Active Defense Against False Data Injection Attacks in Robotic Manipulators
摘要
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.
引用
@article{arxiv.2605.17950,
title = {Active Defense Against False Data Injection Attacks in Robotic Manipulators},
author = {Gabriele Gualandi and Carl Mikael Larsson and Alessandro V. Papadopoulos},
journal= {arXiv preprint arXiv:2605.17950},
year = {2026}
}
备注
Extended 8-page version containing full proofs. An abridged 6-page version has been accepted for publication in the Proceedings of the 23rd IFAC World Congress (2026). v3: Minor typographical fixes and updated reference formatting