Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis

This paper highlights the critical role of Machine Learning (ML) in combating the dynamic nature of cybersecurity threats. Unlike previous studies focusing mainly on static analysis, this work surveys the literature on dynamic analysis-based malware generation and detection. The study addresses the complexities of applying GANs to tabular data with heavy-tailed and multimodal distributions. It also examines the challenges of generating sequential malware behavior data and categorizes GAN-based models and their primary use cases. Furthermore, the paper evaluates adversarial losses and their limitations in generating dynamic malware behavior. Finally, it identifies existing metrics to assess GAN generalization in malware research and suggests future research directions based on identified limitations.