Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Abstract

Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior and leading to high false-positive and false-negative rates. To alleviate the impact of class imbalance on the detection accuracy of network intrusion detection models and improve their effectiveness, this paper proposes a method based on a feature selection-conditional Wasserstein generative adversarial network (FCWGAN) and bidirectional long short-term memory network (BiLSTM). The method uses the XGBoost algorithm with Spearman's correlation coefficient to select the data features, filters out useless and redundant features, and simplifies the data structure. A conditional WGAN (CWGAN) is used to generate a small number of samples in the dataset, add them to the original training set to supplement the dataset samples, and apply BiLSTM to complete the training of the model and realize the classification. In comparative tests based on the NSL-KDD and UNSW-NB15 datasets, the accuracy of the proposed model reached 99.57% and 85.59%, respectively, which is 1.44% and 2.98% higher than that of the same type of CWGAN and deep neural network (CWGAN-DNN) model, respectively.