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Worst-case stealthy innovation-based linear attack on remote state estimation
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 89, p. 117-124Article in journal (Refereed) Published
Abstract [en]

In this work, a security problem in cyber–physical systems is studied. We consider a remote state estimation scenario where a sensor transmits its measurement to a remote estimator through a wireless communication network. The Kullback–Leibler divergence is adopted as a stealthiness metric to detect system anomalies. We propose an innovation-based linear attack strategy and derive the remote estimation error covariance recursion in the presence of attack, based on which a two-stage optimization problem is formulated to investigate the worst-case attack policy. It is proved that the worst-case attack policy is zero-mean Gaussian distributed and the numerical solution is obtained by semi-definite programming. Moreover, an explicit algorithm is provided to calculate the compromised measurement. The trade-off between attack stealthiness and system performance degradation is evaluated via simulation examples. 

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 89, p. 117-124
Keyword [en]
Cyber–Physical system security, Integrity attack, Kullback–Leibler divergence, Remote state estimation, Economic and social effects, Estimation, Optimization, Wireless sensor networks, Wireless telecommunication systems, Gaussian distributed, Integrity attacks, Physical systems, Remote state estimations, Semi-definite programming, System performance degradation, Two stage optimizations, Wireless communication network, State estimation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223135DOI: 10.1016/j.automatica.2017.11.018ISI: 000427210200014Scopus ID: 2-s2.0-85038901975OAI: oai:DiVA.org:kth-223135DiVA, id: diva2:1193470
Funder
Knut and Alice Wallenberg FoundationSwedish Research Council
Note

QC 20180405

Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-04-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • de-DE
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