Intelligent interactive honeypots: A systematization of AI-driven cyber deception

This Systematization of Knowledge (SoK) examines the evolving landscape of intelligent, interactive honeypots, which are deception-based cybersecurity tools that utilize AI/ML to engage attackers proactively. We introduce a novel taxonomy linking interaction levels to Cyber Kill Chain stages and systematically analyze peer-reviewed studies. Our findings expose key design trends, empirical evaluation strategies, and highlight critical research gaps, including scalability, standardization, and dataset diversity. We discuss the roles of LLMs and reinforcement learning, together with the emerging use of federated learning, in advancing honeypot realism and interactivity. This work aims to guide future research in developing adaptive, resilient deception systems for next-generation cyber defense.

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Publication Details

Journal
Computer Science Review
Published
2026-08-26
DOI
https://doi.org/10.1016/j.cosrev.2026.101053
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

Intelligent interactive honeypots: A systematization of AI-driven cyber deception

Elisa Bertino, Mahmoud Abdelsalam, Sajad Khorsandroo, Steve Nyamwaya
Computer Science Review
Network Security and Intrusion Detection
article

Intelligent interactive honeypots: A systematization of AI-driven cyber deception

Elisa Bertino, Mahmoud Abdelsalam, Sajad Khorsandroo, Steve Nyamwaya
article en

Abstract

This Systematization of Knowledge (SoK) examines the evolving landscape of intelligent, interactive honeypots, which are deception-based cybersecurity tools that utilize AI/ML to engage attackers proactively. We introduce a novel taxonomy linking interaction levels to Cyber Kill Chain stages and systematically analyze peer-reviewed studies. Our findings expose key design trends, empirical evaluation strategies, and highlight critical research gaps, including scalability, standardization, and dataset diversity. We discuss the roles of LLMs and reinforcement learning, together with the emerging use of federated learning, in advancing honeypot realism and interactivity. This work aims to guide future research in developing adaptive, resilient deception systems for next-generation cyber defense.

Computer Science ReviewVol. 63
Purdue University West Lafayette (US), North Carolina Agricultural and Technical State University (US)
National Science Foundation
Openalex Percentile: Top 8%
Network Security and Intrusion Detection
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Intelligent interactive honeypots: A systematization of AI-driven cyber deception — Elisa Bertino, Mahmoud Abdelsalam, et al. · Computer Science Review (2026) | TGRS Research Map | TGRS