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Adaptive Honeypot Allocation in Multi‑Attacker Networks via Bayesian Stackelberg Games

The authors present a multi-attacker Bayesian Stackelberg framework for allocating honeypots in a directed network, modeling each attacker as a follower with a latent type and the defender as replanning via a DOBSS-style mixed-integer linear program under posterior beliefs. They aim to scale defense to realistic graphs using layered structures and pruning.

HoneypotDecoyAdversary engagementNetwork

What we know

The authors present a multi-attacker Bayesian Stackelberg framework for allocating honeypots in a directed network, modeling each attacker as a follower with a latent type and the defender as replanning via a DOBSS-style mixed-integer linear program under posterior beliefs. They aim to scale defense to realistic graphs using layered structures and pruning.

Evidence limits

Review based only on the Crossref-deposited abstract; the full text was not examined and results or implementation were not validated.

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

Sources and provenance

  1. Adaptive Honeypot Allocation in Multi‑Attacker Networks via Bayesian Stackelberg Games
    abstract · 2026-10-11

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

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