Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks

TL;DR

This paper develops a secure safety filter for nonlinear systems under sensor spoofing attacks, extending existing linear-focused methods to more complex real-world nonlinear scenarios with theoretical guarantees.

Contribution

It introduces a novel secure safety filter design for nonlinear systems, incorporating secure state estimation and control barrier functions, applicable to both exact and relaxed observability cases.

Findings

Validated on a unicycle vehicle with compromised sensors

Provides theoretical safety guarantees under sensor attacks

Extends security methods from linear to nonlinear systems

Abstract

This paper presents a secure safety filter design for nonlinear systems under sensor spoofing attacks. Existing approaches primarily focus on linear systems which limits their applications in real-world scenarios. In this work, we extend these results to nonlinear systems in a principled way. We introduce exact observability maps that abstract specific state estimation algorithms and extend them to a secure version capable of handling sensor attacks. Our generalization also applies to the relaxed observability case, with slightly relaxed guarantees. More importantly, we propose a secure safety filter design in both exact and relaxed cases, which incorporates secure state estimation and a control barrier function-enabled safety filter. The proposed approach provides theoretical safety guarantees for nonlinear systems in the presence of sensor attacks. We numerically validate our analysis on a unicycle vehicle equipped with redundant yet partly compromised sensors.