AI VS. AI: GENERATIVE ADVERSARIAL NETWORKS (GANS) FOR DYNAMIC HONEYPOT GENERATION IN NEXT-GENERATION CYBER DEFENSE
Department of Computer Science Institute of Information Technology & Management New Delhi, India
DOI: 
SHARE THIS ARTICLE
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Honeypots have long served as a practical tool for cyber deception and threat intelligence collection, but their usefulness is bounded by a handful of stubborn problems. They tend to be static, they require careful manual configuration, and modern attackers
— especially those equipped with AI-driven reconnaissance — can fingerprint them with surprising ease. This paper studies an alternative: using Generative Adversarial Networks (GANs) to automatically produce honeypot configurations that are dynamic, context-aware, and statistically difficult to distinguish from genuine systems. We refer to this approach as HoneyGAN Pots, and we argue it represents a meaningful shift away from reactive, signature-based deception toward a more proactive, generative form of defense.Our discussion is organized around four threads: an architectural framework that frames decoy generation as an adversarial game; a comparison with traditional static approaches; an honest accounting of the implementation challenges, including computational cost and mode collapse; and a roadmap for integrating future work with reinforcement learning and blockchain-based verification. By treating cyber defense as a contest between an attacking AI and a defending AI, we contend that GAN-generated honeypots provide a scalable, adaptive option that fits the threat surface of Industry 5.0, smart cities, and critical-infrastructure environments.
Keywords
Article Information
Article Metrics
HOW TO CITE
Copy
Kumar, R. & Kumar, R. (2026). AI Vs. AI: Generative Adversarial Networks (Gans) for Dynamic Honeypot Generation in Next-Generation Cyber Defense. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.645
Kumar, Rohit, and Rahul Kumar. "AI Vs. AI: Generative Adversarial Networks (Gans) for Dynamic Honeypot Generation in Next-Generation Cyber Defense." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.645.
Kumar, Rohit, and Rahul Kumar. "AI Vs. AI: Generative Adversarial Networks (Gans) for Dynamic Honeypot Generation in Next-Generation Cyber Defense." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.645.
References
2.Spitzner, (2003). Honeypots: Tracking Hackers. Addison-Wesley Professional.
3.Mirza, , & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
4.Arjovsky, , Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. International Conference on Machine Learning, 214–223.
5.Radford, , Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
6.The Honeynet (2004). Know Your Enemy: Learning About Security Threats. Addison-Wesley Professional.
7.Provos, (2004). A virtual honeypot framework. USENIX Security Symposium.
8.Gabrys, , Silva, C., & Bilinski, M. (2024). HoneyGAN Pots: Using generative adversarial networks to generate honeypot configurations. 2nd International Workshop on Adaptive Cyber Defense.
9.Ndayipfukamiye, E., et al. (2025). Generative adversarial networks for adversarial defense in cybersecurity: A systematic review. Journal of Cybersecurity Research.
10.Zhang, L., et al. (2024). MMHP-GAN: Mimicry honeypot feature generation using generative adversarial Chinese Journal of Network and Information Security.
Ethics and Compliance
Indexed In
Similar Articles
Previous Article
