A review of generative adversarial networks and its application in cybersecurity

Abstract

This paper reviews Generative Adversarial Networks (GANs) in detail by discussing the strength of the GAN when compared to other generative models, how GANs works and some of the notable problems with training, tuning and evaluating GANs. The paper also briefly reviews notable GAN architectures like the Deep Convolutional Generative Adversarial Network (DCGAN), and Wasserstein GAN, with the aim of showing how design specifications in these architectures help solve some of the problems with the basic GAN model. All this is done with a view of discussing the application of GANs in cybersecurity studies. Here, the paper reviews notable cybersecurity studies where the GAN plays a key role in the design of a security system or adversarial system. In general, from the review, one can observe two major approaches these cybersecurity studies follow. In the first approach, the GAN is used to improve generalization to unforeseen adversarial attacks, by generating novel samples that resembles adversarial data which can then serve as training data for other machine learning models. In the second approach, the GAN is trained on data that contains authorized features with the goal of generating realistic adversarial data that can thus fool a security system. These two approaches currently guide the scope of modern cybersecurity studies with generative adversarial networks.