Cyber-physical robotic systems are vulnerable to false data injection attacks (FDIAs), in which an adversary corrupts sensor signals while evading residual-based passive anomaly detectors such as the chi-squared test. Such stealthy attacks can induce substantial end-effector deviations without triggering alarms. This paper studies the resilience of redundant manipulators to stealthy FDIAs and advances the architecture from passive monitoring to active defence. We formulate a closed-loop model comprising a feedback-linearized manipulator, a steady-state Kalman filter, and a chi-squared-based anomaly detector. Building on this passive monitoring layer, we propose an active control-level defence that attenuates the control input through a monotone function of an anomaly score generated by a novel actuation-projected, measurement-free state predictor. The proposed design provides probabilistic guarantees on nominal actuation loss and preserves closed-loop stability. From the attacker perspective, we derive a convex QCQP for computing one-step optimal stealthy attacks. Simulations on a 6-DOF planar manipulator show that the proposed defence significantly reduces attack-induced end-effector deviation while preserving nominal task performance in the absence of attacks.

翻译：网络物理机器人系统易受虚假数据注入攻击（FDIAs）的影响，攻击者可篡改传感器信号，同时规避基于残差的被动异常检测器（如卡方检验）。此类隐蔽攻击可在不触发警报的情况下导致末端执行器产生显著偏差。本文研究了冗余机械臂对隐蔽FDIAs的弹性，并将系统架构从被动监控推进至主动防御。我们构建了一个包含反馈线性化机械臂、稳态卡尔曼滤波器和基于卡方检验的异常检测器的闭环模型。在此被动监控层基础上，提出一种主动控制级防御策略，通过新型无测量驱动投影状态预测器生成的异常评分的单调函数来衰减控制输入。所提设计为标称驱动损失提供了概率保证，并保持了闭环稳定性。从攻击者视角出发，我们推导出用于计算单步最优隐蔽攻击的凸二次约束二次规划问题。在六自由度平面机械臂上的仿真表明，所提出的防御策略能显著降低攻击引起的末端执行器偏差，同时在无攻击情况下保持标称任务性能。