MIXED-SENSE：用于无人机对抗虚假数据注入攻击测试与评估的混合现实传感器仿真框架
MIXED-SENSE: A Mixed Reality Sensor Emulation Framework for Test and Evaluation of UAVs Against False Data Injection Attacks
AI总结：
本研究提出高保真混合现实传感器仿真框架MIXED-SENSE，结合Gazebo仿真与动作捕捉系统复现传感器信号特征，可测试评估无人机对抗虚假数据注入攻击的韧性，并通过GNSS攻击实验验证了框架有效性。
摘要：
我们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）抵御虚假数据注入（FDI）攻击的韧性。该方法可用于评估FDI攻击的影响、基准测试攻击检测器的性能，以及验证单无人机和无人机蜂群作战中缓解/重构策略的有效性。我们的混合现实框架利用Gazebo的高保真仿真与动作捕捉系统，实时仿真本体感受（如GNSS）和外部感受（如相机）的传感器测量值。我们提出了一种实证方法，可在这些测量值中忠实复现延迟、噪声等信号特征。最后，我们通过一项针对真实无人机的仿真GNSS攻击混合现实实验，验证了所提框架的有效性：（1）展示了虚假数据注入攻击对GNSS测量值的影响；（2）验证了我们在前期工作中开发的分布式相机网络缓解策略的效果。我们的开源实现可在https://github.com/CogniPilot/mixed_sense获取。
英文摘要：
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}}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
