We present a high-fidelity Mixed Reality sensor emulation framework for testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs) against false data injection (FDI) attacks. The proposed approach can be utilized to assess the impact of FDI attacks, benchmark attack detector performance, and validate the effectiveness of mitigation/reconfiguration strategies in single-UAV and UAV swarm operations. Our Mixed Reality framework leverages high-fidelity simulations of Gazebo and a Motion Capture system to emulate proprioceptive (e.g., GNSS) and exteroceptive (e.g., camera) sensor measurements in real-time. We propose an empirical approach to faithfully recreate signal characteristics such as latency and noise in these measurements. Finally, we illustrate the efficacy of our proposed framework through a Mixed Reality experiment consisting of an emulated GNSS attack on an actual UAV, which (i) demonstrates the impact of false data injection attacks on GNSS measurements and (ii) validates a mitigation strategy utilizing a distributed camera network developed in our previous work. Our open-source implementation is available at \href{https://github.com/CogniPilot/mixed\_sense}{\texttt{https://github.com/CogniPilot/mixed\_sense}}

翻译：本文提出了一种高保真混合现实传感器仿真框架，用于测试和评估无人机（UAV）对虚假数据注入（FDI）攻击的抵御能力。所提方法可用于评估FDI攻击的影响、对标攻击检测器性能，并在单无人机及无人机集群任务中验证缓解/重构策略的有效性。我们的混合现实框架利用Gazebo的高保真仿真和运动捕捉系统，实时仿真本体感受（如GNSS）与外感受（如相机）传感器测量值。我们提出了一种经验方法，以在这些测量值中忠实地复现延迟与噪声等信号特性。最后，我们通过一个混合现实实验说明了所提框架的效能：该实验对一架真实无人机实施了仿真的GNSS攻击，从而（i）展示了虚假数据注入攻击对GNSS测量值的影响，并（ii）验证了一种利用我们先前工作中开发的分布式相机网络的缓解策略。我们的开源实现发布于 \href{https://github.com/CogniPilot/mixed\_sense}{\texttt{https://github.com/CogniPilot/mixed\_sense}}。