Resilient Consensus against Mobile Malicious Agents

Abstract

This paper addresses consensus problems in the presence of adversaries that can move within the network and induce faulty behaviors in the attacked agents. By employing mobile adversary models from the computer science literature, we develop three protocols which can mitigate the influence of malicious agents. The algorithms follow the class of mean subsequence reduced (MSR) algorithms, under which agents ignore the suspicious values received from neighbors during their state updates. Different from the static model, even after the adversaries move away, the infected agents may remain faulty in their values for a short while, which must be taken into account. We develop conditions on the network structures for both the complete and non-complete graph cases, under which the proposed algorithms are guaranteed to attain resilient consensus. An illustrative example is provided to verify the effectiveness of our approach.