A Unified Attack Detection Strategy for Multi-Agent Systems over Transient and Steady Stages

TL;DR

This paper introduces a comprehensive detection strategy for multi-agent systems that effectively identifies communication, agent, and hybrid attacks during both transient and steady operational stages, enhancing system resilience.

Contribution

It presents a novel unified detection framework applicable to various attack types and stages, utilizing watermarking and convergence rate analysis, with a practical example in vehicle platooning.

Findings

Watermarking-based detection effectively identifies communication attacks.

Convergence rate analysis detects agent layer attacks.

Hybrid attack detection framework requires fewer assumptions.

Abstract

This paper proposes a unified detection strategy against three kinds of attacks for multi-agent systems (MASs) which is applicable to both transient and steady stages. For attacks on the communication layer, a watermarking-based detection scheme with KullbackLeibler (KL) divergence is designed. Different from traditional communication schemes, each agent transmits a message set containing two state values with different types of watermarking. It is found that the detection performance is determined by the relevant parameters of the watermarking signal. Unlike the existing detection manoeuvres, such a scheme is capable of transient and steady stages. For attacks on the agent layer, a convergence rate related detection approach is put forward. It is shown that the resilience of the considered system is characterized by the coefficient and offset of the envelope. For hybrid attacks, based on the above detection mechanisms, a general framework resorting to trusted agents is presented, which requires weaker graph conditions and less information transmission. Finally, an example associated with the platooning of connected vehicles is given to support the theoretical results.