An Intrusion Detection Method Based on WGAN and Deep Learning

Using WGAN and deep learning methods, a multiclass network intrusion detection model is proposed. The model uses the WGAN network to generate fake samples of rare attacks to achieve effective expansion of the original dataset and evaluates the samples through a two-classification method to ensure the effectiveness of the generated data. Through the CNN-LSTM network, the dimensionality reduction data is multiclassified and predicted. The network structure and parameters are effectively designed and trained to realize the identification and classification of network attacks. Experiments have proved that the model has improved the accuracy and recall index of network attack detection and classification compared with traditional methods. The proposed data generation method can improve the overall detection effect of the predictive model on rare attack types, and improve the accuracy rate and reduce errors reports.