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

IoT-Network-Intrusion-Detection-System-UNSW-NB15

Network Intrusion Detection based on various Machine learning and Deep learning algorithms using UNSW-NB15 Dataset

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Cyberattacks Detection in IoT-based Smart City Network Traffic

Prerequisites

• Sklearn
• Pandas
• Numpy
• Matplotlib
• Pickle

Running the Notebook

The notebook can be run on

• Google Colaboratory
• Jupyter Notebook

Instructions

• To run the code, user must have the required Dataset on their system or programming environment.
• Upload the notebook and dataset on Jupyter Notebook or Google Colaboratory.
• Click on the file with .ipynb extension to open the notebook. To run complete code at once press Ctrl + F9
• To run any specific segment of code, select that code cell and press Shift+Enter or Ctrl+Shift+Enter

Caution - The code should be executed in the given order for best results without encountering any errors.

Datasets

• UNSW_NB15.csv - Original Dataset
• UNSW_NB15_features.csv - 49 features with the class label. These features are described in UNSW-NB15_freatures.csv file.
• bin_data.csv - CSV Dataset file for Binary Classification
• multi_data.csv - CSV Dataset file for Multi-class Classification

Machine Learning Models

• 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.
• Data type of attributes is converted using provided datatype information from features dataset.

• One-hot-encoding

  • Categorical Columns 'proto', 'service', 'state' are one-hot-encoded using pd.get_dummies() and these 3 attributes are removed afterwards.
  • data_cat Dataframe had 19 attributes after one-hot-encoding.
  • data_cat is concatenated with the main data dataframe.
  • Total attributes of data dataframe - 61

• Data Normalization

  • 58 Numeric Columns of DataFrame is scaled using MinMax Scaler.

• Binary Classification

  • A copy of DataFrame is created for Binary Classification.
  • 'label' attribute is classified into two categories 'normal' and 'abnormal'.
  • 'label' is encoded using LabelEncoder(), encoded labels are saved in 'label'.
  • Binary dataset - 81173 rows, 61 columns

• Multi-class Classification

  • A copy of DataFrame is created for Multi-class Classification.
  • 'attack_cat' attribute is classified into 9 categories 'Analysis', 'Backdoor', 'DoS', 'Exploits', 'Fuzzers', 'Generic', 'Normal', 'Reconnaissance', 'Worms'
  • attack_cat is encoded using LabelEncoder(), encoded labels are saved in label.
  • attack_cat is one-hot-encoded'.
  • Multi-class Dataset - 81173 rows, 69 columns

• Feature Extraction

  • No. of attributes of 'bin_data' - 61
  • No. of attributes of 'multi_data' - 69
  • Pearson Correlation Coefficient method is used for feature extraction.
  • The attributes with more than 0.3 correlation coefficient with the target attribute label were selected.
  • 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

• 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

Decision Tree Classifier

• Binary Classification

  • Accuracy - 98.09054511857099
  • Mean Absolute Error - 0.019094548814290114
  • Mean Squared Error - 0.019094548814290114
  • Root Mean 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')

• Multi-class Classification

  • Accuracy - 97.19940867279895
  • Mean Absolute Error - 0.06800262812089355
  • Mean Squared Error - 0.20532194480946123
  • Root Mean 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')

K-Nearest-Neighbor

• Binary Classification

  • Accuracy - 98.3061287342162
  • Mean Absolute Error - 0.016938712657838004
  • Mean Squared Error - 0.016938712657838004
  • Root Mean 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')

• Multi-class Classification

  • Accuracy - 97.36777266754271
  • Mean Absolute Error - 0.06508705650459921
  • Mean Squared Error - 0.19411136662286466
  • Root Mean 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')

Linear Regression

• Binary Classification

  • Accuracy - 97.80720665229443
  • Mean Absolute Error - 0.021927933477055742
  • Mean Squared Error - 0.021927933477055742
  • Root Mean Squared Error - 0.1480808342664767
  • R2 Score - 88.20923868071647
  • LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

• 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)

Linear Support Vector Machine

• Binary Classification

  • Accuracy - 97.85032337542347
  • Mean Absolute Error - 0.021496766245765322
  • Mean Squared Error - 0.021496766245765322
  • Root Mean 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)

• Multi-class Classification

  • Accuracy - 97.59362680683311
  • Mean Absolute Error - 0.059912943495400786
  • Mean Squared Error - 0.17941031537450722
  • Root Mean 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)

Logistic Regression

• 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)

• Multi-class Classification

  • Accuracy - 97.58952036793693
  • Mean Absolute Error - 0.060077201051248356
  • Mean Squared Error - 0.18056011826544022
  • Root Mean 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 Layer Perceptron

• 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)

• Multi-class Classification

  • Accuracy - 97.54434954007884
  • Mean Absolute Error - 0.06065210249671485
  • Mean Squared Error - 0.17858902759526937
  • Root Mean 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)

Random Forest Classifier

• Binary Classification

  • Accuracy - 98.64490298737296
  • Mean Absolute Error - 0.013550970126270403
  • Mean Squared Error - 0.013550970126270403
  • Root Mean 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)

• 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)

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: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set, Information Security Journal: A Global Perspective, 25:1–3, 18–31, DOI: 10.1080/19393555.2015.1125974

iot-network-intrusion-detection-system-unsw-nb15's Issues

multi_data.csv中，为什么攻击类型只分了四种？

多分类的multi_data.csv中，最后四列只表示了四种攻击类别？不是一共有九种么？