Safety of Linear Systems under Severe Sensor Attacks

TL;DR

This paper investigates the safety of linear systems under severe sensor spoofing attacks, providing a framework for state estimation and safety assurance using control barrier functions and quadratic programming.

Contribution

It introduces a novel characterization of all possible state estimates under severe sensor attacks and proposes safety design principles for offline and online system protection.

Findings

Derived exact set of all possible state estimates under severe attacks

Established conditions for safe sets against sensor spoofing

Proposed a quadratic program-based safety filter for real-time enforcement

Abstract

Cyber-physical systems can be subject to sensor attacks, e.g., sensor spoofing, leading to unsafe behaviors. This paper addresses this problem in the context of linear systems when an omniscient attacker can spoof several system sensors at will. In this adversarial environment, existing results have derived necessary and sufficient conditions under which the state estimation problem has a unique solution. In this work, we consider a severe attacking scenario when such conditions do not hold. To deal with potential state estimation uncertainty, we derive an exact characterization of the set of all possible state estimates. Using the framework of control barrier functions, we propose design principles for system safety in offline and online phases. For the offline phase, we derive conditions on safe sets for all possible sensor attacks that may be encountered during system deployment. For the online phase, with past system measurements collected, a quadratic program-based safety filter is proposed to enforce system safety. A 2D-vehicle example is used to illustrate the theoretical results.