AI in Cyber Deception: Creating Adaptive and Realistic Honeypot Systems

Abstract

As cyber threats evolve in complexity, traditional cybersecurity measures are often insufficient to counteract sophisticated attacks. This paper explores the integration of Artificial Intelligence (AI) in the design and deployment of honeypot systems, aiming to enhance their adaptability and realism. We propose a framework for developing AI-driven honeypots that dynamically adapt to attacker behavior, increasing the efficacy of cyber deception strategies. The framework includes AI algorithms for behavior analysis, deception generation, and response adaptation. We present a case study demonstrating the application of this framework in a simulated environment, showing how AI can improve honeypot effectiveness in detecting and mitigating advanced persistent threats.