From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks

TL;DR

This paper enhances the security of robotic manipulators against stealthy cyberattacks by integrating active control-based defence mechanisms with probabilistic guarantees, significantly reducing attack impact.

Contribution

It introduces an active defence architecture that attenuates malicious control inputs using a novel anomaly score predictor, improving resilience over passive monitoring methods.

Findings

Reduces attack-induced end-effector deviation

Preserves task performance under attack

Provides probabilistic stability guarantees

Abstract

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.