Privacy-Preserving Stealthy Attack Detection in Multi-Agent Control Systems

TL;DR

This paper introduces a scalable, privacy-preserving attack detection framework for multi-agent control systems, capable of identifying stealthy cyber-physical attacks through a combination of global and local observers.

Contribution

It proposes a novel decentralized detection structure that ensures privacy and scalability while effectively detecting covert and zero-dynamics attacks.

Findings

The framework can detect stealthy attacks under certain theoretical conditions.

Decentralized local observers improve scalability and privacy.

Simulation validates the effectiveness of the proposed detection method.

Abstract

This paper develops a glocal (global-local) attack detection framework to detect stealthy cyber-physical attacks, namely covert attack and zero-dynamics attack, against a class of multi-agent control systems seeking average consensus. The detection structure consists of a global (central) observer and local observers for the multi-agent system partitioned into clusters. The proposed structure addresses the scalability of the approach and the privacy preservation of the multi-agent system's state information. The former is addressed by using decentralized local observers, and the latter is achieved by imposing unobservability conditions at the global level. Also, the communication graph model is subject to topology switching, triggered by local observers, allowing for the detection of stealthy attacks by the global observer. Theoretical conditions are derived for detectability of the stealthy attacks using the proposed detection framework. Finally, a numerical simulation is provided to validate the theoretical findings.