Robust and resilient estimation for Cyber-Physical Systems under adversarial attacks

Sze Yong, Ming Qing Foo, Emilio Frazzoli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

In this paper, we propose a novel state estimation algorithm that is resilient to sparse data injection attacks and robust to additive and multiplicative modeling errors. By leveraging principles of robust optimization, we construct uncertainty sets that lead to tractable optimization solutions. As a corollary, we obtain a novel robust filtering algorithm when there are no attacks, which can be viewed as a frequentist robust estimator as no known priors are assumed. We also describe the use of cross-validation to determine the hyperparameters of our estimator. The effectiveness of our estimator is demonstrated in simulations of an IEEE 14-bus electric power system.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages308-315
Number of pages8
Volume2016-July
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Externally publishedYes
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Yong, S., Foo, M. Q., & Frazzoli, E. (2016). Robust and resilient estimation for Cyber-Physical Systems under adversarial attacks. In 2016 American Control Conference, ACC 2016 (Vol. 2016-July, pp. 308-315). [7524933] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7524933