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D3O-IIoT: deep reinforcement learning-driven dynamic deception orchestration for industrial IoT security

The authors present D3O-IIoT, a reinforcement learning model that dynamically coordinates deception techniques such as honeypot deployment, moving target defense, fake telemetry injection, and node isolation, driven by real-time threat monitoring. The defense problem is formulated as a Markov Decision Process, in which a Dueling Deep Q-Network agent maximizes a multi-objective reward to balance attack mitigation, deception engagement, false positive control, and resource cost.

HoneypotMoving target defenseDecoyAdversary engagementIoTOT/ICS

What we know

The authors present D3O-IIoT, a reinforcement learning model that dynamically coordinates deception techniques such as honeypot deployment, moving target defense, fake telemetry injection, and node isolation, driven by real-time threat monitoring. The defense problem is formulated as a Markov Decision Process, in which a Dueling Deep Q-Network agent maximizes a multi-objective reward to balance attack mitigation, deception engagement, false positive control, and resource cost.

Evidence limits

Review based solely on the publisher-deposited abstract; no full text was accessed and results were not independently validated.

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

Sources and provenance

  1. D3O-IIoT: deep reinforcement learning-driven dynamic deception orchestration for industrial IoT security
    abstract · 2026-10-11

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

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