CyberdeceptionAtlas · Question → testES

Library / Paper

Paper

AI-Driven Cyber Deception: A Reinforcement Learning Honeypot Framework with Threat Intelligence and Automated Containment

The authors propose a dynamic honeypot framework combining a reinforcement-learning deception agent (tabular Q-learning, formalized as an MDP), threat-intelligence fusion via indicators of compromise, and automated containment actions. Its purpose is to adapt the honeypot's exposed persona and guide deception decisions, evaluated in an isolated virtualized testbed.

HoneypotAdversary engagementNetworkCloudIoTApplication

What we know

The authors propose a dynamic honeypot framework combining a reinforcement-learning deception agent (tabular Q-learning, formalized as an MDP), threat-intelligence fusion via indicators of compromise, and automated containment actions. Its purpose is to adapt the honeypot's exposed persona and guide deception decisions, evaluated in an isolated virtualized testbed.

Evidence limits

Review based only on the Crossref-deposited abstract; the full text was not read and results were not independently validated.

Reviewed: author abstract. Full-text results were not analyzed.

Sources and provenance

  1. AI-Driven Cyber Deception: A Reinforcement Learning Honeypot Framework with Threat Intelligence and Automated Containment
    abstract · 2026-10-11

Reviewed: 2026-10-16. This record may change when new evidence is found.

RIS · BibTeX