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.

翻译：无人机（UAV）在监控、救援或投送等关键任务系统中具有重要价值。此类系统不可避免地会遭受网络攻击，包括通过耗尽任务无人机（MD）资源发起的拒绝服务（DoS）攻击。我们如何防御无人机任务系统免受DoS攻击？本文采用网络欺骗作为防御策略，提出使用蜜罐无人机（HD）来诱骗和转移攻击。攻击与欺骗性防御依赖于无线电信号强度：攻击者根据信号选择受害MD，而HD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。我们为攻击者和防御者构建一个优化问题，以确定各自策略——在最小化能耗的同时最大化任务性能。为解决该问题，我们提出一种名为HT-DRL的新方法。该方法将超博弈理论的解引入深度强化学习的神经网络，无需漫长的学习收敛过程即可识别最优解，从而实现系统性智能欺骗攻击者的方式。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HD方法在任务性能上比现有非HD方法提升高达两倍，同时保持低能耗。