Cyberattacks Detection In IoT Smart City Network Traffic

Author(s: Abhinav dubey Original publication on , the World’s Leading AI and Technology News and Media Company. We invite you to become an AI sponsor if you’re working on an AI product or service. helps technology and AI startups scale. We can help you bring your technology to mass markets. Deep Learning and Machine Learning: This article describes how different deep and machine learning models were used to classify cyberattacks like DoS, Worms and Backdoor. UNSW-NB15 Dataset has been used to train the ML and DL models. My GitHub account contains the code, dataset, and plots. Created using Draw.io. The Internet of Things was created with the intent to expand the capabilities of the Internet beyond smartphones and computers to include electronic devices such as sensors, motors, and other mechanical gadgets. Security vulnerabilities have increased dramatically due to the growing number of IoT device applications. IoT devices can be used for many purposes, including smart homes, fire alarms and healthcare. Imagine what would happen if someone malicious gained access to these devices. Network Intrusion Detection System is (NIDS), which analyses all traffic and detects malicious activity. It also helps organizations monitor their on-premise or cloud infrastructure. Dataset Pcap files, which are raw network packets, were generated at the Cyber Range Lab (ACCS), using IXIA PerfectStorm. The dataset is officially available at the University of New South Wales website https://research.unsw.edu.au/projects/unsw-nb15-dataset UNSW_NB15.csv — Original Dataset UNSW_NB15_features.csv — 49 features with the class label. These features are described in the UNSW-NB15_freatures.csv file. bin_data.csv — Processed CSV Dataset file for Binary Classification multi_data.csv — Processed CSV Dataset file for Multi-class Classification Machine Learning Models used Decision Tree Classifier K-Nearest-Neighbor Classifier Linear Regression Model Linear Support Vector Machine Logistic Regression Model Multi-Layer Perceptron Classifier Random Forest Classifier Data Preprocessing Dataset had 45 attributes and 175341 rows. After dropping null values Dataset had 45 attributes and 81173 rows. The provided data type information in the features dataset is used to convert data types of attributes. One-hot Encoding Categorical Columns ‘proto’, ‘service’, ‘state’ are one-hot-encoded usingpd.get_dummies and these 3 attributes are removed afterward. data_cat Dataframe had 19 attributes after one-hot-encoding. Data_cat can be concatenated to the main data frame. Total attributes of data dataframe — 61 Data Normalization 58 Numeric Columns of DataFrame are scaled using MinMax Scaler in the range 0 to 1. DataFrame preparation for Binary Classification The ‘label attribute’ is divided into two groups: ‘normal’ or ‘abnormal. LabelEncoder encodes ‘label.’ The corresponding encoded label (0,1) is saved to the column ‘label. Binary dataset — 81173 rows, 61 columns Preparing for Multi-class Classification A copy of DataFrame is created for Multi-class Classification. The attack_cat attribute can be divided into nine categories: Analysis,?Backdoor, Columns Preparing for Multi-class Classification A copy of DataFrame is created to prepare for Multi-class Classification. Attack_cat can be encoded with LabelEncoder. The label attribute contains the corresponding encoded labels (0.1,2,3,4,5.6,7.8,8). Attack_cat can be encoded one-hot. Multi-class Dataset — 81173 rows, 69 columns Feature Selection No. of attributes of ‘bin_data’ — 61 No. of attributes of ‘multi_data’ — 69 The Pearson Correlation Coefficient method is used for feature extraction. We selected attributes that had a greater than 0.3% correlation coefficient to the label of the target attribute. No. of attributes of ‘bin_data’ after feature selection — 15 ‘rate’, ‘sttl’, ‘sload’, ‘dload’, ‘ct_srv_src’, ‘ct_state_ttl’, ‘ct_dst_ltm’, ‘ct_src_dport_ltm’, ‘ct_dst_sport_ltm’, ‘ct_dst_src_ltm’, ‘ct_src_ltm’, ‘ct_srv_dst’, ‘state_CON’, ‘state_INT’, ‘label’. No. of attributes of ‘multi_data’ after feature selection — 16 ‘dttl’, ‘swin’, ‘dwin’, ‘tcprtt’, ‘synack’, ‘ackdat’, ‘label’, ‘proto_tcp’, ‘proto_udp’, ‘service_dns’, ‘state_CON’, ‘state_FIN’, ‘attack_cat_Analysis’, ‘attack_cat_DoS’, ‘attack_cat_Exploits’, ‘attack_cat_Normal’. Splitting Dataset into Training and Testing Randomly splitting the bin_data in 80% for training and 20% for testing. Randomly splitting the multi_data in 70% for training and 30% for testing. Target feature — label Decision Tree Classifier Binary Classification Accuracy — 98. 09054511857099 Mean Absolute Error — 0. 019094548814290114 Squared Error — 019094548814290114 Root Squared Error — 0. 13818302650575473 R2 Score — 89. 55757103838098 DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion=’gini’, max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort=’deprecated’, random_state=123, splitter=’best’) Binary Classification with Decision Tree Classifier Multi-class Classification Accuracy — 97. 19940867279895 Mean Absolute Error — 0. 06800262812089355 Mean Squared Error — 0. 20532194480946123 Root Squared Error — 0. 4531246459965086 R2 Score — 86. 17743099336013 DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion=’gini’, max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort=’deprecated’, random_state=123, splitter=’best’) Multi-class Classification with Decision Tree Classifier K-Nearest Neighbor Classifier Binary Classification Accuracy — 98. 3061287342162 Mean Absolute Error — 0. 016938712657838004 Squared Error — 1. 016938712657838004 Root Squared Error — 0. 13014880966738807 R2 Score — 90. 74435871039374 KNeighborsClassifier(algorithm=’auto’, leaf_size=30, metric=’minkowski’, metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights=’uniform’) Binary Classification with KNN Classifier Multi-class Classification Accuracy — 97. 36777266754271 Mean Absolute Error — 0. 06508705650459921 Mean Squared Error — 0. 19411136662286466 Root Squared Error — 0. 44058071521897624 R2 Score — 86. 92848100772136 KNeighborsClassifier(algorithm=’auto’, leaf_size=30, metric=’minkowski’, metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights=’uniform’) Multi-class Classification with KNN Classifier Linear Regression Model Binary Classification Accuracy — 97. 80720665229443 Mean Absolute Error — 0. 021927933477055742 Squared Error — 021927933477055742 Root Squared Error — 0. 1480808342664767 R2 Score — 88. 20923868071647 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) Binary Classification with Linear Regression Model Multi-class Classification Accuracy — 95. 12976346911958 Mean Absolute Error — 0. 06824901445466491 Mean Squared Error — 0. 12146846254927726 Root Mean Squared Error — 0. 3485232596962178 R2 Score — 91. 82055676180129 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) Multi-class Classification with Linear Regression Model Linear Support Vector Machine Binary Classification Accuracy — 97. 85032337542347 Mean Absolute Error — 0. 021496766245765322 Squared Error — 021496766245765322 Root Squared Error — 0. 1466177555610688 R2 Score — 88. 45167193436498 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=’ovr’, degree=3, gamma=’auto’, kernel=’linear’, max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0. 001, verbose=False) Binary Classification with Linear Support Vector Machine Multi-class Classification Accuracy — 97. 59362680683311 Mean Absolute Error — 0. 059912943495400786 Squared Error — 17941031537450722 Root Squared Error — 0. 42356854861345317 R2 Score — 87. 93449282205455 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=’ovr’, degree=3, gamma=’auto’, kernel=’linear’, max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0. 001, verbose=False) Multi-class Classification with Linear Support Vector Machine Logistic Regression Model Binary Classification Accuracy — 97. 80104712041884 Mean Absolute Error — 0. 02198952879581152 Mean Squared Error — 0. 02198952879581152 Root Mean Squared Error — 0. 1482886671186019 R2 Score — 88. 17947258428785 LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=5000, multi_class=’auto’, n_jobs=None, penalty=’l2', random_state=123, solver=’lbfgs’, tol=0. 0001, verbose=0, warm_start=False) Binary Classification with Logistic Regression Model Multi-class Classification Accuracy — 97. 58952036793693 Mean Absolute Error — 0. 175341 Mean Absolute Error — 0. 18056011826544022 Root Squared Error — 0. 42492366169165047 R2 Score — 87. 87674567880146 LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=5000, multi_class=’multinomial’, n_jobs=None, penalty=’l2', random_state=123, solver=’newton-cg’, tol=0. 0001, verbose=0, warm_start=False) Multi-class Classification with Logistic Regression Model Multi-Layer Perceptron Classifier Binary Classification Accuracy — 98. 36772405297197 Mean Absolute Error — 0. 01632275947028026 Mean Squared Error — 0. 01632275947028026 Root Mean Squared Error — 0. 12776055522061674 R2 Score — 91. 10646238100463 MLPClassifier(activation=’relu’, alpha=0. 0001, batch_size=’auto’, beta_1=0.9, beta_2=0. 999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(100,), learning_rate=’constant’, learning_rate_init=0. 001, max_fun=15000, max_iter=8000, momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5, random_state=123, shuffle=True, solver=’adam’, tol=0. 0001, validation_fraction=0.1, verbose=False, warm_start=False) Binary Classification with Multi-Layer Perceptron Classifier Multi-class Classification Accuracy — 97. 54434954007884 Mean Absolute Error — 0. 06065210249671485 Mean Squared Error — 0. 17858902759526937 Root Squared Error — 0. 4225979502970517 R2 Score — 87. 97913543550516 MLPClassifier(activation=’relu’, alpha=0. 0001, batch_size=’auto’, beta_1=0.9, beta_2=0. 999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(100,), learning_rate=’constant’, learning_rate_init=0. 001, max_fun=15000, max_iter=8000, momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5, random_state=123, shuffle=True, solver=’adam’, tol=0. 0001, validation_fraction=0.1, verbose=False, warm_start=False) Multi-class Classification with Multi-Layer Perceptron Classifier Random Forest Classifier Binary Classification Accuracy — 98. 64490298737296 Mean Absolute Error — 0. 013550970126270403 Squared Error — 013550970126270403 Root Squared Error — 0. 1164086342427846 R2 Score — 92. 59509512345335 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion=’gini’, max_depth=None, max_features=’auto’, max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=False, random_state=123, verbose=0, warm_start=False) Binary Classification with Random Forest Classifier Multi-class Classification Accuracy — 97. 31849540078844 Mean Absolute Error — 0. 06611366622864652 Mean Squared Error — 0. 1985052562417871 Root Mean Squared Error — 0. 4455392869790352 R2 Score — 86. 6379909424011 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion=’gini’, max_depth=None, max_features=’auto’, max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=False, random_state=50, verbose=0, warm_start=False) Multi-class Classification with Random Forest Classifier Get the complete code, models, plots on my GitHub account GitHub – abhinav-bhardwaj/IoT-Network-Intrusion-Detection-System-UNSW-NB15: Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset Citations N. Moustafa and J. Slay, “UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” 2015 Military Communications and Information Systems Conference (MilCIS), 2015, pp. 1-6, DOI: 10.1109/MilCIS.2015. 7348942 Nour Moustafa & Jill Slay (2016) The evaluation of Network Anomaly Detection Systems: