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A Multiphase Dynamic Deployment Mechanism of Virtualized Honeypots Based on Intelligent Attack Path Prediction

The authors propose a multiphase dynamic deployment mechanism for virtualized honeypots (MD2VH) based on intelligent attack path prediction, modeling attack and defense characteristics with a Bayesian state attack graph and a multiphase deployment optimization model based on an extended Markov decision process, and generating deployment strategies by combining online and offline reinforcement learning. They implement a prototype system on software-defined networking and virtualization containers and report experiments on capture rate under simple and complex topologies.

HoneypotMoving target defenseNetwork

What we know

The authors propose a multiphase dynamic deployment mechanism for virtualized honeypots (MD2VH) based on intelligent attack path prediction, modeling attack and defense characteristics with a Bayesian state attack graph and a multiphase deployment optimization model based on an extended Markov decision process, and generating deployment strategies by combining online and offline reinforcement learning. They implement a prototype system on software-defined networking and virtualization containers and report experiments on capture rate under simple and complex topologies.

Evidence limits

Assessment based solely on the Crossref-deposited abstract; full text, replication of results and validity of reported figures were not verified.

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

Sources and provenance

  1. A Multiphase Dynamic Deployment Mechanism of Virtualized Honeypots Based on Intelligent Attack Path Prediction
    abstract · 2026-10-11

Reviewed: 2026-10-13. This record may change when new evidence is found.

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