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Honeypots have been part of defensive security strategy since the late 1980s, yet the core limitation of static environments — that experienced attackers fingerprint and avoid them in seconds — has never been fully solved. This review examines 24 studies published between 2019 and 2025 on integrating artificial intelligence into honeypot architectures. We group the literature into four categories: reinforcement learning-based adaptive systems, large language modelpowered interaction engines, multi-agent deception frameworks, and automated threat intelligence pipelines. The findings show that AIenhanced honeypots consistently outperform static counterparts on engagement duration and fingerprinting resistance, sometimes by wide margins. That said, persistent problems keep surfacing across the literature — no standard evaluation benchmark exists, cold-start challenges for RL-based systems are rarely addressed, and LLM response consistency under adversarial probing remains poorly characterized. Six open research directions are identified, with particular attention to shared benchmarks, adversarial robustness testing, and longitudinal deployment studies in real operational environments.
Keywords:
Honeypot, Cyber Deception, Reinforcement Learning, Large Language Models, MITRE ATT&CK, Threat Intelligence, Network Security, Adaptive Systems.
Cite Article:
"A Comprehensive Review of AI-Based Honeypot Systems: Advances in Cyber Deception and Intelligent Threat Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a88-a91, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605012.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator