This paper focuses on secure communications in UAV-assisted wireless networks, which comprise multiple legitimate UAVs (LE-UAVs) and an intelligent eavesdropping UAV (EA-UAV). The intelligent EA-UAV can observe the LE-UAVs'transmission strategies and adaptively adjust its trajectory to maximize information interception. To counter this threat, we propose a mode-switching scheme that enables LE-UAVs to dynamically switch between the data transmission and jamming modes, thereby balancing data collection efficiency and communication security. However, acquiring full global network state information for LE-UAVs' decision-making incurs significant overhead, as the network state is highly dynamic and time-varying. To address this challenge, we propose a digital twin-enabled simultaneous learning and modeling (DT-SLAM) framework that allows LE-UAVs to learn policies efficiently within the DT, thereby avoiding frequent interactions with the real environment. To capture the competitive relationship between the EA-UAV and the LE-UAVs, we model their interactions as a multi-stage Stackelberg game and jointly optimize the GUs' transmission control, UAVs' trajectory planning, mode selection, and network formation to maximize overall secure throughput. Considering potential model mismatch between the DT and the real environment, we propose a robust proximal policy optimization (RPPO) algorithm that encourages LE-UAVs to explore service regions with higher uncertainty. Numerical results demonstrate that the proposed DT-SLAM framework effectively supports the learning process. Meanwhile, the RPPO algorithm converges about 12% faster and the secure throughput can be increased by 8.6% compared to benchmark methods.

翻译：本文聚焦于无人机辅助无线网络中的安全通信问题，该网络由多个合法无人机（LE-UAV）和一个智能窃听无人机（EA-UAV）组成。智能EA-UAV能够观测LE-UAV的传输策略，并自适应调整其轨迹以最大化信息窃取量。为应对此威胁，我们提出一种模式切换方案，使LE-UAV能够在数据传输与干扰模式之间动态切换，从而平衡数据收集效率与通信安全性。然而，为LE-UAV决策获取全局网络状态信息会带来显著开销，因为网络状态具有高度动态性和时变性。针对这一挑战，我们提出一种数字孪生赋能的同步学习与建模框架（DT-SLAM），使LE-UAV能够在数字孪生中高效学习策略，从而避免与真实环境的频繁交互。为刻画EA-UAV与LE-UAV之间的竞争关系，我们将其交互建模为多阶段Stackelberg博弈，并联合优化地面用户的传输控制、无人机轨迹规划、模式选择及网络组成，以最大化整体安全吞吐量。考虑数字孪生与真实环境之间可能存在的模型失配问题，我们提出一种鲁棒近端策略优化算法（RPPO），鼓励LE-UAV探索不确定性更高的服务区域。数值结果表明，所提出的DT-SLAM框架能够有效支持学习过程。同时，与基准方法相比，RPPO算法的收敛速度提升约12%，安全吞吐量增加8.6%。