Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation

• URL: http://arxiv.org/abs/2509.20411v2
• Date: Tue, 30 Sep 2025 05:15:07 GMT
• Title: Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation
• Authors: Tharcisse Ndayipfukamiye, Jianguo Ding, Doreen Sebastian Sarwatt, Adamu Gaston Philipo, Huansheng Ning,
• Abstract summary: Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses.<n>This survey systematically reviews GAN-based adversarial defenses in cybersecurity.<n>We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model.
• Score: 8.920024578035386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based adversarial defenses in cybersecurity (2021--August 31, 2025), consolidating recent progress, identifying gaps, and outlining future directions. Using a PRISMA-compliant systematic literature review protocol, we searched five major digital libraries. From 829 initial records, 185 peer-reviewed studies were retained and synthesized through quantitative trend analysis and thematic taxonomy development. We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model. GANs improve detection accuracy, robustness, and data utility across network intrusion detection, malware analysis, and IoT security. Notable advances include WGAN-GP for stable training, CGANs for targeted synthesis, and hybrid GAN models for improved resilience. Yet, persistent challenges remain such as instability in training, lack of standardized benchmarks, high computational cost, and limited explainability. GAN-based defenses demonstrate strong potential but require advances in stable architectures, benchmarking, transparency, and deployment. We propose a roadmap emphasizing hybrid models, unified evaluation, real-world integration, and defenses against emerging threats such as LLM-driven cyberattacks. This survey establishes the foundation for scalable, trustworthy, and adaptive GAN-powered defenses.

Related papers

• Techniques of Modern Attacks [51.56484100374058]
  Advanced Persistent Threats (APTs) represent a complex method of attack aimed at specific targets.<n>I will investigate both the attack life cycle and cutting-edge detection and defense strategies proposed in recent academic research.<n>I aim to highlight the strengths and limitations of each approach and propose more adaptive APT mitigation strategies.
  arXiv  Detail & Related papers  (2026-01-19T22:15:25Z)
• Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities [13.175694396580184]
  Neuro-Symbolic (NeSy) AI has emerged with the potential to revolutionize cybersecurity AI.<n>We systematically characterize this field by analyzing 127 publications spanning 2019-July 2025.<n>We show that causal reasoning integration is the most transformative advancement, enabling proactive defense beyond correlation-based approaches.
  arXiv  Detail & Related papers  (2025-09-08T17:33:59Z)
• Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systems [5.787505062263962]
  Adversarial attacks against computer vision systems have emerged as a critical research area that challenges the fundamental assumptions about neural network robustness and security.<n>This comprehensive survey examines the evolving landscape of adversarial techniques, revealing their dual nature as both sophisticated security threats and valuable defensive tools.
  arXiv  Detail & Related papers  (2025-08-03T17:02:05Z)
• Co-evolutionary Dynamics of Attack and Defence in Cybersecurity [7.949437760740007]
  This study uses an Evolutionary Game Theory framework to investigate the evolutionary dynamics of attacks and defences in cyberspace.<n>We find that systems with high defence intensities show stability with minimal attack frequencies, whereas low-defence environments show instability, and are vulnerable to attacks.<n>Our analysis suggests that adaptive cybersecurity strategies based on EGT can improve resource allocation, enhance system resilience, and reduce the overall risk of cyberattacks.
  arXiv  Detail & Related papers  (2025-05-25T22:11:24Z)
• Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends [20.469263896950437]
  We investigate the most recent techniques of resilient federated learning (ResFL) in CyberEdge networks.<n>We focus on joint training with agglomerative deduction and feature-oriented security mechanisms.<n>These advancements offer ultra-low latency, artificial intelligence (AI)-driven network management, and improved resilience against adversarial attacks.
  arXiv  Detail & Related papers  (2025-04-01T23:06:45Z)
• Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety [296.5392512998251]
  We present a comprehensive taxonomy of safety threats to large models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats.<n>We identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices.
  arXiv  Detail & Related papers  (2025-02-02T05:14:22Z)
• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes [1.3631461603291568]
  Generative Adversarial Networks (GANs) 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. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains.
  arXiv  Detail & Related papers  (2024-07-11T19:51:48Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid [62.91192307098067]
  This paper proposes a novel zero trust framework for a power grid supply chain (PGSC) It facilitates early detection of potential GenAI-driven attack vectors, assessment of tail risk-based stability measures, and mitigation of such threats. Experimental results show that the proposed zero trust framework achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.
  arXiv  Detail & Related papers  (2024-03-11T02:47:21Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.