这篇论文用博弈论加强化学习来检测6G通信中的攻击者，针对城市环境里的波束操控，思路挺新颖的。
论文提出一种针对6G通感一体化（ISAC）系统的安全方案，利用博弈论建模合法用户与攻击者的交互，并集成到强化学习（RL）框架中。攻击者通过操控波束赋形方向增加干扰并误导发射器。在城市环境仿真中，该方法成功检测主动攻击者，有效解决了6G ISAC系统的动态安全挑战。
6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection
In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this capability, this work focuses on detecting an active attacker in an urban environment scenario, where the attacker intentionally manipulates beamforming directions to increase interference and mislead the transmitter into allocating the main lobe of beam toward itself instead of legitimate users. We apply game-theoretic approaches to model the interaction between legitimate users and the attacker, and integrate the resulting utility-based formulation into a reinforcement learning (RL) framework. Simulation results demonstrate that the proposed method effectively addresses security challenges in dynamic 6G ISAC systems.