Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Denial-of-Service (DoS) attacks to overwhelm the resources of mission drones (MDs). How can we defend UAV mission systems against DoS attacks? We adopt cyber deception as a defense strategy, in which honey drones (HDs) are proposed to bait and divert attacks. The attack and deceptive defense hinge upon radio signal strength: The attacker selects victim MDs based on their signals, and HDs attract the attacker from afar by emitting stronger signals, despite this reducing battery life. We formulate an optimization pro...
| Authors | Zelin Wan, Jin-Hee Cho, Mu Zhu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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