[論文レビュー] A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes

Md Mashrur Arifin, Md Shoaib Ahmed+4 more|arXiv (Cornell University)|Jul 11, 2024

Anomaly Detection Techniques and Applications被引用数 6

ひとこと要約

A comprehensive survey of how GANs are applied to cybersecurity, outlining applications, taxonomy, challenges, and future directions across IDS, malware detection, anomaly detection, and more.

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.

研究の動機と目的

• Survey the current literature on GANs in cybersecurity across multiple domains (malware, IDS, anomaly detection, etc.).
• Propose a taxonomy to organize GAN-based cybersecurity research.
• Highlight key findings, limitations, and future research directions to guide practitioners and researchers.

提案手法

• Systematic review of recent GAN-based cybersecurity research and its domains.
• Development of a new taxonomy to categorize GAN applications in cybersecurity.
• Use of figures and tables to summarize GAN variants, applications, and outcomes.
• Discussion of challenges, limitations, and open research scopes to inspire future work.

実験結果

リサーチクエスチョン

• RQ1 What are the main cybersecurity problems where GANs have been applied (e.g., IDS, malware detection, anomaly detection, DGAs, etc.)?
• RQ2 What GAN variants and training strategies are most commonly used in these cybersecurity applications?
• RQ3 What are the key challenges and limitations hindering GAN-based cybersecurity deployment, and what future directions are suggested?
• RQ4 How does the surveyed literature categorize and relate different GAN approaches within cybersecurity?

主な発見

• GANs are applied to a broad set of cybersecurity tasks including intrusion detection, malware detection, anomaly detection, and malicious domain detection.
• Multiple GAN variants (e.g., vanilla, CGAN, WGAN, WCGAN, MDGAN, Defense-GAN) are used to generate synthetic data, augment training, or craft adversarial examples for defense and evaluation.
• The survey proposes a taxonomy and provides visual summaries (figures and tables) to organize GAN-based cybersecurity research.
• Several challenges are identified: data scarcity, adversarial examples, training instability, mode collapse, and interoperability with existing security frameworks.
• Future research directions emphasize datasets, benchmark creation, robustness against adversaries, and holistic defense strategies using GANs.