A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes

TL;DR

This survey reviews the application of Generative Adversarial Networks (GANs) in cybersecurity, highlighting their potential to improve defense mechanisms like intrusion detection and malware detection, while discussing challenges and future research directions.

Contribution

It provides a comprehensive overview of GANs' roles in cybersecurity, identifying current applications, challenges, and future research scopes in this rapidly evolving field.

Findings

GANs enhance intrusion detection systems and malware detection.

Challenges include data privacy and model robustness.

Future research should focus on addressing GAN limitations in cybersecurity.

Abstract

With the proliferation of Artificial Intelligence, there has been a massive increase in the amount of data required to be accumulated and disseminated digitally. As the data are available online in digital landscapes with complex and sophisticated infrastructures, it is crucial to implement various defense mechanisms based on cybersecurity. Generative Adversarial Networks (GANs), which are deep learning models, have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. Our survey aims to explore the various works completed in GANs, such as Intrusion Detection Systems (IDS), Mobile and Network Trespass, BotNet Detection, and Malware Detection. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains. Further, the paper discusses the challenges and constraints of using GANs in these areas and suggests future research directions. Overall, the paper highlights the potential of GANs in enhancing cybersecurity measures and addresses the need for further exploration in this field.