Resilient Detection and Recovery of Autonomous Systems Operating under On-board Controller Cyber Attacks

Abstract

Cyber-attacks, failures, and implementation errors inside the controller of an autonomous system can affect its correct behavior leading to unsafe states and degraded performance. In this paper, we focus on such problems specifically on cyber-attacks that manipulate controller parameters like the gains in a feedback controller or that triggers different behaviors or block inputs based on specific values of the state and tracking error. If such attacks are undetected, they can lead to the partial or complete loss of system's control authority, resulting in a hijacking and leading the autonomous system towards unforeseen states. To deal with this problem, we propose a runtime monitoring and recovery scheme in which: 1) we leverage the residual between the expected and the received measurements to detect inconsistencies in the generated inputs and 2) provide a recovery method for counteracting the malicious effects to allow for resilient operations by manipulating the reference signal and state vector provided to the system to avoid the affected regions in the state and error space. We validate our approach with Matlab simulations and experiments on unmanned ground vehicles resiliently performing operations in the presence of malicious attacks to on-board controllers.