As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

中文翻译：

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基于深度强化学习的自动驾驶汽车网络干扰和窃听防御方案

作为传统无线服务的遗留问题，非法窃听被认为是联网和自动驾驶汽车 (CAV) 网络中的关键安全挑战之一。我们的工作考虑使用分布式卡尔曼滤波 (DKF) 和深度强化学习 (DRL) 技术来提高抗窃听通信能力并减轻干扰。为了提高针对智能窃听器和干扰器的安全性能，我们首先开发了一种 DKF 算法，该算法能够通过在相邻节点之间共享状态估计来更准确地跟踪攻击者。然后，在保证授权车辆用户的通信质量要求的同时，建立了控制发射功率和选择通信信道的设计问题。由于窃听和干扰模型是不确定和动态的，因此开发了一种基于分层深度 Q 网络 (DQN) 的体系结构来设计反窃听功率控制和可能的信道选择策略。具体来说，可以首先快速实现没有窃听行为先验信息的最优功率控制方案。基于系统保密率评估，然后在必要时执行频道选择过程。仿真结果证实，与目前可用的技术相比，我们的干扰和窃听防御技术提高了保密率和可实现的通信率。可以首先快速实现没有窃听行为先验信息的最优功率控制方案。基于系统保密率评估，然后在必要时执行频道选择过程。仿真结果证实，与目前可用的技术相比，我们的干扰和窃听防御技术提高了保密率和可实现的通信率。可以首先快速实现没有窃听行为先验信息的最优功率控制方案。基于系统保密率评估，然后在必要时执行频道选择过程。仿真结果证实，与目前可用的技术相比，我们的干扰和窃听防御技术提高了保密率和可实现的通信率。