRARY 63 | DESTINATION "${CMAKE_INSTALL_LIBDIR}/${CMAKE_PROJECT_NAME}/inspectors" 64 | ) 65 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ml_classifiers 2 | **ml_classifiers** is a Snort 3 Machine Learning-based Inspector for Network Traffic Bi-directional Flow Classification. 3 | 4 | It employs several machine learning models previously trained on [**CICIDS2017**](https://www.unb.ca/cic/datasets/ids-2017.html) to classify bi-directional flows in real time, completely replacing the Snort 3's default signature-based (or rule-based) detection approach. 5 | 6 | **Trained classifiers:** 7 | * Gaussian/Bernoulli Naive Bayes; 8 | * Linear Support Vector Machine; 9 | * Decision Tree; 10 | * Random Forest; 11 | * AdaBoost. 12 | 13 | This project was developed for research purposes of my master's thesis. 14 | -------------------------------------------------------------------------------- /dataset-scripts/.directory: -------------------------------------------------------------------------------- 1 | [Dolphin] 2 | PreviewsShown=false 3 | Timestamp=2019,11,4,19,24,31 4 | Version=4 5 | -------------------------------------------------------------------------------- /dataset-scripts/dataset-concatenate.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | # This script concatenates the content of every .csv in this 4 | # folder to a single file, called 'CIC-IDS-2017.csv'. 5 | 6 | import glob 7 | import subprocess 8 | 9 | if __name__ == '__main__': 10 | print('[*] Attempting to clean any output file previously created by this script...') 11 | 12 | sp = subprocess.Popen(['rm', 'CIC-IDS-2017.csv'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) 13 | sp_output, sp_error = sp.communicate() 14 | 15 | if sp.returncode != 0: 16 | print('\t[*] No previous file was removed (Return code: {}).'.format(str(sp.returncode))) 17 | else: 18 | print('\t[*] Successfully removed a previous file (Return code: {}).'.format(str(sp.returncode))) 19 | 20 | input_files = [f for f in glob.glob('./*.csv', recursive=True)] 21 | 22 | print('[*] Creating the output file \'CIC-IDS-2017.csv\'...') 23 | 24 | try: 25 | out_f = open('CIC-IDS-2017.csv', 'w') 26 | except Exception as err: 27 | print('[*] Error! Couldn\'t create the output file.') 28 | print(err) 29 | 30 | for input_file in input_files: 31 | print('[*] Reading \'{}\'...'.format(input_file)) 32 | 33 | try: 34 | in_f = open(input_file, 'r') 35 | 36 | for line in in_f: 37 | split_line = line.strip('\n').split(',') 38 | label = split_line[-1].strip() 39 | 40 | if label == 'Label': continue 41 | if label == 'BENIGN': 42 | split_line[-1] = '0' 43 | else: 44 | split_line[-1] = '1' 45 | 46 | new_line = ','.join(split_line) 47 | 48 | out_f.write(new_line + '\n') 49 | except Exception as err: 50 | print('[*] Error! Something went wrong.') 51 | print(err) 52 | finally: 53 | in_f.close() 54 | print('[*] Done!') 55 | out_f.close() 56 | 57 | -------------------------------------------------------------------------------- /dataset-scripts/dataset-preprocessing.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | import glob 4 | import time 5 | import numpy as np 6 | 7 | from sklearn.preprocessing import MinMaxScaler, StandardScaler 8 | from sklearn.model_selection import train_test_split, GridSearchCV 9 | from sklearn.metrics import precision_recall_fscore_support, confusion_matrix, recall_score, accuracy_score 10 | 11 | from sklearn.svm import LinearSVC 12 | from sklearn.tree import DecisionTreeClassifier 13 | from sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB 14 | from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier 15 | 16 | from joblib import dump, load 17 | from imblearn.over_sampling import SMOTE 18 | 19 | if __name__ == '__main__': 20 | joblibs = [f[2:] for f in glob.glob('./*.joblib')] 21 | 22 | print('[*] Joblibs found:') 23 | print(joblibs) 24 | print() 25 | 26 | print('[*] Reading the contents of \'CIC-IDS-2017.csv\' into a numpy array...') 27 | dataset = np.genfromtxt('CIC-IDS-2017.csv', delimiter=',') 28 | filtered_dataset = dataset[~np.isnan(dataset).any(axis=1)] 29 | filtered_dataset = filtered_dataset[np.isfinite(filtered_dataset).all(axis=1)] 30 | print('\t[*] dataset numpy array shape: {}.'.format(str(dataset.shape))) 31 | print('\t[*] filtered_dataset numpy array shape: {}.'.format(str(filtered_dataset.shape))) 32 | 33 | print('[*] Splitting the dataset into \'features\' and \'labels\'...') 34 | dataset_features = filtered_dataset[:,:78] 35 | dataset_labels = filtered_dataset[:,78] 36 | print('\t[*] dataset_features numpy array shape: {}.'.format(str(dataset_features.shape))) 37 | print('\t[*] dataset_labels numpy array shape: {}.'.format(str(dataset_labels.shape))) 38 | 39 | print('[*] Splitting both \'features\' and \'labels\' into training and test sets...') 40 | training_features, test_features, training_labels, test_labels = train_test_split(dataset_features, dataset_labels, test_size=.33, random_state=12) 41 | print('\t[*] training_features numpy array shape: {}.'.format(str(training_features.shape))) 42 | print('\t[*] training_labels numpy array shape: {}.'.format(str(training_labels.shape))) 43 | print('\t[*] test_features numpy array shape: {}.'.format(str(test_features.shape))) 44 | print('\t[*] test_labels numpy array shape: {}.'.format(str(test_labels.shape))) 45 | 46 | print('[*] Applying the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to the training set...') 47 | sm = SMOTE(random_state=12, ratio=1.0) 48 | train_x, train_y = sm.fit_sample(training_features, training_labels) 49 | print('\t[*] train_x numpy array shape: {}.'.format(str(train_x.shape))) 50 | print('\t[*] train_y numpy array shape: {}.'.format(str(train_y.shape))) 51 | 52 | print('[*] Applying the Min Max Scaler algorithm to the training set...') 53 | # Creates a new scaler to fit the training set and transform both training/test sets. 54 | # Default range is (0,1). 55 | scaler = MinMaxScaler() 56 | scaler.fit(train_x) 57 | train_x_adjusted = scaler.transform(train_x) 58 | test_features_adjusted = scaler.transform(test_features) 59 | 60 | # Saves the scaler for later use in the real-time intrusion detection step. 61 | dump(scaler, 'scaler.joblib') 62 | 63 | # Classifiers' names, joblists and constructors. 64 | clf_names = ['Linear SVC', 'AdaBoost', 'Decision Tree', 'Random Forest', 'Bernoulli NB', 'Gaussian NB'] 65 | clf_joblibs = ['clf_svc.joblib', 'clf_ab.joblib', 'clf_dt.joblib', 'clf_rf.joblib', 'clf_bnb.joblib', 'clf_gnb.joblib'] 66 | clf_constructors = [LinearSVC(dual=False), AdaBoostClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BernoulliNB(), GaussianNB()] 67 | 68 | # GridSearchCV related 69 | clf_best_estimators = [] 70 | clf_parameters = [{'C': [0.1, 1, 10, 100]}, {'n_estimators': [10, 50, 100], 'learning_rate': [0.01, 0.05, 0.1, 1]}, {'max_depth': [None, 3], 'min_samples_split': np.linspace(0.1, 1.0, 10, endpoint=True), 'min_samples_leaf': np.linspace(0.1, 0.5, 5, endpoint=True), 'max_features': [0.1, 0.5, 1.0]}, {'n_estimators': [10, 50, 100], 'max_depth': [None, 3], 'min_samples_split': np.linspace(0.1, 1.0, 10, endpoint=True), 'min_samples_leaf': np.linspace(0.1, 0.5, 5, endpoint=True), 'max_features': [0.1, 0.5, 1.0]}] 71 | 72 | # GridSearchCV Parameters. 73 | # svc_parameters = {'C': [0.1, 1, 10, 100]} 74 | # ab_parameters = {'n_estimators': [10, 50, 100], 'learning_rate': [0.01, 0.05, 0.1, 1]} 75 | # dt_parameters = {'max_depth': [None, 3], 'min_samples_split': np.linspace(0.1, 1.0, 10, endpoint=True), 'min_samples_leaf': np.linspace(0.1, 0.5, 5, endpoint=True), 'max_features': [0.1, 0.5, 1.0]} 76 | # rf_parameters = {'n_estimators': [10, 50, 100], 'max_depth': [None, 3], 'min_samples_split': np.linspace(0.1, 1.0, 10, endpoint=True), 'min_samples_leaf': np.linspace(0.1, 0.5, 5, endpoint=True), 'max_features': [0.1, 0.5, 1.0]} 77 | 78 | results = [] 79 | results_gs = [] 80 | 81 | print('[*] Running GridSearchCV...') 82 | # GridSearchCV for Linear SVC, AdaBoost, DecisionTree, RandomForest. 83 | for i, clf in enumerate(clf_constructors[:-2]): 84 | print('\t[*] {}...'.format(clf_names[i])) 85 | 86 | start_time = time.time() 87 | 88 | grid = GridSearchCV(clf_constructors[i], clf_parameters[i], n_jobs=-1, verbose=3) 89 | grid.fit(train_x_adjusted, train_y) 90 | 91 | # results_gs consists of [index, best score, best parameters, fit time]. 92 | results_gs.append([i, grid.best_score_, grid.best_params_, (time.time() - start_time)]) 93 | 94 | clf_best_estimators.append(grid.best_estimator_) 95 | 96 | print('[*] Running fifteen rounds of training for all classifiers...') 97 | for t in range(15): 98 | print('\t[*] Round {}.'.format(t)) 99 | print('\t[*] Splitting the dataset...') 100 | training_features, test_features, training_labels, test_labels = train_test_split(dataset_features, dataset_labels, test_size=.33) 101 | print('\t[*] Applying the SMOTE algorithm...') 102 | train_x, train_y = sm.fit_sample(training_features, training_labels) 103 | print('\t[*] Scaling the features...') 104 | scaler.fit(train_x) 105 | train_x_adjusted = scaler.transform(train_x) 106 | test_features_adjusted = scaler.transform(test_features) 107 | 108 | for i, clf in enumerate(clf_constructors): 109 | print('\t\t[*] {}...'.format(clf_names[i])) 110 | if i < 4: 111 | # For Linear SVC, AdaBoost, DecisionTree and RandomForest, 112 | # we load the best estimators according to GridSearchCV. 113 | 114 | clf = clf_best_estimators[i] 115 | 116 | start_time = time.time() 117 | clf.fit(train_x_adjusted, train_y) 118 | fit_time = time.time() - start_time 119 | 120 | start_time = time.time() 121 | predictions = clf.predict(test_features_adjusted) 122 | test_time = time.time() - start_time 123 | 124 | accuracy = accuracy_score(test_labels, predictions) 125 | c_matrix = confusion_matrix(test_labels, predictions) 126 | precision, recall, fscore, support = precision_recall_fscore_support(test_labels, predictions) 127 | 128 | results.append([t, i, clf_names[i], accuracy, precision, recall, fscore, support, c_matrix, fit_time, test_time]) 129 | 130 | print('\t\t[*] Accuracy: {}'.format(accuracy)) 131 | print('\t\t[*] Result: {}'.format(results[i])) 132 | else: 133 | start_time = time.time() 134 | clf.fit(train_x_adjusted, train_y) 135 | fit_time = time.time() - start_time 136 | 137 | start_time = time.time() 138 | predictions = clf.predict(test_features_adjusted) 139 | test_time = time.time() - start_time 140 | 141 | accuracy = accuracy_score(test_labels, predictions) 142 | c_matrix = confusion_matrix(test_labels, predictions) 143 | precision, recall, fscore, support = precision_recall_fscore_support(test_labels, predictions) 144 | 145 | results.append([t, i, clf_names[i], accuracy, precision, recall, fscore, support, c_matrix, fit_time, test_time]) 146 | 147 | print('\t\t[*] Accuracy: {}'.format(accuracy)) 148 | print('\t\t[*] Result: {}'.format(results[i])) 149 | 150 | # If it's the last round, we save the classifiers 151 | # for later use in the real-time intrusion detection. 152 | if t == 14: 153 | dump(clf, clf_joblibs[i]) 154 | 155 | print('[*] Dumping results/results_gs to .txt files...') 156 | with open('results.txt', 'w') as f: 157 | for item in results: 158 | f.write('%s\n' % item) 159 | 160 | with open('results_gs.txt', 'w') as f: 161 | for item in results_gs: 162 | f.write('%s\n' % item) 163 | 164 | # Extra 165 | print('[*] Extra informations of the test set:') 166 | test_unique, test_counts = np.unique(test_labels, return_counts=True) 167 | print(dict(zip(test_unique, test_counts))) 168 | 169 | -------------------------------------------------------------------------------- /dataset-scripts/dataset-stats.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | # This script reads the CIC IDS 2017's CSV files 4 | # and counts the appearance of each label in the 5 | # dataset. 6 | 7 | import glob 8 | 9 | # Function that exports the labels' count to a 10 | # text file (dataset-label-count.txt). 11 | def exportDictionary(label_dict): 12 | file = open('dataset-stats.txt', 'w') 13 | 14 | # Sums all the labels' count. 15 | totalLabelCount = 0 16 | totalNonBenignCount = 0 17 | 18 | for key, value in label_dict.items(): 19 | totalLabelCount += value 20 | if key != 'BENIGN': 21 | totalNonBenignCount += value 22 | 23 | # Displays everything in a nice format. 24 | file.write('Detailed count:\n\n') 25 | 26 | for key, value in label_dict.items(): 27 | file.write('{}: {} ({})\n'.format(key, value, str((value / totalLabelCount) * 100))) 28 | 29 | file.write('\nSummary count:\n\n') 30 | file.write('BENIGN: {} ({})\n'.format(label_dict['BENIGN'], str((label_dict['BENIGN'] / totalLabelCount) * 100))) 31 | file.write('NON-BENIGN: {} ({})\n'.format(totalNonBenignCount, str((totalNonBenignCount / totalLabelCount) * 100))) 32 | 33 | file.close() 34 | 35 | if __name__ == '__main__': 36 | # Dictionary used to store and count the appearance 37 | # of each label in the dataset. 38 | label_dict = {} 39 | 40 | # List of .txt/.csv files to read. 41 | # (Ignores the 'CIC-IDS-2017.csv'). 42 | input_files = [f for f in glob.glob('./*.csv', recursive=True) if 'CIC-IDS-2017.csv' not in f] 43 | 44 | if input_files: 45 | for input_file in input_files: 46 | print('[*] Reading \'{}\'...'.format(input_file)) 47 | try: 48 | file = open(input_file) 49 | 50 | for line in file: 51 | parameters_list = line.strip('\n').split(',') 52 | label = parameters_list[-1].strip() 53 | 54 | if label == 'Label': continue 55 | 56 | if label in label_dict: label_dict[label] += 1 57 | else: label_dict[label] = 1 58 | except Exception as err: 59 | print('[*] Error! Something went wrong.') 60 | print(err) 61 | finally: 62 | file.close() 63 | 64 | # Once every file is read, 65 | # we're ready to export the dictionary. 66 | exportDictionary(label_dict) 67 | print('[*] Done.') 68 | else: 69 | print('[*] Sorry, couldn\'t find any file with the {} extension.'.format(extension)) 70 | -------------------------------------------------------------------------------- /joblibs/clf_ab.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lnutimura/ml_classifiers/d873b05952dacc1d8f6100234d5ae4ff3e2660b0/joblibs/clf_ab.joblib -------------------------------------------------------------------------------- /joblibs/clf_bnb.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lnutimura/ml_classifiers/d873b05952dacc1d8f6100234d5ae4ff3e2660b0/joblibs/clf_bnb.joblib 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-------------------------------------------------------------------------------- /jupyter-notebooks/.directory: -------------------------------------------------------------------------------- 1 | [Dolphin] 2 | Timestamp=2019,12,7,18,31,37 3 | Version=4 4 | ViewMode=1 5 | -------------------------------------------------------------------------------- /jupyter-notebooks/.ipynb_checkpoints/formatter-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /jupyter-notebooks/AdaBoost.csv: -------------------------------------------------------------------------------- 1 | 0.9874389198456922, 0.94634743, 0.99228297, 0.96877098, 2170.4828226566315, 8.368554830551147 2 | 0.9875160737248179, 0.94822799, 0.99068203, 0.96899023, 2021.4549748897552, 8.379628419876099 3 | 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0.95450091, 0.99264329, 0.97319852, 1029.670860528946, 8.267982721328735 14 | 0.988421560222889, 0.9533663, 0.98976895, 0.97122664, 1189.5021209716797, 8.284527778625488 15 | 0.9895970852978997, 0.95675324, 0.99198598, 0.97405111, 1037.7235596179962, 8.359991788864136 16 | -------------------------------------------------------------------------------- /jupyter-notebooks/Bernoulli NB.csv: -------------------------------------------------------------------------------- 1 | 0.6659858551221604, 0.34780878, 0.80117447, 0.48504713, 2.096623659133911, 0.5233948230743408 2 | 0.665899057008144, 0.34849001, 0.80153594, 0.48577559, 2.1381096839904785, 0.5739712715148926 3 | 0.6654629232747535, 0.3477979, 0.80082596, 0.48497266, 2.4364984035491943, 0.529186487197876 4 | 0.6661733819117017, 0.34902415, 0.80033139, 0.48607237, 2.2666473388671875, 0.5278220176696777 5 | 0.6662998285469353, 0.348026, 0.80097909, 0.48522248, 2.446922779083252, 0.5214099884033203 6 | 0.6659783540505787, 0.34896893, 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-------------------------------------------------------------------------------- /jupyter-notebooks/Decision Tree.csv: -------------------------------------------------------------------------------- 1 | 0.9261605229318474, 0.73061654, 0.98834258, 0.84015894, 44.56497287750244, 0.12401485443115234 2 | 0.9260490784397771, 0.73078966, 0.98855936, 0.84035173, 38.674792528152466, 0.12355828285217285 3 | 0.9260726532361766, 0.73072678, 0.98832426, 0.8402252, 35.07924032211304, 0.12334680557250977 4 | 0.9260105015002144, 0.73102631, 0.98866766, 0.84054731, 29.95108413696289, 0.12130856513977051 5 | 0.92587441063009, 0.7298222, 0.98836994, 0.83964337, 29.447593688964844, 0.12233114242553711 6 | 0.9261937419631376, 0.73161441, 0.98829583, 0.8408014, 30.155953645706177, 0.12353777885437012 7 | 0.9263984140591514, 0.73212122, 0.98840343, 0.84117496, 29.897309064865112, 0.11911535263061523 8 | 0.9263426918131161, 0.73138061, 0.98890232, 0.84086631, 30.665919065475464, 0.1221456527709961 9 | 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0.9115055722246035, 0.70116845, 0.95728366, 0.80945008, 569.7234733104706, 0.07672691345214844 6 | 0.9116834547792542, 0.70255617, 0.95759005, 0.81048379, 296.09825348854065, 0.07705140113830566 7 | 0.9119824260608659, 0.70311727, 0.95825997, 0.81109711, 357.66516947746277, 0.07636523246765137 8 | 0.9116063009001286, 0.7016824, 0.95818495, 0.81011475, 316.24880743026733, 0.07690143585205078 9 | 0.9116598799828547, 0.70220942, 0.95750585, 0.81022286, 350.92709827423096, 0.07699728012084961 10 | 0.9118313330475782, 0.70260783, 0.95702368, 0.81031522, 515.4708218574524, 0.07662487030029297 11 | 0.9113994856408059, 0.70106234, 0.957294, 0.80938307, 493.23327445983887, 0.08440446853637695 12 | 0.9113962708958423, 0.70093711, 0.95790535, 0.809518, 465.1130328178406, 0.08160400390625 13 | 0.9115623660522932, 0.7015666, 0.95760902, 0.8098317, 444.4584126472473, 0.0774388313293457 14 | 0.911531290184312, 0.70245614, 0.95745836, 0.81037005, 388.62764835357666, 0.0774238109588623 15 | 0.9115720102871839, 0.70182236, 0.9575616, 0.8099851, 345.0412871837616, 0.08224225044250488 16 | -------------------------------------------------------------------------------- /jupyter-notebooks/Random Forest.csv: -------------------------------------------------------------------------------- 1 | 0.9386980282897557, 0.77710737, 0.96440013, 0.86068243, 1458.4946258068085, 4.42433762550354 2 | 0.9289723531933133, 0.74529043, 0.97113171, 0.84335333, 1260.5495026111603, 4.42354154586792 3 | 0.9386680240034291, 0.77742684, 0.96421547, 0.86080475, 861.8964004516602, 4.361448526382446 4 | 0.9386487355336477, 0.77785762, 0.96437865, 0.86113382, 730.3741946220398, 4.355270624160767 5 | 0.938300471495928, 0.77646888, 0.96299227, 0.85973007, 738.3053982257843, 4.3427574634552 6 | 0.926430561508787, 0.74072019, 0.96459408, 0.83796203, 732.3003907203674, 4.344332695007324 7 | 0.9298328332618946, 0.74874306, 0.96950875, 0.84494362, 728.4759392738342, 4.343808174133301 8 | 0.9395992284612087, 0.78031151, 0.96465406, 0.86274558, 754.4640500545502, 4.364483594894409 9 | 0.9391513073296185, 0.77908009, 0.96455193, 0.8619516, 733.7280304431915, 4.340607166290283 10 | 0.9286808829832833, 0.74540653, 0.96829053, 0.84235435, 733.6966254711151, 4.337875604629517 11 | 0.9274603514787827, 0.74266403, 0.96532148, 0.83947965, 905.353193283081, 4.391338348388672 12 | 0.9388201885983712, 0.7777724, 0.96423509, 0.86102436, 897.5508694648743, 4.378940582275391 13 | 0.9250096442348907, 0.73384362, 0.9707094, 0.835819, 728.3350808620453, 4.346201181411743 14 | 0.939103086155165, 0.77940441, 0.96455225, 0.86215018, 731.6788067817688, 4.344741106033325 15 | 0.9388769824260609, 0.77847461, 0.96368645, 0.86123545, 721.5761070251465, 4.362285375595093 16 | -------------------------------------------------------------------------------- /jupyter-notebooks/formatter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import re\n", 10 | "import ast\n", 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 8, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "# This script fixes the formatting of the results.txt file, created after 30+ hours of training.\n", 21 | "# It basically creates .csv files for each machine learning technique with useful informations. \n", 22 | "\n", 23 | "# Dictionary used to store the results of each machine learning technique.\n", 24 | "results_dict = {'Linear SVC': [], 'AdaBoost': [], 'Decision Tree': [], 'Random Forest': [], 'Bernoulli NB': [], 'Gaussian NB': []}\n", 25 | "\n", 26 | "with open('results.txt', 'r') as f:\n", 27 | " for line in f:\n", 28 | " # Removes the newline character.\n", 29 | " line = line.strip('\\n')\n", 30 | " \n", 31 | " # Adjusts the precision, recall and f-score arrays.\n", 32 | " # e.g. array([0.98869048, 0.70039387]), array([0.89989186, 0.95786739]), array([0.94220358, 0.80914204])\n", 33 | " # => 0.98869048, 0.70039387, 0.89989186, 0.95786739, 0.94220358, 0.80914204.\n", 34 | " res_line = re.sub(r\"array\\(\\[(\\d\\.\\d+)\\s*,\\s*(\\d\\.\\d+)\\s*\\]\\)\", \"\\g<1>, \\g<2>\", line)\n", 35 | " \n", 36 | " # Adjusts the support array.\n", 37 | " # e.g. array([749969, 183231]) => 749969, 183231.\n", 38 | " res_line = re.sub(r\"array\\(\\[(\\d+)\\s*,\\s*(\\d+)\\]\\)\", \"\\g<1>, \\g<2>\", res_line)\n", 39 | " \n", 40 | " # Adjusts the confusion matrix array.\n", 41 | " # e.g. array([[674891, 75078], [ 7720, 175511]]) => 674891, 75078, 7720, 175511.\n", 42 | " res_line = re.sub(r\"array\\(\\[\\[(\\d+)\\s*,\\s*(\\d+)\\]\\s*,\\s*\\[\\s+(\\d+),\\s+(\\d+)\\]\\]\\)\", \"\\g<1>, \\g<2>, \\g<3>, \\g<4>\", res_line)\n", 43 | " \n", 44 | " # Replaces the ' by \".\n", 45 | " res_line = res_line.replace('\\'', '\\\"')\n", 46 | " \n", 47 | " # Converts the string representation of an array to a literal array.\n", 48 | " res_array = ast.literal_eval(res_line)\n", 49 | " \n", 50 | " # Accuracy.\n", 51 | " acc = res_array[3]\n", 52 | " \n", 53 | " # Precision.\n", 54 | " pre_normal = res_array[4]\n", 55 | " pre_attack = res_array[5]\n", 56 | " \n", 57 | " # Recall.\n", 58 | " rec_normal = res_array[6]\n", 59 | " rec_attack = res_array[7]\n", 60 | " \n", 61 | " # F-Score.\n", 62 | " fsc_normal = res_array[8]\n", 63 | " fsc_attack = res_array[9]\n", 64 | " \n", 65 | " # Support.\n", 66 | " sup_normal = res_array[10]\n", 67 | " sup_attack = res_array[11]\n", 68 | " \n", 69 | " # Fit and test times.\n", 70 | " fit_time = res_array[16]\n", 71 | " tst_time = res_array[17]\n", 72 | " \n", 73 | " # Weighted precision, recall and f-score.\n", 74 | " # UPDATE: I'm not using the weighted version of these metrics anymore.\n", 75 | " # Instead, I'm analyzing the classifers based on the metrics obtained in the minority class (attacks).\n", 76 | " # pre_weighted = (pre_normal * sup_normal + pre_attack * sup_attack) / (sup_normal + sup_attack)\n", 77 | " # rec_weighted = (rec_normal * sup_normal + rec_attack * sup_attack) / (sup_normal + sup_attack)\n", 78 | " # fsc_weighted = (fsc_normal * sup_normal + fsc_attack * sup_attack) / (sup_normal + sup_attack)\n", 79 | " \n", 80 | " # Dump the results to results_dict.\n", 81 | " results_dict[res_array[2]].append([acc, pre_attack, rec_attack, fsc_attack, fit_time, tst_time])\n", 82 | " \n", 83 | "# Creates the .csv files.\n", 84 | "for key, values in results_dict.items():\n", 85 | " with open(key + '.csv', 'w') as f:\n", 86 | " for value in values:\n", 87 | " value = [str(x) for x in value]\n", 88 | " csv_value = ', '.join(value)\n", 89 | " f.write(csv_value + '\\n')\n", 90 | " " 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": null, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [] 99 | } 100 | ], 101 | "metadata": { 102 | "kernelspec": { 103 | "display_name": "Python 3", 104 | "language": "python", 105 | "name": "python3" 106 | }, 107 | "language_info": { 108 | "codemirror_mode": { 109 | "name": "ipython", 110 | "version": 3 111 | }, 112 | "file_extension": ".py", 113 | "mimetype": "text/x-python", 114 | "name": "python", 115 | "nbconvert_exporter": "python", 116 | "pygments_lexer": "ipython3", 117 | "version": "3.7.4" 118 | } 119 | }, 120 | "nbformat": 4, 121 | "nbformat_minor": 2 122 | } 123 | -------------------------------------------------------------------------------- /jupyter-notebooks/results.txt: -------------------------------------------------------------------------------- 1 | [0, 0, 'Linear SVC', 0.9112751821688813, array([0.98869048, 0.70039387]), array([0.89989186, 0.95786739]), array([0.94220358, 0.80914204]), array([749969, 183231]), array([[674891, 75078], [ 7720, 175511]]), 411.7389039993286, 0.08142900466918945] 2 | [0, 1, 'AdaBoost', 0.9874389198456922, array([0.99809196, 0.94634743]), array([0.98625543, 0.99228297]), array([0.99213839, 0.96877098]), array([749969, 183231]), array([[739661, 10308], [ 1414, 181817]]), 2170.4828226566315, 8.368554830551147] 3 | [0, 2, 'Decision Tree', 0.9261605229318474, array([0.99688327, 0.73061654]), array([0.91096832, 0.98834258]), array([0.95199132, 0.84015894]), array([749969, 183231]), array([[683198, 66771], [ 2136, 181095]]), 44.56497287750244, 0.12401485443115234] 4 | [0, 3, 'Random Forest', 0.9386980282897557, array([0.99075811, 0.77710737]), array([0.93241854, 0.96440013]), array([0.96070346, 0.86068243]), array([749969, 183231]), array([[699285, 50684], [ 6523, 176708]]), 1458.4946258068085, 4.42433762550354] 5 | [0, 4, 'Bernoulli NB', 0.6659858551221604, array([0.92872445, 0.34780878]), array([0.63295683, 0.80117447]), array([0.75283285, 0.48504713]), array([749969, 183231]), array([[474698, 275271], [ 36431, 146800]]), 2.096623659133911, 0.5233948230743408] 6 | [0, 5, 'Gaussian NB', 0.4732972567509644, array([0.98925513, 0.26961592]), array([0.3483984 , 0.98451135]), array([0.51531267, 0.42330621]), array([749969, 183231]), array([[261288, 488681], [ 2838, 180393]]), 2.496795654296875, 0.7585709095001221] 7 | [1, 0, 'Linear SVC', 0.9122813973424775, array([0.98860047, 0.70373259]), array([0.90116869, 0.95761194]), array([0.94286203, 0.81127391]), array([749469, 183731]), array([[675398, 74071], [ 7788, 175943]]), 548.8340351581573, 0.07720232009887695] 8 | [1, 1, 'AdaBoost', 0.9875160737248179, array([0.99769037, 0.94822799]), array([0.98673995, 0.99068203]), array([0.99218494, 0.96899023]), array([749469, 183731]), array([[739531, 9938], [ 1712, 182019]]), 2021.4549748897552, 8.379628419876099] 9 | [1, 2, 'Decision Tree', 0.9260490784397771, array([0.99692987, 0.73078966]), array([0.91072479, 0.98855936]), array([0.95187957, 0.84035173]), array([749469, 183731]), array([[682560, 66909], [ 2102, 181629]]), 38.674792528152466, 0.12355828285217285] 10 | [1, 3, 'Random Forest', 0.9289723531933133, array([0.99235508, 0.74529043]), array([0.91863706, 0.97113171]), array([0.95407421, 0.84335333]), array([749469, 183731]), array([[688490, 60979], [ 5304, 178427]]), 1260.5495026111603, 4.42354154586792] 11 | [1, 4, 'Bernoulli NB', 0.665899057008144, array([0.92858794, 0.34849001]), array([0.63264791, 0.80153594]), array([0.75256947, 0.48577559]), array([749469, 183731]), array([[474150, 275319], [ 36464, 147267]]), 2.1381096839904785, 0.5739712715148926] 12 | [1, 5, 'Gaussian NB', 0.457038148306901, array([0.98632239, 0.26377753]), array([0.32848724, 0.98141849]), array([0.49283837, 0.41579983]), array([749469, 183731]), array([[246191, 503278], [ 3414, 180317]]), 2.585611581802368, 0.7920031547546387] 13 | [2, 0, 'Linear SVC', 0.9120735105015002, array([0.98857891, 0.70298812]), array([0.9009547, 0.9574868]), array([0.94273509, 0.81073413]), array([749657, 183543]), array([[675407, 74250], [ 7803, 175740]]), 699.7636241912842, 0.08191847801208496] 14 | [2, 1, 'AdaBoost', 0.9888491213030433, array([0.99816839, 0.95269072]), array([0.98793181, 0.99259574]), array([0.99302372, 0.97223393]), array([749657, 183543]), array([[740610, 9047], [ 1359, 182184]]), 1183.9737057685852, 8.326764583587646] 15 | [2, 2, 'Decision Tree', 0.9260726532361766, array([0.99687132, 0.73072678]), array([0.91083122, 0.98832426]), array([0.95191101, 0.8402252 ]), array([749657, 183543]), array([[682811, 66846], [ 2143, 181400]]), 35.07924032211304, 0.12334680557250977] 16 | [2, 3, 'Random Forest', 0.9386680240034291, array([0.99069106, 0.77742684]), array([0.93241309, 0.96421547]), array([0.96066904, 0.86080475]), array([749657, 183543]), array([[698990, 50667], [ 6568, 176975]]), 861.8964004516602, 4.361448526382446] 17 | [2, 4, 'Bernoulli NB', 0.6654629232747535, array([0.92840117, 0.3477979 ]), array([0.63232118, 0.80082596]), array([0.75227695, 0.48497266]), array([749657, 183543]), array([[474024, 275633], [ 36557, 146986]]), 2.4364984035491943, 0.529186487197876] 18 | [2, 5, 'Gaussian NB', 0.4306161594513502, array([0.98583713, 0.25455734]), array([0.29545512, 0.98266346]), array([0.45465135, 0.40436468]), array([749657, 183543]), array([[221490, 528167], [ 3182, 180361]]), 3.14081072807312, 0.7507762908935547] 19 | [3, 0, 'Linear SVC', 0.911533433347621, array([0.98836541, 0.7024271 ]), array([0.90039446, 0.95686541]), array([0.94233127, 0.81013828]), array([749125, 184075]), array([[674508, 74617], [ 7940, 176135]]), 369.1318430900574, 0.07701587677001953] 20 | [3, 1, 'AdaBoost', 0.9868259751393056, array([0.99757705, 0.94553405]), array([0.98598365, 0.99025397]), array([0.99174647, 0.96737746]), array([749125, 184075]), array([[738625, 10500], [ 1794, 182281]]), 1351.4775865077972, 8.26123571395874] 21 | [3, 2, 'Decision Tree', 0.9260105015002144, array([0.99695141, 0.73102631]), array([0.91061438, 0.98866766]), array([0.95182907, 0.84054731]), array([749125, 184075]), array([[682164, 66961], [ 2086, 181989]]), 29.95108413696289, 0.12130856513977051] 22 | [3, 3, 'Random Forest', 0.9386487355336477, array([0.99069911, 0.77785762]), array([0.93232638, 0.96437865]), array([0.9606268 , 0.86113382]), array([749125, 184075]), array([[698429, 50696], [ 6557, 177518]]), 730.3741946220398, 4.355270624160767] 23 | [3, 4, 'Bernoulli NB', 0.6661733819117017, array([0.92808928, 0.34902415]), array([0.63320808, 0.80033139]), array([0.75280167, 0.48607237]), array([749125, 184075]), array([[474352, 274773], [ 36754, 147321]]), 2.2666473388671875, 0.5278220176696777] 24 | [3, 5, 'Gaussian NB', 0.4422428204029147, array([0.98525716, 0.25888009]), array([0.3098268 , 0.98113269]), array([0.47141202, 0.40966629]), array([749125, 184075]), array([[232099, 517026], [ 3473, 180602]]), 2.956317186355591, 0.8061532974243164] 25 | [4, 0, 'Linear SVC', 0.9115055722246035, array([0.98854092, 0.70116845]), array([0.90032108, 0.95728366]), array([0.94237083, 0.80945008]), array([749968, 183232]), array([[675212, 74756], [ 7827, 175405]]), 569.7234733104706, 0.07672691345214844] 26 | [4, 1, 'AdaBoost', 0.9861444492070296, array([0.99845264, 0.93920794]), array([0.98428466, 0.99375655]), array([0.99131803, 0.96571256]), array([749968, 183232]), array([[738182, 11786], [ 1144, 182088]]), 1039.438570022583, 8.261409521102905] 27 | [4, 2, 'Decision Tree', 0.92587441063009, array([0.99688931, 0.7298222 ]), array([0.91060552, 0.98836994]), array([0.95179593, 0.83964337]), array([749968, 183232]), array([[682925, 67043], [ 2131, 181101]]), 29.447593688964844, 0.12233114242553711] 28 | [4, 3, 'Random Forest', 0.938300471495928, array([0.99039453, 0.77646888]), array([0.93226778, 0.96299227]), array([0.9604525 , 0.85973007]), array([749968, 183232]), array([[699171, 50797], [ 6781, 176451]]), 738.3053982257843, 4.3427574634552] 29 | [4, 4, 'Bernoulli NB', 0.6662998285469353, array([0.92870479, 0.348026 ]), array([0.63339502, 0.80097909]), array([0.75313624, 0.48522248]), array([749968, 183232]), array([[475026, 274942], [ 36467, 146765]]), 2.446922779083252, 0.5214099884033203] 30 | [4, 5, 'Gaussian NB', 0.44031397342477496, array([0.9880469 , 0.25779372]), array([0.30728911, 0.98478432]), array([0.46878347, 0.40862014]), array([749968, 183232]), array([[230457, 519511], [ 2788, 180444]]), 3.144662857055664, 0.7535772323608398] 31 | [5, 0, 'Linear SVC', 0.9116834547792542, array([0.98856169, 0.70255617]), array([0.90040619, 0.95759005]), array([0.94242689, 0.81048379]), array([749163, 184037]), array([[674551, 74612], [ 7805, 176232]]), 296.09825348854065, 0.07705140113830566] 32 | [5, 1, 'AdaBoost', 0.9878911273039006, array([0.99819321, 0.9482926 ]), array([0.98670249, 0.99272972]), array([0.99241459, 0.9700025 ]), array([749163, 184037]), array([[739201, 9962], [ 1338, 182699]]), 1033.9353158473969, 8.277140855789185] 33 | [5, 2, 'Decision Tree', 0.9261937419631376, array([0.99685361, 0.73161441]), array([0.91093794, 0.98829583]), array([0.95196121, 0.8408014 ]), array([749163, 184037]), array([[682441, 66722], [ 2154, 181883]]), 30.155953645706177, 0.12353777885437012] 34 | [5, 3, 'Random Forest', 0.926430561508787, array([0.99060472, 0.74072019]), array([0.91705543, 0.96459408]), array([0.95241224, 0.83796203]), array([749163, 184037]), array([[687024, 62139], [ 6516, 177521]]), 732.3003907203674, 4.344332695007324] 35 | [5, 4, 'Bernoulli NB', 0.6659783540505787, array([0.92842956, 0.34896893]), array([0.63269676, 0.8014584 ]), array([0.75255201, 0.48622642]), array([749163, 184037]), array([[473993, 275170], [ 36539, 147498]]), 2.2786309719085693, 0.5249361991882324] 36 | [5, 5, 'Gaussian NB', 0.5076371624517788, array([0.98815052, 0.28362489]), array([0.39137811, 0.98089515]), array([0.56068497, 0.44001877]), array([749163, 184037]), array([[293206, 455957], [ 3516, 180521]]), 3.007310152053833, 0.8049867153167725] 37 | [6, 0, 'Linear SVC', 0.9119824260608659, array([0.9887442 , 0.70311727]), array([0.90061534, 0.95825997]), array([0.94262439, 0.81109711]), array([749180, 184020]), array([[674723, 74457], [ 7681, 176339]]), 357.66516947746277, 0.07636523246765137] 38 | [6, 1, 'AdaBoost', 0.9879554222031719, array([0.99820954, 0.94852758]), array([0.98676687, 0.99279426]), array([0.99245522, 0.97015623]), array([749180, 184020]), array([[739266, 9914], [ 1326, 182694]]), 1030.2138278484344, 8.251977443695068] 39 | [6, 2, 'Decision Tree', 0.9263984140591514, array([0.99688359, 0.73212122]), array([0.91116821, 0.98840343]), array([0.95210061, 0.84117496]), array([749180, 184020]), array([[682629, 66551], [ 2134, 181886]]), 29.897309064865112, 0.11911535263061523] 40 | [6, 3, 'Random Forest', 0.9298328332618946, array([0.99192571, 0.74874306]), array([0.9200873 , 0.96950875]), array([0.95465694, 0.84494362]), array([749180, 184020]), array([[689311, 59869], [ 5611, 178409]]), 728.4759392738342, 4.343808174133301] 41 | [6, 4, 'Bernoulli NB', 0.6661208744106301, array([0.92855128, 0.34909544]), array([0.63280253, 0.80176611]), array([0.75266682, 0.48640585]), array([749180, 184020]), array([[474083, 275097], [ 36479, 147541]]), 1.9353320598602295, 0.5233690738677979] 42 | [6, 5, 'Gaussian NB', 0.4357651093013288, array([0.98674246, 0.25690277]), array([0.30122 , 0.98352353]), array([0.46154538, 0.40739207]), array([749180, 184020]), array([[225668, 523512], [ 3032, 180988]]), 2.45505428314209, 0.7455010414123535] 43 | [7, 0, 'Linear SVC', 0.9116063009001286, array([0.98874751, 0.7016824 ]), array([0.90019451, 0.95818495]), array([0.94239535, 0.81011475]), array([749558, 183642]), array([[674748, 74810], [ 7679, 175963]]), 316.24880743026733, 0.07690143585205078] 44 | [7, 1, 'AdaBoost', 0.9871581654522075, array([0.998412 , 0.94407479]), array([0.9855795 , 0.99360168]), array([0.99195425, 0.96820529]), array([749558, 183642]), array([[738749, 10809], [ 1175, 182467]]), 1057.754732131958, 8.27647852897644] 45 | [7, 2, 'Decision Tree', 0.9263426918131161, array([0.99702437, 0.73138061]), array([0.91101556, 0.98890232]), array([0.95208145, 0.84086631]), array([749558, 183642]), array([[682859, 66699], [ 2038, 181604]]), 30.665919065475464, 0.1221456527709961] 46 | [7, 3, 'Random Forest', 0.9395992284612087, array([0.99080821, 0.78031151]), array([0.93346079, 0.96465406]), array([0.96127996, 0.86274558]), array([749558, 183642]), array([[699683, 49875], [ 6491, 177151]]), 754.4640500545502, 4.364483594894409] 47 | [7, 4, 'Bernoulli NB', 0.6660640805829404, array([0.92874779, 0.34853359]), array([0.63279693, 0.80184816]), array([0.7527274 , 0.48587526]), array([749558, 183642]), array([[474318, 275240], [ 36389, 147253]]), 2.298161506652832, 0.5705170631408691] 48 | [7, 5, 'Gaussian NB', 0.44290934419202743, array([0.98816988, 0.25912776]), array([0.31013477, 0.98484551]), array([0.47210158, 0.41029951]), array([749558, 183642]), array([[232464, 517094], [ 2783, 180859]]), 3.0701005458831787, 0.9877808094024658] 49 | [8, 0, 'Linear SVC', 0.9116598799828547, array([0.9885583 , 0.70220942]), array([0.90041633, 0.95750585]), array([0.94243091, 0.81022286]), array([749410, 183790]), array([[674781, 74629], [ 7810, 175980]]), 350.92709827423096, 0.07699728012084961] 50 | [8, 1, 'AdaBoost', 0.988953064723532, array([0.99814653, 0.95331044]), array([0.98807862, 0.99251864]), array([0.99308706, 0.97251952]), array([749410, 183790]), array([[740476, 8934], [ 1375, 182415]]), 1044.905187368393, 8.272042751312256] 51 | [8, 2, 'Decision Tree', 0.9262708958422632, array([0.99691312, 0.73148499]), array([0.91100999, 0.98849774]), array([0.95202769, 0.84078897]), array([749410, 183790]), array([[682720, 66690], [ 2114, 181676]]), 30.510152578353882, 0.12217044830322266] 52 | [8, 3, 'Random Forest', 0.9391513073296185, array([0.99076746, 0.77908009]), array([0.9329219 , 0.96455193]), array([0.96097497, 0.8619516 ]), array([749410, 183790]), array([[699141, 50269], [ 6515, 177275]]), 733.7280304431915, 4.340607166290283] 53 | [8, 4, 'Bernoulli NB', 0.6669599228461208, array([0.9285738 , 0.34933793]), array([0.63405479, 0.80113173]), array([0.75355932, 0.48652426]), array([749410, 183790]), array([[475167, 274243], [ 36550, 147240]]), 2.431907892227173, 0.5227527618408203] 54 | [8, 5, 'Gaussian NB', 0.48655057865409346, array([0.98712343, 0.2748076 ]), array([0.36539544, 0.98056478]), array([0.53336099, 0.42930155]), array([749410, 183790]), array([[273831, 475579], [ 3572, 180218]]), 3.099947690963745, 0.7558448314666748] 55 | [9, 0, 'Linear SVC', 0.9118313330475782, array([0.98844626, 0.70260783]), array([0.90075964, 0.95702368]), array([0.94256798, 0.81031522]), array([749564, 183636]), array([[675177, 74387], [ 7892, 175744]]), 515.4708218574524, 0.07662487030029297] 56 | [9, 1, 'AdaBoost', 0.9882447492498928, array([0.99837786, 0.94917847]), array([0.98696842, 0.99345444]), array([0.99264036, 0.97081189]), array([749564, 183636]), array([[739796, 9768], [ 1202, 182434]]), 1037.5659914016724, 8.253598690032959] 57 | [9, 2, 'Decision Tree', 0.9261648092584654, array([0.9969482 , 0.73098217]), array([0.91086418, 0.98861879]), array([0.95196405, 0.84050047]), array([749564, 183636]), array([[682751, 66813], [ 2090, 181546]]), 30.580723762512207, 0.12112069129943848] 58 | [9, 3, 'Random Forest', 0.9286808829832833, array([0.99161742, 0.74540653]), array([0.9189769 , 0.96829053]), array([0.95391627, 0.84235435]), array([749564, 183636]), array([[688832, 60732], [ 5823, 177813]]), 733.6966254711151, 4.337875604629517] 59 | [9, 4, 'Bernoulli NB', 0.6657522503214744, array([0.9281125 , 0.34802923]), array([0.63288525, 0.79990851]), array([0.75258111, 0.48502899]), array([749564, 183636]), array([[474388, 275176], [ 36744, 146892]]), 2.2881264686584473, 0.5196497440338135] 60 | [9, 5, 'Gaussian NB', 0.444683883411916, array([0.98570655, 0.25930834]), array([0.31317806, 0.98146333]), array([0.47533347, 0.4102312 ]), array([749564, 183636]), array([[234747, 514817], [ 3404, 180232]]), 3.0286731719970703, 0.8023295402526855] 61 | [10, 0, 'Linear SVC', 0.9113994856408059, array([0.98853122, 0.70106234]), array([0.90017604, 0.957294 ]), array([0.94228697, 0.80938307]), array([749830, 183370]), array([[674979, 74851], [ 7831, 175539]]), 493.23327445983887, 0.08440446853637695] 62 | [10, 1, 'AdaBoost', 0.9879232747535363, array([0.99769938, 0.94999111]), array([0.98724644, 0.99069095]), array([0.99244539, 0.96991425]), array([749830, 183370]), array([[740267, 9563], [ 1707, 181663]]), 1372.2912945747375, 8.263375759124756] 63 | [10, 2, 'Decision Tree', 0.9261262323189027, array([0.99693762, 0.73060259]), array([0.91085846, 0.98855865]), array([0.95195611, 0.84022777]), array([749830, 183370]), array([[682989, 66841], [ 2098, 181272]]), 29.631062507629395, 0.1201019287109375] 64 | [10, 3, 'Random Forest', 0.9274603514787827, array([0.99084844, 0.74266403]), array([0.91820146, 0.96532148]), array([0.95314269, 0.83947965]), array([749830, 183370]), array([[688495, 61335], [ 6359, 177011]]), 905.353193283081, 4.391338348388672] 65 | [10, 4, 'Bernoulli NB', 0.6653332618945563, array([0.92843531, 0.34744285]), array([0.6322233 , 0.80072531]), array([0.75221888, 0.48460895]), array([749830, 183370]), array([[474060, 275770], [ 36541, 146829]]), 2.4275100231170654, 0.5212483406066895] 66 | [10, 5, 'Gaussian NB', 0.4247524646378054, array([0.9910825 , 0.25328797]), array([0.28665564, 0.98945302]), array([0.4446911 , 0.40332869]), array([749830, 183370]), array([[214943, 534887], [ 1934, 181436]]), 3.098311185836792, 0.7614588737487793] 67 | [11, 0, 'Linear SVC', 0.9113962708958423, array([0.98868779, 0.70093711]), array([0.90001867, 0.95790535]), array([0.94227186, 0.809518 ]), array([749780, 183420]), array([[674816, 74964], [ 7721, 175699]]), 465.1130328178406, 0.08160400390625] 68 | [11, 1, 'AdaBoost', 0.9875192884697814, array([0.99739488, 0.94919483]), array([0.9870442 , 0.98946135]), array([0.99219254, 0.96890991]), array([749780, 183420]), array([[740066, 9714], [ 1933, 181487]]), 1352.4101054668427, 8.286378622055054] 69 | [11, 2, 'Decision Tree', 0.9264509215602229, array([0.99694165, 0.73154633]), array([0.91125397, 0.98857267]), array([0.9521739, 0.8408566]), array([749780, 183420]), array([[683240, 66540], [ 2096, 181324]]), 29.609118461608887, 0.12054562568664551] 70 | [11, 3, 'Random Forest', 0.9388201885983712, array([0.99070567, 0.7777724 ]), array([0.9326029 , 0.96423509]), array([0.96077665, 0.86102436]), array([749780, 183420]), array([[699247, 50533], [ 6560, 176860]]), 897.5508694648743, 4.378940582275391] 71 | [11, 4, 'Bernoulli NB', 0.6664991427346764, array([0.92872798, 0.34848021]), array([0.6335325, 0.8012594]), array([0.75324105, 0.48571528]), array([749780, 183420]), array([[475010, 274770], [ 36453, 146967]]), 2.2822823524475098, 0.5224814414978027] 72 | [11, 5, 'Gaussian NB', 0.47161273039005575, array([0.98700752, 0.26878594]), array([0.3469191 , 0.98133246]), array([0.5133892 , 0.42198942]), array([749780, 183420]), array([[260113, 489667], [ 3424, 179996]]), 2.9440484046936035, 0.8076584339141846] 73 | [12, 0, 'Linear SVC', 0.9115623660522932, array([0.9886059, 0.7015666]), array([0.90029132, 0.95760902]), array([0.94238406, 0.8098317 ]), array([749694, 183506]), array([[674943, 74751], [ 7779, 175727]]), 444.4584126472473, 0.0774388313293457] 74 | [12, 1, 'AdaBoost', 0.98924882126018, array([0.99818148, 0.95450091]), array([0.98841794, 0.99264329]), array([0.99327572, 0.97319852]), array([749694, 183506]), array([[741011, 8683], [ 1350, 182156]]), 1029.670860528946, 8.267982721328735] 75 | [12, 2, 'Decision Tree', 0.9259676382340334, array([0.99690587, 0.73035373]), array([0.91067289, 0.98845269]), array([0.95184028, 0.84002492]), array([749694, 183506]), array([[682726, 66968], [ 2119, 181387]]), 29.696049213409424, 0.12069129943847656] 76 | [12, 3, 'Random Forest', 0.9250096442348907, array([0.99221537, 0.73384362]), array([0.91382351, 0.9707094 ]), array([0.95140738, 0.835819 ]), array([749694, 183506]), array([[685088, 64606], [ 5375, 178131]]), 728.3350808620453, 4.346201181411743] 77 | [12, 4, 'Bernoulli NB', 0.6661219459922846, array([0.92846567, 0.3482361 ]), array([0.633181 , 0.80069861]), array([0.75290632, 0.48537512]), array([749694, 183506]), array([[474692, 275002], [ 36573, 146933]]), 2.0796308517456055, 0.5262131690979004] 78 | [12, 5, 'Gaussian NB', 0.4436519502786112, array([0.9864558 , 0.25894622]), array([0.31175253, 0.98251283]), array([0.47377618, 0.40986931]), array([749694, 183506]), array([[233719, 515975], [ 3209, 180297]]), 2.395132541656494, 0.7494006156921387] 79 | [13, 0, 'Linear SVC', 0.911531290184312, array([0.98850858, 0.70245614]), array([0.90023326, 0.95745836]), array([0.94230803, 0.81037005]), array([748957, 184243]), array([[674236, 74721], [ 7838, 176405]]), 388.62764835357666, 0.0774238109588623] 80 | [13, 1, 'AdaBoost', 0.988421560222889, array([0.9974593, 0.9533663]), array([0.9880901 , 0.98976895]), array([0.9927526 , 0.97122664]), array([748957, 184243]), array([[740037, 8920], [ 1885, 182358]]), 1189.5021209716797, 8.284527778625488] 81 | [13, 2, 'Decision Tree', 0.9263009001285898, array([0.99693589, 0.73202452]), array([0.91097086, 0.98861829]), array([0.9520167 , 0.84118891]), array([748957, 184243]), array([[682278, 66679], [ 2097, 182146]]), 29.41314196586609, 0.1203773021697998] 82 | [13, 3, 'Random Forest', 0.939103086155165, array([0.99073867, 0.77940441]), array([0.93284261, 0.96455225]), array([0.96091936, 0.86215018]), array([748957, 184243]), array([[698659, 50298], [ 6531, 177712]]), 731.6788067817688, 4.344741106033325] 83 | [13, 4, 'Bernoulli NB', 0.6661326618088298, array([0.92813235, 0.34928241]), array([0.63301765, 0.80074684]), array([0.75268124, 0.48639943]), array([748957, 184243]), array([[474103, 274854], [ 36711, 147532]]), 2.1042163372039795, 0.5246832370758057] 84 | [13, 5, 'Gaussian NB', 0.4571549507072439, array([0.98729362, 0.26454553]), array([0.32783458, 0.98284874]), array([0.49222423, 0.41688221]), array([748957, 184243]), array([[245534, 503423], [ 3160, 181083]]), 2.4253673553466797, 0.7559788227081299] 85 | [14, 0, 'Linear SVC', 0.9115720102871839, array([0.98858028, 0.70182236]), array([0.90030179, 0.9575616 ]), array([0.94237815, 0.8099851 ]), array([749522, 183678]), array([[674796, 74726], [ 7795, 175883]]), 345.0412871837616, 0.08224225044250488] 86 | [14, 1, 'AdaBoost', 0.9895970852978997, array([0.9980182 , 0.95675324]), array([0.98901166, 0.99198598]), array([0.99349452, 0.97405111]), array([749522, 183678]), array([[741286, 8236], [ 1472, 182206]]), 1037.7235596179962, 8.359991788864136] 87 | [14, 2, 'Decision Tree', 0.9263641234462066, array([0.99685599, 0.73169493]), array([0.91119273, 0.98827296]), array([0.95210141, 0.84084629]), array([749522, 183678]), array([[682959, 66563], [ 2154, 181524]]), 29.930074453353882, 0.12349128723144531] 88 | [14, 3, 'Random Forest', 0.9388769824260609, array([0.99055003, 0.77847461]), array([0.93279717, 0.96368645]), array([0.96080652, 0.86123545]), array([749522, 183678]), array([[699152, 50370], [ 6670, 177008]]), 721.5761070251465, 4.362285375595093] 89 | [14, 4, 'Bernoulli NB', 0.6656911701671667, array([0.92844399, 0.34819525]), array([0.63251379, 0.8010758 ]), array([0.7524273, 0.4854047]), array([749522, 183678]), array([[474083, 275439], [ 36538, 147140]]), 2.081385850906372, 0.5245161056518555] 90 | [14, 5, 'Gaussian NB', 0.4547321045863695, array([0.98615533, 0.26288379]), array([0.3256809 , 0.98134235]), array([0.48965252, 0.41468184]), array([749522, 183678]), array([[244105, 505417], [ 3427, 180251]]), 2.4351279735565186, 0.8076331615447998] 91 | -------------------------------------------------------------------------------- /jupyter-notebooks/results_gs.txt: -------------------------------------------------------------------------------- 1 | [0, 0.9294448397500967, {'C': 100}, 1861.9709231853485] 2 | [1, 0.9906322415670135, {'learning_rate': 1, 'n_estimators': 100}, 11682.068260908127] 3 | [2, 0.9497529284921641, {'max_depth': None, 'max_features': 1.0, 'min_samples_leaf': 0.1, 'min_samples_split': 0.1}, 3445.209409236908] 4 | [3, 0.9498137026298448, {'max_depth': None, 'max_features': 0.5, 'min_samples_leaf': 0.1, 'min_samples_split': 0.2, 'n_estimators': 100}, 102302.89634490013] 5 | -------------------------------------------------------------------------------- /ml_classifiers.cc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lnutimura/ml_classifiers/d873b05952dacc1d8f6100234d5ae4ff3e2660b0/ml_classifiers.cc -------------------------------------------------------------------------------- /ml_classifiers.h: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | #include 15 | #include 16 | 17 | #include 18 | #include 19 | #include 20 | #include 21 | #include 22 | #include 23 | #include 24 | 25 | #include "protocols/packet.h" 26 | #include "protocols/icmp4.h" 27 | #include "protocols/icmp6.h" 28 | #include "protocols/tcp.h" 29 | #include "protocols/udp.h" 30 | 31 | /* For convenience. */ 32 | namespace bp = boost::python; 33 | 34 | using namespace snort; 35 | using namespace boost::accumulators; 36 | 37 | typedef accumulator_set< int64_t, features > intAcc; 38 | typedef accumulator_set< double, features > doubleAcc; 39 | 40 | class Connection; 41 | 42 | /* Mutex. */ 43 | std::mutex ml_mutex; 44 | 45 | /* Selected Machine Learning Technique. */ 46 | std::string ml_technique; 47 | 48 | /* Map of current active connections.*/ 49 | std::map connections; 50 | std::map::iterator connections_it; 51 | 52 | /* 53 | Struct of timeouted connections. 54 | Contains their id and features. 55 | */ 56 | 57 | struct TimeoutedConnections { 58 | std::vector id; 59 | std::vector connections; 60 | std::vector> features; 61 | }; 62 | 63 | TimeoutedConnections t_connections; 64 | 65 | /* Auxiliary functions prototypes. */ 66 | int64_t get_time_in_microseconds(); 67 | int64_t get_time_in_microseconds(time_t tvsec, suseconds_t tvusec); 68 | 69 | std::vector get_id_candidates(Packet* p); 70 | 71 | void classify_connections(); 72 | void check_connections(Packet* p); 73 | void verify_timeouts(); 74 | 75 | /* This class' features are based on the CICFlowMeter's features. */ 76 | class Connection { 77 | public: 78 | /* 79 | Basic constructor: 80 | Initializes most of this class' parameters. 81 | */ 82 | Connection (Packet* p, std::string id) { 83 | std::cout << "[+] " << id << std::endl; 84 | 85 | /* Initializes the flags_counter and other parameters. */ 86 | init_flags(); 87 | init_parameters(); 88 | 89 | update_flow_bulk(p); 90 | update_subflows(p); 91 | 92 | /* Updates the flags_counter based on the packet's flags (TCP-only). */ 93 | if (p->is_tcp()) { 94 | update_flags_counter(p); 95 | } 96 | 97 | flow_id = id; 98 | protocol = (uint8_t)p->ip_proto_next; 99 | 100 | /* The packet's timestamp in microseconds. */ 101 | //uint32_t packet_timestamp = p->pkth->ts.tv_usec; 102 | int64_t packet_timestamp = get_time_in_microseconds(p->pkth->ts.tv_sec, p->pkth->ts.tv_usec); 103 | 104 | flow_first_seen = flow_last_seen = 105 | start_active_time = end_active_time = packet_timestamp; 106 | 107 | flow_length((double)p->dsize); 108 | 109 | p->flow->client_ip.ntop(client_ip); 110 | client_port = p->flow->client_port; 111 | 112 | p->flow->server_ip.ntop(server_ip); 113 | server_port = p->flow->server_port; 114 | 115 | /* Instead of comparing client_ip w/ packet_source, 116 | I'll use "p->is_from_client()". 117 | 118 | SfIpString packet_source; 119 | *(p->ptrs.ip_api.get_src())->ntop(packet_source); 120 | */ 121 | 122 | /* Checks whether this packet is coming from the client or the server. */ 123 | if (p->is_from_client()) { 124 | /* Coming from client (forward direction). */ 125 | min_seg_size_forward = p->pkth->pktlen - p->dsize; 126 | 127 | if (p->is_tcp()) { 128 | init_win_bytes_forward = p->ptrs.tcph->win(); 129 | 130 | if (p->ptrs.tcph->are_flags_set(TH_PUSH)) { 131 | forward_PSH += 1; 132 | } 133 | 134 | if (p->ptrs.tcph->are_flags_set(TH_URG)) { 135 | forward_URG += 1; 136 | } 137 | } 138 | 139 | /* 140 | Note: In CICFlowMeter's code, the authors 141 | update the flow_length one more time. 142 | (Does that makes any sense?) 143 | flow_length((double)p->dsize); 144 | */ 145 | forward_pkt((double)p->dsize); 146 | forward_bytes += p->dsize; 147 | forward_hbytes += p->pkth->pktlen - p->dsize; 148 | 149 | forward_last_seen = packet_timestamp; 150 | forward_count += 1; 151 | 152 | } else { 153 | /* Coming from server (backward direction). */ 154 | if (p->is_tcp()) { 155 | init_win_bytes_backward = p->ptrs.tcph->win(); 156 | 157 | if (p->ptrs.tcph->are_flags_set(TH_PUSH)) { 158 | backward_PSH += 1; 159 | } 160 | 161 | if (p->ptrs.tcph->are_flags_set(TH_URG)) { 162 | backward_URG += 1; 163 | } 164 | } 165 | 166 | /* 167 | Note: In CICFlowMeter's code, the authors 168 | update the flow_length one more time. 169 | (Does that makes any sense?) 170 | flow_length((double)p->dsize); 171 | */ 172 | backward_pkt((double)p->dsize); 173 | backward_bytes += p->dsize; 174 | backward_hbytes += p->pkth->pktlen - p->dsize; 175 | 176 | backward_last_seen = packet_timestamp; 177 | backward_count += 1; 178 | } 179 | } 180 | 181 | /* Method used to update a connection based on the packet's information. */ 182 | void add_packet(Packet* p) { 183 | //uint32_t packet_timestamp = p->pkth->ts.tv_usec; 184 | int64_t packet_timestamp = get_time_in_microseconds(p->pkth->ts.tv_sec, p->pkth->ts.tv_usec); 185 | 186 | /* 187 | For some reason, the CICFlowMeter's authors kept these 188 | three lines commented for a long time. 189 | */ 190 | update_flow_bulk(p); 191 | update_subflows(p); 192 | 193 | if (p->is_tcp()) { 194 | update_flags_counter(p); 195 | } 196 | 197 | 198 | flow_length((double)p->dsize); 199 | 200 | /* 201 | SfIpString packet_source; 202 | *(p->ptrs.ip_api.get_src())->ntop(packet_source); 203 | */ 204 | 205 | if (p->is_from_client()) { 206 | if (p->dsize >= 1.0f) { 207 | act_data_pkt_forward += 1; 208 | } 209 | 210 | if (p->is_tcp()) { 211 | if (p->ptrs.tcph->are_flags_set(TH_PUSH)) { 212 | backward_PSH += 1; 213 | } 214 | 215 | if (p->ptrs.tcph->are_flags_set(TH_URG)) { 216 | backward_URG += 1; 217 | } 218 | } 219 | 220 | forward_pkt((double)p->dsize); 221 | forward_bytes += p->dsize; 222 | forward_hbytes += p->pkth->pktlen - p->dsize; 223 | 224 | forward_count += 1; 225 | 226 | if (forward_count > 1) { 227 | forward_iat(packet_timestamp - forward_last_seen); 228 | } 229 | 230 | forward_last_seen = packet_timestamp; 231 | min_seg_size_forward = std::min((p->pkth->pktlen - p->dsize), min_seg_size_forward); 232 | 233 | } else { 234 | if (p->is_tcp()) { 235 | init_win_bytes_backward = p->ptrs.tcph->win(); 236 | 237 | if (p->ptrs.tcph->are_flags_set(TH_PUSH)) { 238 | backward_PSH += 1; 239 | } 240 | 241 | if (p->ptrs.tcph->are_flags_set(TH_URG)) { 242 | backward_URG += 1; 243 | } 244 | } 245 | 246 | backward_pkt((double)p->dsize); 247 | backward_bytes += p->dsize; 248 | backward_hbytes += p->pkth->pktlen - p->dsize; 249 | 250 | backward_count += 1; 251 | 252 | if (backward_count > 1) { 253 | backward_iat(packet_timestamp - backward_last_seen); 254 | } 255 | 256 | backward_last_seen = packet_timestamp; 257 | } 258 | 259 | flow_iat(packet_timestamp - flow_last_seen); 260 | flow_last_seen = packet_timestamp; 261 | } 262 | 263 | /* Method used to initialize the flags counter. */ 264 | void init_flags() { 265 | flags_counter["FIN"] = 0; 266 | flags_counter["SYN"] = 0; 267 | flags_counter["RST"] = 0; 268 | flags_counter["PSH"] = 0; 269 | flags_counter["ACK"] = 0; 270 | flags_counter["URG"] = 0; 271 | flags_counter["CWR"] = 0; 272 | flags_counter["ECE"] = 0; 273 | } 274 | 275 | /* Method used to initialize most of this class' variables/parameters. */ 276 | void init_parameters() { 277 | forward_count = 0; 278 | backward_count = 0; 279 | 280 | flow_first_seen = 0; 281 | flow_last_seen = 0; 282 | 283 | forward_last_seen = 0; 284 | backward_last_seen = 0; 285 | 286 | forward_PSH = 0; 287 | forward_URG = 0; 288 | backward_PSH = 0; 289 | backward_URG = 0; 290 | 291 | forward_bytes = 0; 292 | forward_hbytes = 0; 293 | backward_bytes = 0; 294 | backward_hbytes = 0; 295 | 296 | start_active_time = 0; 297 | end_active_time = 0; 298 | 299 | act_data_pkt_forward = 0; 300 | min_seg_size_forward = 0; 301 | 302 | init_win_bytes_forward = 0; 303 | init_win_bytes_backward = 0; 304 | } 305 | 306 | /* Method used to update the flags_counter (TCP-only). */ 307 | void update_flags_counter(Packet* p) { 308 | const tcp::TCPHdr* tcpHeader = p->ptrs.tcph; 309 | 310 | if (tcpHeader->are_flags_set(TH_FIN)) { 311 | flags_counter["FIN"] += 1; 312 | } 313 | if (tcpHeader->are_flags_set(TH_SYN)) { 314 | flags_counter["SYN"] += 1; 315 | } 316 | if (tcpHeader->are_flags_set(TH_RST)) { 317 | flags_counter["RST"] += 1; 318 | } 319 | if (tcpHeader->are_flags_set(TH_PUSH)) { 320 | flags_counter["PSH"] += 1; 321 | } 322 | if (tcpHeader->are_flags_set(TH_ACK)) { 323 | flags_counter["ACK"] += 1; 324 | } 325 | if (tcpHeader->are_flags_set(TH_URG)) { 326 | flags_counter["URG"] += 1; 327 | } 328 | if (tcpHeader->are_flags_set(TH_CWR)) { 329 | flags_counter["CWR"] += 1; 330 | } 331 | if (tcpHeader->are_flags_set(TH_ECE)) { 332 | flags_counter["ECE"] += 1; 333 | } 334 | } 335 | 336 | /* Method used to update the bulk flow in the forward direction. */ 337 | void update_forward_bulk(Packet* p, int64_t op_bulk_last_timestamp) { 338 | uint32_t size = p->dsize; 339 | //uint32_t packet_timestamp = p->pkth->ts.tv_usec; 340 | int64_t packet_timestamp = get_time_in_microseconds(p->pkth->ts.tv_sec, p->pkth->ts.tv_usec); 341 | 342 | if (op_bulk_last_timestamp > f_bulk_start_helper) f_bulk_start_helper = 0; 343 | if (size <= 0) return; 344 | 345 | if (f_bulk_start_helper == 0) { 346 | f_bulk_size_helper = size; 347 | f_bulk_packet_count_helper = 1; 348 | f_bulk_start_helper = packet_timestamp; 349 | f_bulk_last_timestamp = packet_timestamp; 350 | } else { 351 | if (((packet_timestamp - f_bulk_last_timestamp) / (double)1000000) > 1) { 352 | f_bulk_size_helper = size; 353 | f_bulk_packet_count_helper = 1; 354 | f_bulk_start_helper = packet_timestamp; 355 | f_bulk_last_timestamp = packet_timestamp; 356 | } else { 357 | f_bulk_size_helper += size; 358 | f_bulk_packet_count_helper += 1; 359 | 360 | if (f_bulk_packet_count_helper == 4) { 361 | f_bulk_state_count += 1; 362 | f_bulk_packet_count += f_bulk_packet_count_helper; 363 | f_bulk_total_size += f_bulk_size_helper; 364 | f_bulk_duration += packet_timestamp - f_bulk_start_helper; 365 | } else if (f_bulk_packet_count_helper > 4) { 366 | f_bulk_packet_count += 1; 367 | f_bulk_total_size += size; 368 | f_bulk_duration += packet_timestamp - f_bulk_last_timestamp; 369 | } 370 | 371 | f_bulk_last_timestamp = packet_timestamp; 372 | } 373 | } 374 | } 375 | 376 | /* Method used to update the bulk flow in the backward direction. */ 377 | void update_backward_bulk(Packet* p, uint32_t op_bulk_last_timestamp) { 378 | uint32_t size = p->dsize; 379 | //uint32_t packet_timestamp = p->pkth->ts.tv_usec; 380 | int64_t packet_timestamp = get_time_in_microseconds(p->pkth->ts.tv_sec, p->pkth->ts.tv_usec); 381 | 382 | if (op_bulk_last_timestamp > b_bulk_start_helper) b_bulk_start_helper = 0; 383 | if (size <= 0) return; 384 | 385 | if (b_bulk_start_helper == 0) { 386 | b_bulk_size_helper = size; 387 | b_bulk_packet_count_helper = 1; 388 | b_bulk_start_helper = packet_timestamp; 389 | b_bulk_last_timestamp = packet_timestamp; 390 | } 391 | else { 392 | if (((packet_timestamp - b_bulk_last_timestamp) / (double)1000000) > 1) { 393 | b_bulk_size_helper = size; 394 | b_bulk_packet_count_helper = 1; 395 | b_bulk_start_helper = packet_timestamp; 396 | b_bulk_last_timestamp = packet_timestamp; 397 | } 398 | else { 399 | b_bulk_size_helper += size; 400 | b_bulk_packet_count_helper += 1; 401 | 402 | if (b_bulk_packet_count_helper == 4) { 403 | b_bulk_state_count += 1; 404 | b_bulk_packet_count += b_bulk_packet_count_helper; 405 | b_bulk_total_size += b_bulk_size_helper; 406 | b_bulk_duration += packet_timestamp - b_bulk_start_helper; 407 | } 408 | else if (b_bulk_packet_count_helper > 4) { 409 | b_bulk_packet_count += 1; 410 | b_bulk_total_size += size; 411 | b_bulk_duration += packet_timestamp - b_bulk_last_timestamp; 412 | } 413 | 414 | b_bulk_last_timestamp = packet_timestamp; 415 | } 416 | } 417 | } 418 | 419 | /* Method used to update the bulk flow. */ 420 | void update_flow_bulk(Packet* p) { 421 | /* 422 | SfIpString packet_source; 423 | *(p->ptrs.ip_api.get_src())->ntop(packet_source); 424 | */ 425 | if (p->is_from_client()) { 426 | update_forward_bulk(p, b_bulk_last_timestamp); 427 | } else { 428 | update_backward_bulk(p, f_bulk_last_timestamp); 429 | } 430 | } 431 | 432 | /* Method used to update both active and idle time of the flow. */ 433 | void update_active_idle_time(int64_t current_time, int64_t threshold) { 434 | if ((current_time - end_active_time) > threshold) { 435 | if ((end_active_time - start_active_time) > 0) { 436 | flow_active(end_active_time - start_active_time); 437 | } 438 | 439 | flow_idle(current_time - end_active_time); 440 | start_active_time = current_time; 441 | end_active_time = current_time; 442 | } else { 443 | end_active_time = current_time; 444 | } 445 | } 446 | 447 | /* Method used to update subflows. */ 448 | void update_subflows(Packet* p) { 449 | //uint32_t packet_timestamp = p->pkth->ts.tv_usec; 450 | int64_t packet_timestamp = get_time_in_microseconds(p->pkth->ts.tv_sec, p->pkth->ts.tv_usec); 451 | 452 | if (sf_last_packet_timestamp == -1) { 453 | sf_last_packet_timestamp = packet_timestamp; 454 | sf_ac_helper = packet_timestamp; 455 | } 456 | 457 | if (((packet_timestamp - sf_last_packet_timestamp) / (double)1000000) > 1) { 458 | sf_count += 1; 459 | int64_t last_sf_duration = packet_timestamp - sf_ac_helper; 460 | update_active_idle_time(packet_timestamp - sf_last_packet_timestamp, 5000000); 461 | sf_ac_helper = packet_timestamp; 462 | } 463 | 464 | sf_last_packet_timestamp = packet_timestamp; 465 | } 466 | 467 | /* Features "getters". */ 468 | std::string get_flowid() { 469 | return flow_id; 470 | } 471 | 472 | int64_t get_flowfirstseen() { 473 | return flow_first_seen; 474 | } 475 | 476 | int64_t get_flowlastseen() { 477 | return flow_last_seen; 478 | } 479 | 480 | double get_flowbytespersec() { 481 | int64_t duration = flow_last_seen - flow_first_seen; 482 | 483 | if (duration > 0) { 484 | return ((double)(forward_bytes + backward_bytes)) / ((double)duration/1000000); 485 | } else { 486 | return 0; 487 | } 488 | } 489 | 490 | double get_flowpktspersec() { 491 | int64_t duration = flow_last_seen - flow_first_seen; 492 | uint32_t packet_count = forward_count + backward_count; 493 | 494 | if (duration > 0) { 495 | return ((double)packet_count) / ((double)duration/1000000); 496 | } else { 497 | return 0; 498 | } 499 | } 500 | 501 | double get_fpktspersec() { 502 | int64_t duration = flow_last_seen - flow_first_seen; 503 | 504 | if (duration > 0) { 505 | return ((double)forward_count) / ((double)duration/1000000); 506 | } else { 507 | return 0; 508 | } 509 | } 510 | 511 | double get_bpktspersec() { 512 | int64_t duration = flow_last_seen - flow_first_seen; 513 | 514 | if (duration > 0) { 515 | return ((double)backward_count) / ((double)duration/1000000); 516 | } 517 | else { 518 | return 0; 519 | } 520 | } 521 | 522 | double get_downupratio() { 523 | if (forward_count > 0) { 524 | return ((double)backward_count / (double)forward_count); 525 | } else { 526 | return 0; 527 | } 528 | } 529 | 530 | double get_avgpktsize() { 531 | uint32_t packet_count = forward_count + backward_count; 532 | if (packet_count > 0) { 533 | return (sum(flow_length) / (double)packet_count); 534 | } else { 535 | return 0; 536 | } 537 | } 538 | 539 | double get_favgsegmentsize() { 540 | if (forward_count > 0) { 541 | return (sum(forward_pkt) / (double)forward_count); 542 | } else { 543 | return 0; 544 | } 545 | } 546 | 547 | double get_bavgsegmentsize() { 548 | if (backward_count > 0) { 549 | return (sum(backward_pkt) / (double)backward_count); 550 | } else { 551 | return 0; 552 | } 553 | } 554 | 555 | double get_fsubflowbytes() { 556 | if (sf_count > 0) { 557 | return ((double)forward_bytes / (double)sf_count); 558 | } else { 559 | return 0; 560 | } 561 | } 562 | 563 | double get_fsubflowpkts() { 564 | if (sf_count > 0) { 565 | return ((double)forward_count / (double)sf_count); 566 | } else { 567 | return 0; 568 | } 569 | } 570 | 571 | double get_bsubflowbytes() { 572 | if (sf_count > 0) { 573 | return ((double)backward_bytes / (double)sf_count); 574 | } else { 575 | return 0; 576 | } 577 | } 578 | 579 | double get_bsubflowpkts() { 580 | if (sf_count > 0) { 581 | return ((double)backward_count / (double)sf_count); 582 | } else { 583 | return 0; 584 | } 585 | } 586 | 587 | uint32_t get_fbulkstatecount() { 588 | return f_bulk_state_count; 589 | } 590 | 591 | uint32_t get_fbulktotalsize() { 592 | return f_bulk_total_size; 593 | } 594 | 595 | uint32_t get_fbulkpktcount() { 596 | return f_bulk_packet_count; 597 | } 598 | 599 | int64_t get_fbulkduration() { 600 | return f_bulk_duration; 601 | } 602 | 603 | double get_fbulkduration_seconds() { 604 | return f_bulk_duration / (double)1000000; 605 | } 606 | 607 | uint32_t get_favgbytesperbulk() { 608 | if (get_fbulkstatecount() != 0) { 609 | return (get_fbulktotalsize() / get_fbulkstatecount()); 610 | } else { 611 | return 0; 612 | } 613 | } 614 | 615 | uint32_t get_favgpktsperbulk() { 616 | if (get_fbulkstatecount() != 0) { 617 | return (get_fbulkpktcount() / get_fbulkstatecount()); 618 | } else { 619 | return 0; 620 | } 621 | } 622 | 623 | uint32_t get_favgbulkrate() { 624 | if (get_fbulkduration() != 0) { 625 | return (uint32_t)(get_fbulktotalsize() / get_fbulkduration_seconds()); 626 | } else { 627 | return 0; 628 | } 629 | } 630 | 631 | uint32_t get_bbulkstatecount() { 632 | return b_bulk_state_count; 633 | } 634 | 635 | uint32_t get_bbulktotalsize() { 636 | return b_bulk_total_size; 637 | } 638 | 639 | uint32_t get_bbulkpktcount() { 640 | return b_bulk_packet_count; 641 | } 642 | 643 | int64_t get_bbulkduration() { 644 | return b_bulk_duration; 645 | } 646 | 647 | double get_bbulkduration_seconds() { 648 | return b_bulk_duration / (double)1000000; 649 | } 650 | 651 | uint32_t get_bavgbytesperbulk() { 652 | if (get_bbulkstatecount() != 0) { 653 | return (get_bbulktotalsize() / get_bbulkstatecount()); 654 | } else { 655 | return 0; 656 | } 657 | } 658 | 659 | uint32_t get_bavgpktsperbulk() { 660 | if (get_bbulkstatecount() != 0) { 661 | return (get_bbulkpktcount() / get_bbulkstatecount()); 662 | } else { 663 | return 0; 664 | } 665 | } 666 | 667 | uint32_t get_bavgbulkrate() { 668 | if (get_bbulkduration() != 0) { 669 | return (uint32_t)(get_bbulktotalsize() / get_bbulkduration_seconds()); 670 | } else { 671 | return 0; 672 | } 673 | } 674 | 675 | /* Method used to print the feature vector. */ 676 | void print_feature_vector(std::vector feature_vector) { 677 | std::cout << "["; 678 | for (int i = 0; i < feature_vector.size(); i++) { 679 | std::cout << "(" << (i + 1) << "): " << feature_vector[i]; 680 | 681 | if (i < (feature_vector.size() - 1)) { 682 | std::cout << " "; 683 | } 684 | } 685 | std::cout << "]" << std::endl; 686 | } 687 | 688 | /* Method used to get the feature vector. */ 689 | std::vector get_feature_vector() { 690 | std::vector feature_vector; 691 | 692 | /* 693 | MachineLearningCVE - Features 694 | Destination Port, Flow Duration, Total Fwd Packets, Total Backward Packets,Total Length of Fwd Packets, 695 | Total Length of Bwd Packets, Fwd Packet Length Max, Fwd Packet Length Min, Fwd Packet Length Mean, Fwd Packet Length Std, 696 | Bwd Packet Length Max, Bwd Packet Length Min, Bwd Packet Length Mean, Bwd Packet Length Std,Flow Bytes/s, Flow Packets/s, 697 | Flow IAT Mean, Flow IAT Std, Flow IAT Max, Flow IAT Min,Fwd IAT Total, Fwd IAT Mean, Fwd IAT Std, Fwd IAT Max, Fwd IAT Min, 698 | 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, 699 | Fwd Header Length, Bwd Header Length,Fwd Packets/s, Bwd Packets/s, Min Packet Length, Max Packet Length, Packet Length Mean, 700 | Packet Length Std, Packet Length Variance,FIN Flag Count, SYN Flag Count, RST Flag Count, PSH Flag Count, ACK Flag Count, 701 | URG Flag Count, CWE Flag Count, ECE Flag Count, Down/Up Ratio, Average Packet Size, Avg Fwd Segment Size, Avg Bwd Segment Size, 702 | Fwd Header Length,Fwd Avg Bytes/Bulk, Fwd Avg Packets/Bulk, Fwd Avg Bulk Rate, Bwd Avg Bytes/Bulk, Bwd Avg Packets/Bulk, 703 | Bwd Avg Bulk Rate,Subflow Fwd Packets, Subflow Fwd Bytes, Subflow Bwd Packets, Subflow Bwd Bytes,Init_Win_bytes_forward, 704 | Init_Win_bytes_backward, act_data_pkt_fwd, min_seg_size_forward,Active Mean, Active Std, Active Max, Active Min,Idle Mean, 705 | Idle Std, Idle Max, Idle Min, Label 706 | */ 707 | 708 | int64_t duration = flow_last_seen - flow_first_seen; 709 | 710 | feature_vector.push_back(server_port); /* 1 */ 711 | 712 | feature_vector.push_back(duration); /* 2 */ 713 | 714 | feature_vector.push_back(count(forward_pkt)); /* 3 */ 715 | feature_vector.push_back(count(backward_pkt)); /* 4 */ 716 | feature_vector.push_back(sum(forward_pkt)); /* 5 */ 717 | feature_vector.push_back(sum(backward_pkt)); /* 6 */ 718 | 719 | /* Forward Packet Length. */ 720 | if (count(forward_pkt) > 0) { 721 | feature_vector.push_back((max)(forward_pkt)); /* 7 */ 722 | feature_vector.push_back((min)(forward_pkt)); /* 8 */ 723 | feature_vector.push_back(mean(forward_pkt)); /* 9 */ 724 | feature_vector.push_back(sqrt(variance(forward_pkt))); /* 10 */ 725 | } else { 726 | feature_vector.push_back(0); 727 | feature_vector.push_back(0); 728 | feature_vector.push_back(0); 729 | feature_vector.push_back(0); 730 | } 731 | 732 | /* Backward Packet Length. */ 733 | if (count(backward_pkt) > 0) { 734 | feature_vector.push_back((max)(backward_pkt)); /* 11 */ 735 | feature_vector.push_back((min)(backward_pkt)); /* 12 */ 736 | feature_vector.push_back(mean(backward_pkt)); /* 13 */ 737 | feature_vector.push_back(sqrt(variance(backward_pkt))); /* 14 */ 738 | } else { 739 | feature_vector.push_back(0); 740 | feature_vector.push_back(0); 741 | feature_vector.push_back(0); 742 | feature_vector.push_back(0); 743 | } 744 | 745 | feature_vector.push_back(get_flowbytespersec()); /* 15 */ 746 | feature_vector.push_back(get_flowpktspersec()); /* 16 */ 747 | 748 | /* Flow IAT. */ 749 | if (count(flow_iat) > 0) { 750 | feature_vector.push_back(mean(flow_iat)); /* 17 */ 751 | feature_vector.push_back(sqrt(variance(flow_iat))); /* 18 */ 752 | feature_vector.push_back((max)(flow_iat)); /* 19 */ 753 | feature_vector.push_back((min)(flow_iat)); /* 20 */ 754 | } else { 755 | feature_vector.push_back(0); 756 | feature_vector.push_back(0); 757 | feature_vector.push_back(0); 758 | feature_vector.push_back(0); 759 | } 760 | 761 | /* Forward IAT. */ 762 | if (forward_count > 1) { 763 | feature_vector.push_back(sum(forward_iat)); /* 21 */ 764 | feature_vector.push_back(mean(forward_iat)); /* 22 */ 765 | feature_vector.push_back(sqrt(variance(forward_iat))); /* 23 */ 766 | feature_vector.push_back((max)(forward_iat)); /* 24 */ 767 | feature_vector.push_back((min)(forward_iat)); /* 25 */ 768 | } else { 769 | feature_vector.push_back(0); 770 | feature_vector.push_back(0); 771 | feature_vector.push_back(0); 772 | feature_vector.push_back(0); 773 | feature_vector.push_back(0); 774 | } 775 | 776 | /* Backward IAT. */ 777 | if (backward_count > 1) { 778 | feature_vector.push_back(sum(backward_iat)); /* 26 */ 779 | feature_vector.push_back(mean(backward_iat)); /* 27 */ 780 | feature_vector.push_back(sqrt(variance(backward_iat))); /* 28 */ 781 | feature_vector.push_back((max)(backward_iat)); /* 29 */ 782 | feature_vector.push_back((min)(backward_iat)); /* 30 */ 783 | } else { 784 | feature_vector.push_back(0); 785 | feature_vector.push_back(0); 786 | feature_vector.push_back(0); 787 | feature_vector.push_back(0); 788 | feature_vector.push_back(0); 789 | } 790 | 791 | feature_vector.push_back(forward_PSH); /* 31 */ 792 | feature_vector.push_back(backward_PSH); /* 32 */ 793 | feature_vector.push_back(forward_URG); /* 33 */ 794 | feature_vector.push_back(backward_PSH); /* 34 */ 795 | 796 | feature_vector.push_back(forward_hbytes); /* 35 */ 797 | feature_vector.push_back(backward_hbytes); /* 36 */ 798 | feature_vector.push_back(get_fpktspersec()); /* 37 */ 799 | feature_vector.push_back(get_bpktspersec()); /* 38 */ 800 | 801 | /* Flow Length. */ 802 | if (count(flow_length) > 0) { 803 | feature_vector.push_back((min)(flow_length)); /* 39 */ 804 | feature_vector.push_back((max)(flow_length)); /* 40 */ 805 | feature_vector.push_back(mean(flow_length)); /* 41 */ 806 | feature_vector.push_back(sqrt(variance(flow_length))); /* 42 */ 807 | feature_vector.push_back(variance(flow_length)); /* 43 */ 808 | } else { 809 | feature_vector.push_back(0); 810 | feature_vector.push_back(0); 811 | feature_vector.push_back(0); 812 | feature_vector.push_back(0); 813 | feature_vector.push_back(0); 814 | } 815 | 816 | feature_vector.push_back(flags_counter["FIN"]); /* 44 */ 817 | feature_vector.push_back(flags_counter["SYN"]); /* 45 */ 818 | feature_vector.push_back(flags_counter["RST"]); /* 46 */ 819 | feature_vector.push_back(flags_counter["PSH"]); /* 47 */ 820 | feature_vector.push_back(flags_counter["ACK"]); /* 48 */ 821 | feature_vector.push_back(flags_counter["URG"]); /* 49 */ 822 | feature_vector.push_back(flags_counter["CWR"]); /* 50 */ 823 | feature_vector.push_back(flags_counter["ECE"]); /* 51 */ 824 | 825 | feature_vector.push_back(get_downupratio()); /* 52 */ 826 | feature_vector.push_back(get_avgpktsize()); /* 53 */ 827 | feature_vector.push_back(get_favgsegmentsize()); /* 54 */ 828 | feature_vector.push_back(get_bavgsegmentsize()); /* 55 */ 829 | 830 | feature_vector.push_back(forward_hbytes); /* 56 831 | This feature is duplicated (35). 832 | I'm keeping it because the CICIDS2017's authors kept it in the CSV 833 | files used to train the machine learning techniques. 834 | */ 835 | 836 | feature_vector.push_back(get_favgbytesperbulk()); /* 57 */ 837 | feature_vector.push_back(get_favgpktsperbulk()); /* 58 */ 838 | feature_vector.push_back(get_favgbulkrate()); /* 59 */ 839 | feature_vector.push_back(get_bavgbytesperbulk()); /* 60 */ 840 | feature_vector.push_back(get_bavgpktsperbulk()); /* 61 */ 841 | feature_vector.push_back(get_bavgbulkrate()); /* 62 */ 842 | 843 | feature_vector.push_back(get_fsubflowpkts()); /* 63 */ 844 | feature_vector.push_back(get_fsubflowbytes()); /* 64 */ 845 | feature_vector.push_back(get_bsubflowpkts()); /* 65 */ 846 | feature_vector.push_back(get_bsubflowbytes()); /* 66 */ 847 | 848 | feature_vector.push_back(init_win_bytes_forward); /* 67 */ 849 | feature_vector.push_back(init_win_bytes_backward); /* 68 */ 850 | feature_vector.push_back(act_data_pkt_forward); /* 69 */ 851 | feature_vector.push_back(min_seg_size_forward); /* 70 */ 852 | 853 | /* Flow Active. */ 854 | if (count(flow_active) > 0) { 855 | feature_vector.push_back(mean(flow_active)); /* 71 */ 856 | feature_vector.push_back(sqrt(variance(flow_active))); /* 72 */ 857 | feature_vector.push_back((max)(flow_active)); /* 73 */ 858 | feature_vector.push_back((min)(flow_active)); /* 74 */ 859 | } else { 860 | feature_vector.push_back(0); 861 | feature_vector.push_back(0); 862 | feature_vector.push_back(0); 863 | feature_vector.push_back(0); 864 | } 865 | 866 | /* Flow Idle. */ 867 | if (count(flow_idle) > 0) { 868 | feature_vector.push_back(mean(flow_idle)); /* 75 */ 869 | feature_vector.push_back(sqrt(variance(flow_idle))); /* 76 */ 870 | feature_vector.push_back((max)(flow_idle)); /* 77 */ 871 | feature_vector.push_back((min)(flow_idle)); /* 78 */ 872 | } 873 | else { 874 | feature_vector.push_back(0); 875 | feature_vector.push_back(0); 876 | feature_vector.push_back(0); 877 | feature_vector.push_back(0); 878 | } 879 | 880 | return feature_vector; 881 | } 882 | 883 | /* 884 | These are meant to be private. 885 | They're currently public for debugging purpose. 886 | */ 887 | private: 888 | /* Flow ID*/ 889 | std::string flow_id; 890 | 891 | /* Client/Server IP Addresses */ 892 | SfIpString client_ip; 893 | SfIpString server_ip; 894 | 895 | /* Client/Server Ports */ 896 | uint16_t client_port; 897 | uint16_t server_port; 898 | 899 | /* Connection Protocol */ 900 | uint8_t protocol; 901 | 902 | /* Count of packets sent in the forward/backward direction of the flow */ 903 | uint32_t forward_count; 904 | uint32_t backward_count; 905 | 906 | /* First and last time this flow was seen */ 907 | int64_t flow_first_seen; 908 | int64_t flow_last_seen; 909 | 910 | /* Last time the forward/backward direction of the flow was seen */ 911 | int64_t forward_last_seen; 912 | int64_t backward_last_seen; 913 | 914 | /* Start/end of this flow active time */ 915 | int64_t start_active_time; 916 | int64_t end_active_time; 917 | 918 | /* Flags counter (TCP) */ 919 | std::map flags_counter; 920 | 921 | /* PSH/URG flags counters for the forward/backward direction of the flow */ 922 | uint32_t forward_PSH; 923 | uint32_t forward_URG; 924 | uint32_t backward_PSH; 925 | uint32_t backward_URG; 926 | 927 | /* Bytes and header bytes counters for the forward/backward direction of the flow */ 928 | uint32_t forward_bytes; 929 | uint32_t forward_hbytes; 930 | uint32_t backward_bytes; 931 | uint32_t backward_hbytes; 932 | 933 | /* Count of packets with at least 1 byte of TCP data payload in the forward direction */ 934 | uint32_t act_data_pkt_forward; 935 | 936 | /* Minimum segment size observed in the forward direction */ 937 | uint32_t min_seg_size_forward; 938 | 939 | /* Total number of bytes sent in initial window in the forward direction */ 940 | uint32_t init_win_bytes_forward; 941 | 942 | /* Total number of bytes sent in initial window in the backward direction */ 943 | uint32_t init_win_bytes_backward; 944 | 945 | /* 946 | Accumulator sets for flow's statistics: 947 | - inter-arrival time of packets; 948 | - time a flow was idle before becoming active; 949 | - time a flow was active before becoming idle; 950 | - total bytes of payload (flow and forward/backward direction of the flow). 951 | */ 952 | intAcc flow_iat; 953 | intAcc forward_iat; 954 | intAcc backward_iat; 955 | 956 | intAcc flow_idle; 957 | intAcc flow_active; 958 | 959 | doubleAcc flow_length; 960 | doubleAcc forward_pkt; 961 | doubleAcc backward_pkt; 962 | 963 | /* 964 | Bulk related variables/parameters. 965 | */ 966 | /* Subflows */ 967 | uint32_t sf_count = 0; 968 | int64_t sf_ac_helper = -1; /* This is initialized as -1, so it has to be int32_t. */ 969 | int64_t sf_last_packet_timestamp = -1; /* This is initialized as -1, so it has to be int32_t. */ 970 | 971 | /* Forward bulk flow. */ 972 | int64_t f_bulk_duration = 0; 973 | uint32_t f_bulk_total_size = 0; 974 | 975 | uint32_t f_bulk_state_count = 0; 976 | uint32_t f_bulk_packet_count = 0; 977 | 978 | uint32_t f_bulk_size_helper = 0; 979 | int64_t f_bulk_start_helper = 0; 980 | uint32_t f_bulk_packet_count_helper = 0; 981 | 982 | int64_t f_bulk_last_timestamp = 0; 983 | 984 | /* Backward bulk flow. */ 985 | int64_t b_bulk_duration = 0; 986 | uint32_t b_bulk_total_size = 0; 987 | 988 | uint32_t b_bulk_state_count = 0; 989 | uint32_t b_bulk_packet_count = 0; 990 | 991 | uint32_t b_bulk_size_helper = 0; 992 | int64_t b_bulk_start_helper = 0; 993 | uint32_t b_bulk_packet_count_helper = 0; 994 | 995 | int64_t b_bulk_last_timestamp = 0; 996 | }; 997 | 998 | /* 999 | Auxiliary function used to retrieve the current time in microseconds. 1000 | */ 1001 | int64_t get_time_in_microseconds() { 1002 | struct timeval timestamp; 1003 | gettimeofday(×tamp, NULL); 1004 | return timestamp.tv_sec * (int)1e6 + timestamp.tv_usec; 1005 | } 1006 | 1007 | int64_t get_time_in_microseconds(time_t tvsec, suseconds_t tvusec) { 1008 | return tvsec * (int)1e6 + tvusec; 1009 | } 1010 | 1011 | /* 1012 | Auxiliary function used to retrieve possible strings for the flow id: 1013 | - id_candidates[0]: flow_id; 1014 | - id_candidates[1]: reversed flow_id. 1015 | */ 1016 | std::vector get_id_candidates(Packet* p) { 1017 | std::vector id_candidates; 1018 | 1019 | std::ostringstream iss, reversed_iss; 1020 | 1021 | if (p->is_tcp()) { 1022 | iss << "TCP"; reversed_iss << "TCP"; 1023 | } else if (p->is_udp()) { 1024 | iss << "UDP"; reversed_iss << "UDP"; 1025 | } else if (p->is_icmp()) { 1026 | iss << "ICMP"; reversed_iss << "ICMP"; 1027 | } 1028 | 1029 | SfIpString client_ip, server_ip; 1030 | p->flow->client_ip.ntop(client_ip); 1031 | p->flow->server_ip.ntop(server_ip); 1032 | 1033 | iss << "-" << client_ip << ":" << p->flow->client_port << "-" << server_ip << ":" << p->flow->server_port; 1034 | reversed_iss << "-" << server_ip << ":" << p->flow->server_port << "-" << client_ip << ":" << p->flow->client_port; 1035 | 1036 | if (p->is_icmp()) { 1037 | iss << "-" << p->ptrs.icmph->s_icmp_id; 1038 | reversed_iss << "-" << p->ptrs.icmph->s_icmp_id; 1039 | } 1040 | 1041 | id_candidates.push_back(iss.str()); 1042 | id_candidates.push_back(reversed_iss.str()); 1043 | 1044 | return id_candidates; 1045 | } 1046 | 1047 | /* 1048 | Auxiliary function used to classify the timeouted connections. 1049 | */ 1050 | void classify_connections() { 1051 | /* Creates a file containing the feature vector of each timeouted connection. */ 1052 | std::ofstream outputFile; 1053 | outputFile.open("/home/lnutimura/Desktop/ml_classifiers/tmp/timeouted_connections.txt", std::ios_base::trunc); 1054 | 1055 | for (int i = 0; i < t_connections.id.size(); i++) { 1056 | outputFile << std::fixed << std::setprecision(9); 1057 | 1058 | for (int j = 0; j < 78; j++) { 1059 | outputFile << t_connections.features[i][j]; 1060 | 1061 | if (j == 77) 1062 | outputFile << std::scientific << "\n"; 1063 | else 1064 | outputFile << " "; 1065 | } 1066 | } 1067 | outputFile.close(); 1068 | 1069 | /* Executes the script that classifies every single feature vector in the timeouted_connections.txt file. */ 1070 | std::string py_cmd = "python3 /home/lnutimura/Desktop/ml_classifiers/ml_classifiers.py " + ml_technique; 1071 | system(py_cmd.c_str()); 1072 | 1073 | /* Reads the predictions of every single connection timeouted previously. */ 1074 | std::ifstream inputFile ("/home/lnutimura/Desktop/ml_classifiers/tmp/timeouted_connections_results.txt"); 1075 | 1076 | if (inputFile.is_open()) { 1077 | std::string line; 1078 | uint32_t index = 0; 1079 | 1080 | while (std::getline(inputFile, line)) { 1081 | float predictedValue; 1082 | 1083 | std::cout << "[-] " << t_connections.id[index] << std::endl; 1084 | t_connections.connections[index].print_feature_vector(t_connections.features[index]); 1085 | std::cout << "\tResult: "; 1086 | 1087 | std::istringstream iss (line); 1088 | iss >> predictedValue; 1089 | 1090 | if (predictedValue == 0.0f) { 1091 | std::cout << "Normal (" << predictedValue << ")" << std::endl; 1092 | } else { 1093 | std::cout << "Attack (" << predictedValue << ")" << std::endl; 1094 | } 1095 | 1096 | index++; 1097 | } 1098 | 1099 | inputFile.close(); 1100 | } 1101 | 1102 | t_connections.id.clear(); 1103 | t_connections.connections.clear(); 1104 | t_connections.features.clear(); 1105 | } 1106 | 1107 | /* 1108 | Auxiliary function used to check currently active connections 1109 | and handle timeouted connections. 1110 | */ 1111 | void check_connections(Packet* p) { 1112 | ml_mutex.lock(); 1113 | std::map active_connections = connections; 1114 | ml_mutex.unlock(); 1115 | 1116 | for (auto it = active_connections.begin(); it != active_connections.end(); it++) { 1117 | int64_t time_difference; 1118 | 1119 | /* 1120 | If this function is called with a null argument (p == nullptr), it means 1121 | that the caller is a thread checking for timeouts in the map of currently active connections. 1122 | In this case, we must compare the current time (time(nullptr)) with the last time the target flow was seen. 1123 | In the scenario where we're reading a .pcap, this function will always be called with an argument (the current packet being processed), 1124 | hence we just need to compare the packet's time with the last time the target flow was seen. 1125 | */ 1126 | if (p == nullptr) { 1127 | //time_difference = time(nullptr) - it->second.flow_last_seen; 1128 | //time_difference = time(nullptr) - it->second.get_flowlastseen(); 1129 | time_difference = get_time_in_microseconds() - it->second.get_flowlastseen(); 1130 | } else { 1131 | //time_difference = p->pkth->ts.tv_sec - it->second.flow_last_seen; 1132 | //time_difference = p->pkth->ts.tv_usec - it->second.get_flowlastseen(); 1133 | time_difference = get_time_in_microseconds(p->pkth->ts.tv_sec, p->pkth->ts.tv_usec) - it->second.get_flowlastseen(); 1134 | } 1135 | 1136 | /* Assuming a default timeout value of 120 sec. */ 1137 | if (time_difference > 120000000) { 1138 | ml_mutex.lock(); 1139 | 1140 | /* Iterator pointing to the soon-to-be timeouted connection. */ 1141 | std::map::iterator t_it = connections.find(it->first); 1142 | 1143 | 1144 | if (t_it != connections.end()) { 1145 | /* Retrieves all the flow's information and puts them in a vector. */ 1146 | std::vector feature_vector = t_it->second.get_feature_vector(); 1147 | 1148 | /* 1149 | Transfer the timeouted connection to a struct responsible for 1150 | holding it's informations. 1151 | */ 1152 | t_connections.id.push_back(t_it->second.get_flowid()); 1153 | t_connections.features.push_back(feature_vector); 1154 | t_connections.connections.push_back(t_it->second); 1155 | 1156 | connections.erase(t_it); 1157 | } 1158 | ml_mutex.unlock(); 1159 | } 1160 | } 1161 | 1162 | /* 1163 | If there are timeouted connections inside the t_connections struct, 1164 | we have to classify them. 1165 | */ 1166 | if (t_connections.id.size() > 0) { 1167 | classify_connections(); 1168 | } 1169 | } 1170 | 1171 | /* 1172 | Thread's run function. 1173 | Runs every 20 sec. 1174 | */ 1175 | void verify_timeouts() { 1176 | while (true) { 1177 | std::cout << "[+] verify_timeouts (" << connections.size() << ")" << std::endl; 1178 | 1179 | check_connections(nullptr); 1180 | std::this_thread::sleep_for(std::chrono::milliseconds(20000)); 1181 | } 1182 | } 1183 | -------------------------------------------------------------------------------- /ml_classifiers.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | import sys 4 | import glob 5 | import time 6 | import numpy as np 7 | 8 | from sklearn.preprocessing import MinMaxScaler, StandardScaler 9 | 10 | from sklearn.svm import LinearSVC 11 | from sklearn.tree import DecisionTreeClassifier 12 | from sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB 13 | from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier 14 | 15 | from joblib import dump, load 16 | 17 | if len(sys.argv) != 2: 18 | print('Something went wrong.\nUsage: python3 /path/to/ml_classifiers.py ') 19 | sys.exit(1) 20 | 21 | if __name__ == '__main__': 22 | input_data = [] 23 | input_file = open ('/home/lnutimura/Desktop/ml_classifiers/tmp/timeouted_connections.txt', 'r') 24 | output_file = open ('/home/lnutimura/Desktop/ml_classifiers/tmp/timeouted_connections_results.txt', 'w') 25 | 26 | clf_joblibs = {'svc':'clf_svc.joblib', 'ab':'clf_ab.joblib', 'dt':'clf_dt.joblib', 'rf':'clf_rf.joblib', 'bnb':'clf_bnb.joblib', 'gnb':'clf_gnb.joblib'} 27 | clf = load('/home/lnutimura/Desktop/ml_classifiers/joblibs/' + clf_joblibs[sys.argv[1]]) 28 | scaler = load('/home/lnutimura/Desktop/ml_classifiers/joblibs/scaler.joblib') 29 | 30 | for line in input_file.readlines(): 31 | features = line.strip().split(' ') 32 | feature_vector = [float(x) for x in features] 33 | 34 | input_data.append(feature_vector) 35 | 36 | np_input_data = np.array(input_data) 37 | np_input_data_adjusted = scaler.transform(np_input_data) 38 | 39 | start_time = time.time() 40 | predictions = clf.predict(np_input_data_adjusted) 41 | print('#{}'.format(time.time() - start_time)) 42 | 43 | for prediction in predictions: 44 | output_file.write(str(prediction) + '\n') 45 | 46 | input_file.close() 47 | output_file.close() 48 | -------------------------------------------------------------------------------- /tmp/timeouted_connections.txt: 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