[2603.20981] Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning

Computer Science > Cryptography and Security arXiv:2603.20981 (cs) [Submitted on 21 Mar 2026] Title:Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning Authors:Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua, Munindar P. Singh View a PDF of the paper titled Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning, by Zelin Wan and 5 other authors View PDF HTML (experimental) Abstract: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 problem for the attacker and defender to identify their respective strategies for maximizing mission performance while minimizing energy consumption. To address this problem, we propose a novel approach, called HT-DRL. HT-DRL identifies optimal solutions without a long learning convergence time by taking the solutions of ...