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Active Model Discrimination with Applications to Fraud Detection in Smart Buildings
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 9527-9534Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider the problem of active model discrimination amongst a finite number of affine models with uncontrolled and noise inputs, each representing a different system operating mode that corresponds to a fault type or an attack strategy, or to an unobserved intent of another robot, etc. The active model discrimination problem aims to find optimal separating inputs that guarantee that the outputs of all the affine models cannot be identical over a finite horizon. This will enable a system operator to detect and uniquely identify potential faults or attacks, despite the presence of process and measurement noise. Since the resulting model discrimination problem is a nonlinear non-convex mixed-integer program, we propose to solve this in a computationally tractable manner, albeit only approximately, by proposing a sequence of restrictions that guarantee that the obtained input is separating. Finally, we apply our approach to attack detection in the area of cyber-physical systems security.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 9527-9534
Keyword [en]
attack detection, Fault, Input design, Model discrimination, Smart building
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223056DOI: 10.1016/j.ifacol.2017.08.1616ISI: 000423965100086Scopus ID: 2-s2.0-85031790626OAI: oai:DiVA.org:kth-223056DiVA, id: diva2:1182987
Note

QC 20180215

Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-03-05Bibliographically approved

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Jacobsen, Emil

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