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 hypergame theory into the neural network of deep reinforcement learning. This achieves a systematic way to intelligently deceive attackers. We analyze the performance of diverse defense mechanisms under different attack strategies. Further, the HT-DRL-based HD approach outperforms existing non-HD counterparts up to two times better in mission performance while incurring low energy consumption.
Abstract (translated)
无人机（UAVs）在监视、救援或投送等任务关键型系统中具有重要价值。此类系统自然会招致网络攻击，包括旨在耗尽任务无人机（MDs）资源的拒绝服务（DoS）攻击。我们如何防御无人机任务系统免受DoS攻击？本研究采用网络欺骗作为防御策略，通过部署蜜罐无人机（HDs）引诱并转移攻击。攻击行为与欺骗防御均依赖于无线电信号强度：攻击者依据信号强度选择受害的任务无人机，而蜜罐无人机则通过发射更强信号从远处吸引攻击者，尽管这会缩短电池寿命。我们构建了一个优化问题，用于确定攻击方与防御方各自在最大化任务性能的同时最小化能耗的最优策略。针对该问题，我们提出一种名为HT-DRL（超博弈理论与深度强化学习结合）的新方法。HT-DRL通过将超博弈理论的解融入深度强化学习的神经网络，无需长时间学习收敛即可识别最优解，从而实现系统化智能欺骗攻击者的方法。我们分析了不同攻击策略下多种防御机制的性能。此外，基于HT-DRL的蜜罐方案在任务性能上比现有非蜜罐方案提升近两倍，同时保持低能耗。
URL
https://arxiv.org/abs/2603.20981
