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Documentation

Overview

Loosely based on research paper A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach https://doi.org/10.1016/j.neucom.2019.11.016

Datasets

  • bin_data.csv - CSV Dataset file for Binary Classification
  • multi_data.csv - CSV Dataset file for Multi-class Classification
  • KDDTrain+.txt - Original Dataset downloaded

The NSL-KDD dataset from the Canadian Institute for Cybersecurity (updated version of the original KDD Cup 1999 Data (KDD99) https://www.unb.ca/cic/datasets/nsl.html

Machine Learning Models

  • Linear Support Vector Machine
  • Quadratic Support Vector Machine
  • K-Nearest-Neighbor
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Multi Layer Perceptron
  • Long Short-Term Memory
  • Auto Encoder

Data Preprocessing

  • Dataset had 43 attributes, attribute 'difficulty_level' was dropped.

  • Data Normalization

    • 38 Numeric Columns of DataFrame is scaled using Standard Scaler.

  • One-hot-encoding

    • Categorical Columns 'protocol_type', 'service', 'flag' are one-hot-encoded using pd.get_dummies().
    • 'categorical' Dataframe had 84 attributes after one-hot-encoding.

  • Binary Classification

    • A copy of DataFrame is created for Binary Classification.
    • Attack label ('label' attribute) is classified into two categories 'normal' and 'abnormal'.
    • 'label' is encoded using LabelEncoder(), encoded labels are saved in 'intrusion'.
    • 'label' is one-hot-encoded.

  • Multi-class Classification

    • A copy of DataFrame is created for Multi-class Classification.
    • Attack label ('label' attribute) is classified into five categories 'normal', 'U2R', 'R2L', 'Probe', 'Dos'.
    • 'label' is encoded using LabelEncoder(), encoded labels are saved in 'intrusion'.
    • 'label is one-hot-encoded'.

  • Feature Extraction

    • No. of attributes of 'bin_data' - 45
    • No. of attributes of 'multi_data' - 48
    • The attributes of 'bin_data' and 'multi_data' are selected using 'Pearson Correlation Coefficient'.
    • The attributes with more than 0.5 correlation coefficient with the target attribute 'intrusion' were selected.
    • 9 attributes 'count', 'srv_serror_rate', 'serror_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'logged_in', 'dst_host_same_srv_rate', 'dst_host_srv_count', 'same_srv_rate'.
    • No. of attributes of 'bin_data' after feature selection and joining 'categorical' DataFrame - 97
    • No. of attributes of 'multi_data' after feature selection and joining 'categorical' DataFrame - 100

Splitting the dataset

  • Splitting the dataset into 1:4 Ratio for Testing and Training.
  • 93 attributes were selected out of 97 attributes, to exclude the target attribute (encoded, one-hot-encoded, original) for Binary Classification
  • 'intrusion' attribute was selected as the target attribute.
  • 93 attributes were selected out of 100 attributes, to exclude the target attribute (encoded, one-hot-encoded, original) for Multi-class Classification.

Linear Support Vector Machine

  • Binary Classification Accuracy - 96.69 %
  • Multi-class Classification Accuracy - 95.24 %
  • Kernel Type used - Linear
  • 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)

Quadratic Support Vector Machine

  • Binary Classification Accuracy - 95.71 %
  • Multi-class Classification Accuracy - 92.86 %
  • Kernel Type used - Poly
  • 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='poly', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)

K-Nearest-Neighbor

  • Binary Classification Accuracy - 98.55 %
  • Multi-class Classification Accuracy - 98.29 %
  • No. of neighbors - 5
  • Weights - Uniform
  • KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2,weights='uniform')

Linear Discriminant Analysis

  • Binary Classification Accuracy - 96.70 %
  • Multi-class Classification Accuracy - 93.19 %
  • Solver used - svd (singular value decomposition)
  • LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,solver='svd', store_covariance=False, tol=0.0001)

Quadratic Discriminant Analysis

  • Binary Classification Accuracy - 68.79 %
  • Multi-class Classification Accuracy - 44.96 %
  • QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001)

Multi Layer Perceptron

  • Binary Classification Accuracy - 97.79 %
    • Input layer with 93 input dimensions
    • 1 Hidden layer with 50 Neurons and relu activation function
    • Output layer with 1 neuron and sigmoid activation function
    • Loss - binary_crossentropy
    • Optimizer - adam
    • Batch size - 5000
    • Epochs - 100
  • Multi-class Classification Accuracy - 96.92 %
    • Input layer with 93 input dimensions
    • 1 Hidden layer with 50 Neurons and relu activation function
    • Output layer with 5 neurons and softmax activation function
    • Loss - categorical_crossentropy
    • Optimizer - adam
    • Batch size - 5000
    • Epochs - 100

Long Short-Term Memory

  • Binary Classification Accuracy - 83.05 %
  • Input layer with 93 input dimensions
  • LSTM layer with 50 encoding cells
  • Output layer with 1 neuron and sigmoid activation function
  • Loss - binary_crossentropy
  • Optimizer - adam
  • Batch Size - 5000
  • Epochs - 100

Autoencoder

  • Binary Classification Accuracy - 92.26 %
  • Multi-class Classification Accuracy - 91.22 %
  • Input layer
  • Encoding layer with 50 encoding cells
  • Output layer and Decoding Layer with softmax activation function
  • Loss - mean_squared_error
  • Optimizer - adam
  • Batch Size - 500
  • Epochs - 100

Citations

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