Advancements and Challenges in AI-Powered Honeypots: A Comparative Study of Detection, Engagement and Ethical Implications

Honeypots have long served as decoy systems in cybersecurity, luring attackers to gather intelligence on their techniques. However, traditional honeypots face challenges like scalability, data overload, and susceptibility to detection. AI-powered honeypots, utilizing machine learning and reinforcement learning, address these limitations by dynamically adapting to evolving threats, improving detection rates, and engaging attackers for longer periods. This paper explores the evolution of honeypots from static systems to AI-driven defenses, highlighting their role in mitigating complex cyberattacks such as IoT botnet-driven DDoS attacks. We compare the strengths and weaknesses of AI-based honeypots with traditional models, examine their performance metrics, and discuss ongoing challenges related to data quality, evasion techniques, and ethical considerations.