Cyber Deception Strategies Using AI-Powered Honeypots and Generative Models for Attacker Behavior Profiling
R. Shobana, V Pavithra, R Baghia Laxmi · 2025
Cyber deception strategies, powered by artificial intelligence (AI), have emerged as a critical tool in defending against increasingly sophisticated cyber threats. This chapter explores the integration of AI-driven honeypots and generative models for enhancing cybersecurity defenses, with a particular focus on mitigating cloud supply chain attacks. Cloud environments, characterized by their complexity and interconnected nature, have become prime targets for cybercriminals seeking to exploit vulnerabilities in trusted vendor relationships. AI-powered honeypots simulate realistic decoy systems within cloud infrastructures, luring attackers into engaging with fake environments, thus providing valuable intelligence on attacker tactics, techniques, and procedures (TTPs). The use of generative models, such as Generative Adversarial Networks (GANs), further strengthens deception by generating dynamic and convincing cloud service interactions, complicating attackers’ efforts to identify legitimate systems. By continuously adapting to attacker behavior, AI-driven deception frameworks can offer real-time detection, analysis, and mitigation of supply chain compromises. The chapter also highlights the role of AI in enhancing the capabilities of traditional cybersecurity tools, such as intrusion detection systems (IDS) and Security Information and Event Management (SIEM) platforms, by providing more accurate and timely threat intelligence. As cloud-based services continue to grow, the application of AI-driven honeypots and generative models is poised to become a fundamental aspect of proactive, scalable, and adaptive defense mechanisms against cloud supply chain attacks.