Active Defense Against False Data Injection Attacks in Robotic Manipulators

Engineering notes

These strategies can be integrated into robotic systems for improved security against cyber attacks. Engineers can implement these methods to safeguard sensitive robotic applications in industrial automation and autonomous systems.

Chinese explanation / 中文解读

本研究探讨了机器人操控系统面对虚假数据注入攻击（FDIA）的防御措施。我们提出了异常感知虚拟阻尼和可操控性降低两种方法，以提高七自由度冗余机械臂在执行任务时的鲁棒性，这些方法在面对攻击时有效降低了对任务性能的影响。

Original 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.

Links and sources

• Official / arXiv page
• PDF from original source

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