On the confidentiality of linear anomaly detector states

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2019 American Control Conference, ACC 2019
PublisherIEEE
Pages397-403
ISBN (Electronic)978-1-5386-7926-5
Publication statusPublished - Jul 2019
Externally publishedYes

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Title2019 American Control Conference, ACC 2019
PlaceUnited States
CityPhiladelphia
Period10 - 12 July 2019

Abstract

A malicious attacker with access to the sensor channel in a feedback control system can severely affect the physical system under control, while simultaneously being hard to detect. A properly designed anomaly detector can restrict the impact of such attacks, however. Anomaly detectors with an internal state (stateful detectors) have gained popularity because they seem to be able to mitigate these attacks more than detectors without a state (stateless detectors). In the analysis of attacks against control systems with anomaly detectors, it has been assumed that the attacker has access to the detector's internal state, or designs its attack such that it is not detected regardless of the detector's state. In this paper, we show how an attacker can realize the first case by breaking the confidentiality of a stateful detector state evolving with linear dynamics, while remaining undetected and imitating the statistics of the detector under nominal conditions. The realization of the attack is posed in a convex optimization framework using the notion of Kullback-Leibler divergence. Further, the attack is designed such that the maximum mean estimation error of the Kalman filter is maximized at each time step by exploiting dual norms. A numerical example is given to illustrate the results.

Citation Format(s)

On the confidentiality of linear anomaly detector states. / Umsonst, David; Nekouei, Ehsan; Teixeira, Andre; Sandberg, Henrik.

2019 American Control Conference, ACC 2019. IEEE, 2019. p. 397-403 8814731 (Proceedings of the American Control Conference; Vol. 2019-July).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review