A Recombination Generative Adversarial Network for Intrusion Detection

The imbalance and complexity of network traffic data are hot issues in the field of intrusion detection. To improve the detection rate of minority class attacks in network traffic, this paper presents a method for intrusion detection based on the recombination generative adversarial network (RGAN). In this study, dual-stage game learning is used to optimize the discriminator for efficient identification of attack samples. In the first stage, the proposed model trains a deep convolutional generative adversarial network (DCGAN) integrated with the self-attention (SA) mechanism, and simultaneously trains an independent convolutional neural network (CNN) classifier integrated with the gated recurrent unit (GRU). This stage allows the generator to generate minority class attack samples that closely resemble real samples, while the independent classifier possesses the basic classification ability. In the second stage, the generator and the independent classifier of the DCGAN together constitute the second layer of the model-the generative adversarial network. Through dual-stage game learning, the classifier's discrimination ability for the minority samples is optimized, and it serves as the final output of the discriminator. In addition, the introduction of reconstruction loss helps prevent the detection rate of false positive samples. Experimental results on the CSE-IDS-2018 dataset demonstrate that our model performs well compared with various other intrusion detection techniques in terms of detection accuracy, recall, and F1-score for minority class attacks.