A Study on Generative Adversarial Networks (GANs) for Cybersecurity

Based on machine learning (ML), a framework known as a Generative Adversarial Networks (GANs) pits two neural networks against one another using deep learning techniques. To handle photos, audio, and video efficiently, GANs are used to learn distributions on multiple high-dimensional data. In terms of security and privacy, GANs examine novel flaws and difficulties in ML algorithms. Mode collapse, non-convergence, and instability were among several issues the current model encountered as a result of the inappropriate network architecture design and optimization method selection. To overcome these types of challenges, GANs have been introduced with the principle of re-engineering network architecture and a modified optimization algorithm. The focus is on novel difficulties related to the detection of adversarial, phishing, insider, and Distributed Denial of Service (DDoS) attacks, among others. This chapter provides the introduction to GANs, variants, and its challenges.