Research on Network Attack Sample Generation and Defence Techniques Based on Generative Adversarial Networks

Generative Adversarial Networks, as a powerful generative model, show great potential in generating adversarial samples and defending against adversarial attacks. In this paper, using Generative Adversarial Networks (GANs) as the basic framework, we design a network attack sample generation method based on Deep Convolutional Generative Adversarial Networks (DCGANs) and an adversarial sample defence method based on multi-scale GANs, and verify the practicality of the two methods through experiments, respectively. Compared with the three adversarial sample generation methods of AE-CDA, AE-DEEP and AE-ATTACK, the DCGAN-based adversarial sample generation method in this paper can interfere with the detection function of the anomaly detection model more effectively, and has better stability and versatility, and can maintain a relatively stable attack effect on a wide range of models and datasets. On the MNIST dataset, the classification accuracy of the adversarial sample defence method proposed in this paper is only slightly lower than that of the APE-GAN defence method on the JSMA adversarial samples, with a maximum classification accuracy of 98.69%. The maximum classification accuracy reaches 98.69%, and the time consumption is 1.5 s, which is only slightly larger than that of the APE-GAN defence method of 1.2 s. Thus, the time consumption of this paper’s multi-scale GAN-based adversarial sample defense method is smaller or equal to that of other comparative defense methods when systematic errors are ignored. The purpose of this paper is to provide a technical reference on how to eliminate adversarial perturbations using generative adversarial networks.