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Analysis and Mitigation of Bias Injection Attacks Against a Kalman Filter
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-1835-2963
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 8393-8398Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider a state estimation problem for stochastic linear dynamical systems in the presence of bias injection attacks. A Kalman filter is used as an estimator, and a chi-squared test is used to detect anomalies. We first show that the impact of the worst-case bias injection attack in a stochastic setting can be analyzed by a deterministic quadratically constrained quadratic program, which has an analytical solution. Based on this result, we propose a criterion for selecting sensors to secure in order to mitigate the attack impact. Furthermore, we derive a condition on the necessary number of sensors to secure in order for the impact to be less than a desired threshold.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 8393-8398
Keywords [en]
Cyber-Attacks, Cyber-Physical Systems, Cyber-Security
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223059DOI: 10.1016/j.ifacol.2017.08.1564ISI: 000423964900388Scopus ID: 2-s2.0-85031794404OAI: oai:DiVA.org:kth-223059DiVA, id: diva2:1182464
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Funder
Knut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research Swedish Research Council
Note

QC 20180213

Available from: 2018-02-13 Created: 2018-02-13 Last updated: 2018-03-14Bibliographically approved

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Milosevic, JezdimirTanaka, TakashiSandberg, Henrik

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