Year: 2026
Type: article
Study design: Systematic Review
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.
Source: Computer Science Review
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,190
Funder: National Science Foundation
Awards: Collaborative Research: SaTC: EDU: Adversarial Malware Analysis - An Artificial Intelligence Driven Hands-On Curriculum for Next Generation Cyber Security Workforce, Collaborative Research: SaTC: EDU: A Hands-on Approach to Securing Self-Driving Networks, Collaborative Research: SaTC: EDU: Enhancing IoT Software and System Security Education with Student-centered Pedagogy and Next Generation Capture-the-Flag Platform, Excellence in Research: Cyber Threats Early Warning Framework for Operational Technology Systems, Collaborative Research: CyberTraining: Implementation: Small: Training AI-skilled and Fairness-aware Research Workforce for Cloud Infrastructure Cybersecurity
OpenAlex ID: W7204262613