提出两种新方法防御机器人传感器数据攻击，提升系统鲁棒性。
Active Defense Against False Data Injection Attacks in Robotic Manipulators
- 引入感知异常的虚拟阻尼与可操作性降低策略
- 仿真显示攻击影响显著降低，且无攻击时任务性能不变
- 适合关注机器人安全控制的研究者与工程师
机器人系统易受虚假数据注入攻击（FDIA）威胁，攻击者通过篡改传感器信号实现恶意控制。反馈线性化使系统存在积分器漏洞，导致隐蔽攻击可能引发末端执行器行为大幅偏移而不触发警报。本文针对有限时域内的FDIA，形式化提出两种防御方法：异常感知的虚拟阻尼和可操作性降低，并提供名义任务执行的概率保障。在7自由度冗余机械臂上的仿真表明，相较于仅使用卡方阈值的自适应检测系统（ADS），所提方法显著降低了攻击影响，同时在无攻击情况下保持了正常的任务性能。
原文摘要 · Abstract (English)
Robotic systems are vulnerable to False Data Injection Attacks (FDIAs), where adversaries corrupt sensor signals to gain malicious control. Feedback linearization exposes robotic systems to integrator vulnerability, making them susceptible to stealthy attacks that can cause significant deviations in end-effector behavior without raising alarms. This paper addresses the resilience of manipulators against finite-horizon FDIAs by formalizing two defense methods, namely anomaly-aware virtual damping and manipulability reduction, with probabilistic guarantees on nominal task execution. Simulations on a 7-DOF redundant manipulator show that the proposed defenses substantially reduce the impact of FDIA compared to using solely a threshold-based ADS like the Chi-squared, while preserving nominal task performance in the absence of attack.
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