Risk Assessment for Nonlinear Cyber-Physical Systems under Stealth Attacks

TL;DR

This paper introduces a comprehensive framework for assessing risks in nonlinear cyber-physical systems under stealth attacks, combining reachability analysis and risk distribution modeling to enable early warnings and explainable risk quantification.

Contribution

It presents a novel algorithm for approximating system reachability under stealth attacks and constructs a risk field to formally describe risk distribution in nonlinear systems.

Findings

Effective risk prediction in nonlinear systems

Early warning capability demonstrated in case study

Explainable risk quantification method

Abstract

Stealth attacks pose potential risks to cyber-physical systems because they are difficult to detect. Assessing the risk of systems under stealth attacks remains an open challenge, especially in nonlinear systems. To comprehensively quantify these risks, we propose a framework that considers both the reachability of a system and the risk distribution of a scenario. We propose an algorithm to approximate the reachability of a nonlinear system under stealth attacks with a union of standard sets. Meanwhile, we present a method to construct a risk field to formally describe the risk distribution in a given scenario. The intersection relationships of system reachability and risk regions in the risk field indicate that attackers can cause corresponding risks without being detected. Based on this, we introduce a metric to dynamically quantify the risk. Compared to traditional methods, our framework predicts the risk value in an explainable way and provides early warnings for safety control. We demonstrate the effectiveness of our framework through a case study of an automated warehouse.