Strategic Attacks on Finite-Time Consensus

Abstract

This work addresses the linear consensus problem in multi-agent systems under adversarial attacks. We examine scenarios where legitimate agents utilize stochastic inter-agent trust observations to assess the likelihood of neighboring agents acting maliciously in order to mitigate their impact. Malicious agents, in turn, aim to strategically choose what values to send to maximize the disagreement among legitimate agents in a finite horizon. In contrast to prior studies that assume static adversarial behavior and focus on asymptotic consensus, this work investigates the impact of strategic attacks within a finite number of iterations. Specifically, we characterize the maximum disagreement that malicious agents can induce in finite time and the computational complexity of computing the best attack strategy. We compare the effectiveness of different attack strategies through numerical experiments.