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An overview on multi-agent consensus under adversarial attacks
Tokyo Inst Technol, Dept Comp Sci, Yokohama, Japan..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-2704-0427
Nanjing Univ Sci & Technol, Sch Automation, Nanjing, Peoples R China..
2022 (English)In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 53, p. 252-272Article, review/survey (Refereed) Published
Abstract [en]

This paper presents an overview on the recent advances in the research of security of cyber-physical systems. We place particular emphases on consensus problems for multi-agent systems in hostile environments and their analyses on the resiliency against two types of attacks. First, we discuss a class of data injection attacks by focusing on the approach based on mean subsequence reduced (MSR) algorithms and their variants. Agents equipped with such algorithms will ignore their neighbors taking extreme state values. Characterizations on the properties necessary for network topologies and moreover a number of extensions with enhanced resiliency will be established. As the second class of attacks, the effects of denial-of-service (DoS) attacks will be examined in the context of multi-agent consensus. By employing a DoS model based on the energy constraints of the attacker, we will observe that robustness against such attacks may depend on system properties such as dynamics of the individual agents and network structures. Applications of the algorithms will be further discussed for clock synchronization in wireless sensor networks and control of a group of mobile robots.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 53, p. 252-272
Keywords [en]
Cyber-physical systems, Cyber-security, Distributed algorithms, Multi-agent consensus, False-data injection attacks, Denial-of-service attacks
National Category
Reliability and Maintenance Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-315708DOI: 10.1016/j.arcontrol.2022.01.004ISI: 000807345600016Scopus ID: 2-s2.0-85126802042OAI: oai:DiVA.org:kth-315708DiVA, id: diva2:1683686
Note

QC 20220718

Available from: 2022-07-18 Created: 2022-07-18 Last updated: 2022-07-18Bibliographically approved

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Wang, Yuan

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CiteExportLink to record
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  • apa
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  • de-DE
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