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An optimal defensive deception framework for the container‐based cloud with deep reinforcement learning

The authors propose a defensive deception framework for the container-based cloud, aiming to reveal stealthy attackers by presenting intentionally falsified information. The approach formalizes a System Risk Graph (SRG) as an updatable adversarial model, trains a deep reinforcement learning (DRL) model over the SRG so the agent produces decoy placement and deceptive routing strategies, and defines a coefficient of deception to evaluate the placement strategy.

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What we know

The authors propose a defensive deception framework for the container-based cloud, aiming to reveal stealthy attackers by presenting intentionally falsified information. The approach formalizes a System Risk Graph (SRG) as an updatable adversarial model, trains a deep reinforcement learning (DRL) model over the SRG so the agent produces decoy placement and deceptive routing strategies, and defines a coefficient of deception to evaluate the placement strategy.

Evidence limits

Review based only on the publisher-deposited Crossref 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. An optimal defensive deception framework for the container‐based cloud with deep reinforcement learning
    abstract · 2026-10-11

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

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