通过超博弈深度强化学习实现任务监视的网络欺骗
Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
AI总结：
本文提出基于超博弈深度强化学习的网络欺骗策略，通过诱饵无人机有效防御DDoS攻击，提升任务性能并降低能耗。
摘要：
无人驾驶航空器（UAVs）在监视、救援或配送等关键系统中具有重要价值，但此类系统易受网络攻击，如通过拒绝服务（DoS）攻击耗尽任务无人机（MDs）资源。本文采用网络欺骗作为防御策略，提出诱饵无人机（HDs）以诱骗和分散攻击。攻击与欺骗防御依赖于无线电信号强度：攻击者根据信号选择受害者MDs，而HDs通过发射更强的信号吸引攻击者，尽管这会减少电池寿命。我们为攻击者和防御者制定优化问题，以识别各自策略以最大化任务性能并最小化能耗。为解决此问题，我们提出了一种新型方法，称为HT-DRL。HT-DRL通过将超博弈理论的解决方案纳入深度强化学习的神经网络中，无需长时间学习收敛即可获得最优解。这实现了系统性地智能欺骗攻击者的方法。我们分析了不同攻击策略下的各种防御机制性能。进一步，基于HT-DRL的HD方法在任务性能上比现有非HD方法高出两倍，同时能耗较低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
