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Optimal Stealthy Attack under KL Divergence and Countermeasure with Randomized Threshold
Hong Kong Univ Sci & Technol, Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China..
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.ORCID iD: 0000-0003-0762-5743
Hong Kong Univ Sci & Technol, Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China..
2017 (English)In: IFAC-PapersOnLine, Elsevier, 2017, Vol. 50, no 1, p. 9496-9501Conference paper, Published paper (Refereed)
Abstract [en]

In a cyber-physical system, there are potential sources of malicious attacks that can damage the estimation quality in an underlying network control system. The attacker aims to maximize these damages while the estimator attempts to minimize them. In this paper we define an attack's stealth based on the KL divergence and obtain an optimal attack. Furthermore, we suggest one method in which the estimator may limit the damage to the system while imposing on any attack a probability for it to be non-stealthy. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 9496-9501
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 50:1
Keywords [en]
Cyber-Physical Systems, Detection, Security
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-350711DOI: 10.1016/j.ifacol.2017.08.1587ISI: 000423965100081OAI: oai:DiVA.org:uu-350711DiVA, id: diva2:1206042
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC), JUL 09-14, 2017, Toulouse, FRANCE
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

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Dey, Subhrakanti

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