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Paper details
Number 2 - June 2024
Volume 34 - 2024
A recombination generative adversarial network for intrusion detection
Haoqi Luo, Liang Wan
Abstract
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.
Keywords
intrusion detection, generative adversarial network, class imbalance, RGAN