Active Defense Against False Data Injection Attacks in Robotic Manipulators
For robotic systems, this work offers a practical defense against stealthy FDIAs that exploit feedback linearization, though the methods are domain-specific and incremental.
The paper addresses the vulnerability of robotic manipulators to false data injection attacks (FDIAs) and proposes two active defense methods—anomaly-aware virtual damping and manipulability reduction—that provide probabilistic guarantees on task execution. Simulations on a 7-DOF manipulator show these defenses substantially reduce FDIA impact compared to a Chi-squared anomaly detection system alone, while preserving nominal performance.
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.