AI-Powered Adaptive Honeypot for Advanced Threat Intelligence

Abstract

The modern digital landscape is subjected to quickly changing threats like zero-day attacks, advanced persistent threats (APTs), and polymorphic malware, which often go unnoticed by the traditional firewalls and signature-based detection systems. Traditional honeypots are still a valuable tool, but their static nature makes them very easy to detect. In this paper, we present an AI-enabled adaptive honeypot that can constantly change its interaction patterns, vulnerability exposure, and deceptive responses according to the attacker's behavior in real time. The proposed system combines the use of machine learning for intrusion detection, reinforcement learning for deceptive practices, and a threat intelligence engine that correlates the data collected from the honeypot with other external data sources. The experimental tests including simulated attacks and malware payloads reveal the possibility of longer attacker dwell time, better detection of new threats, and high-fidelity intelligence being produced automatically. The use of this adaptive method is a considerable boost to the capacity of proactive defense.