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Self-adaptive cyber deception and resilient network defense via adversarial environment simulation

The authors propose a proactive cyber-defense system that combines cyber deception, adversarial environment simulation and deep reinforcement learning, with GAN-generated attack vectors emulating zero-day and polymorphic threats. The aim is to train context-aware defense policies that detect malicious behavior, re-route detection vectors and trigger deceptive traps, evaluated with benign and adversarial traffic on a synthetic smart-grid network.

DecoyAdversary engagementMoving target defenseNetworkOT/ICS

What we know

The authors propose a proactive cyber-defense system that combines cyber deception, adversarial environment simulation and deep reinforcement learning, with GAN-generated attack vectors emulating zero-day and polymorphic threats. The aim is to train context-aware defense policies that detect malicious behavior, re-route detection vectors and trigger deceptive traps, evaluated with benign and adversarial traffic on a synthetic smart-grid network.

Evidence limits

Review limited to the abstract deposited in Crossref by the authors; full text, reproducibility and actual effectiveness of the results were not verified.

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

Sources and provenance

  1. Self-adaptive cyber deception and resilient network defense via adversarial environment simulation
    abstract · 2026-10-11

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

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