An Intelligent Honeypot System for Proactive Threat Detection and Engagement

Abstract— The increasing sophistication of cyber threats made traditional intrusion observation and defense systems inadequate for modern attack approaches. Honeypot is a cybersecurity technique where a decoy system is created to set up which looks like a real computer, network, or service, helps to attract, detect, and study attackers. The paper hands an intelligent honeypot system augmented with adaptive artificial intelligence to preparedly find, categorize, and involve cyber adversaries. The considered system integrates real-time traffic monitoring, machine learning-based aims segmentation and flexible artifice methods that dynamically transform based on attacker’s interaction. Key advancements integrate context- aware record and document creation, sandboxed malware accomplishment for behavioral evaluation, and structured trap services that change to various attack vectors. Moreover, the framework embeds anomaly detection, attacker profiling, and automated reporting to elevate situational awareness for network administrators. By growing attacker attention span and truthfulness, the system obtains deeper threat assessment while residual resilient to elusion. Detailed modeling and evaluation demonstrate that the suggested system decreases false positives, enhances threat classification authenticity, and assists proactive countermeasures. The adaptive honeypot not only notices and inspects attacks but also supplies to predictive cybersecurity defense by schooling from evolving attack patterns. The conclusions emphasize the potential of AI- driven deception systems to transform honeypots from passive traps into active, intelligent cybersecurity mechanisms capable of addressing to rising threats in real time. Index Terms— Cybersecurity, Honeypot, Adaptive Deception, Threat Detection, Intrusion Analysis, Machine Learning, Artificial Intelligence, Network Security, Sandboxing, Cyber Threat Intelligence