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Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

This work aims to develop a drone-based surveillance mission system when Denial-of-Service (DoS) attacks disrupt normal operations. The authors introduce cyber deception via honey drones (HDs) that exhibit fake vulnerabilities and use stronger signal strengths to lure DoS attacks, formulate an optimization problem over HD and mission drone signal strengths, and solve it using deep reinforcement learning (DRL) with the Asynchronous Advantage Actor-Critic (A3C) algorithm, evaluated in a physical engine-based simulation testbed.

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What we know

This work aims to develop a drone-based surveillance mission system when Denial-of-Service (DoS) attacks disrupt normal operations. The authors introduce cyber deception via honey drones (HDs) that exhibit fake vulnerabilities and use stronger signal strengths to lure DoS attacks, formulate an optimization problem over HD and mission drone signal strengths, and solve it using deep reinforcement learning (DRL) with the Asynchronous Advantage Actor-Critic (A3C) algorithm, evaluated in a physical engine-based simulation testbed.

Evidence limits

Review based only on the Crossref-deposited abstract; no full text was read and no results were independently validated.

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

Sources and provenance

  1. Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones
    abstract · 2026-10-11

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

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