While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications.

翻译：尽管具有灵活机动性的无人机被寄望于增强无线通信中的物理层安全性，但适应这种高度动态网络的高效安全设计仍颇具挑战。从优化视角延伸的传统方法通常较为复杂，尤其是在联合考虑无人机相关场景中不同尺度的因素（如部署与传输）时。本文通过提出一种深度图强化学习框架来解决无人机多用户安全通信问题。具体而言，我们将安全波束成形重新阐释为图神经网络学习任务，其中用户间的相互干扰通过消息传递机制进行管理。随后，无人机部署通过柔性演员-评论家强化学习获得，其中基于图神经网络的安全波束成形被用于指导部署策略更新。仿真结果表明，所提方法实现了接近最优的安全性能，并显著提升了策略确定效率。此外，该深度图强化学习框架提供了可扩展的解决方案，能够适应各种网络场景与配置，为无人机通信中的信息安全奠定了坚实基础。