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.

翻译：机器人系统易受虚假数据注入攻击（FDIAs）影响，攻击者通过篡改传感器信号实现恶意控制。反馈线性化使机器人系统面临积分器脆弱性，导致其易受隐蔽攻击——此类攻击可在不触发警报的前提下造成末端执行器行为的显著偏差。本文通过形式化两种防御方法（即异常感知虚拟阻尼与可操作性降阶），解决机械臂对有限时域FDIAs的鲁棒性问题，并对其标称任务执行提供概率性保障。在七自由度冗余机械臂上的仿真结果表明：与单纯使用基于阈值的异常检测系统（如卡方检测器）相比，所提防御策略能在无攻击时保持标称任务性能的同时，显著削弱FDIAs的影响。