Active Defense Against False Data Injection Attacks in Robotic Manipulators

TL;DR

This paper proposes two novel defense methods, anomaly-aware virtual damping and manipulability reduction, to enhance robotic manipulators' resilience against stealthy false data injection attacks, ensuring task integrity.

Contribution

It introduces formalized, probabilistically guaranteed defense strategies that outperform threshold-based methods in mitigating FDIA impacts on robotic manipulators.

Findings

Proposed defenses significantly reduce FDIA impact in simulations.

Defense methods preserve nominal task performance without attacks.

Outperforms traditional threshold-based anomaly detection.

Abstract

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.