A survey on generative adversarial networks for malware analysis

Abstract

Generative Adversarial Networks (GANs) have recently become an interesting subject for researchers due to their diverse applications across various fields. Initially focused on imagerelated tasks, they then have been used to generate new synthetic data for applications across many areas of machine learning research. In malware analysis, GANs have rapidly expanded and are used to generate adversarial data for enhancing the effectiveness of malware detection systems. This paper reviews the application of GANs in malware analysis to generate adversarial examples, to modify semantic information within data, to augment datasets for rare classes, and to support representation learning. The paper provides an extensive overview that serves both as a primer for cybersecurity specialists and a resource for machine learning researchers. The paper outlines the fundamentals of GANs, their operational mechanism, the current types of GANs, the challenges faced, and future directions for exploration in malware analysis.