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

Sze Zheng Yong; Ming Qing Foo; Emilio Frazzoli

doi:10.1109/ACC.2016.7524933

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

Sze Zheng Yong, Ming Qing Foo, Emilio Frazzoli

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

33 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 language	English (US)
Title of host publication	2016 American Control Conference, ACC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	308-315
Number of pages	8
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7524933
State	Published - Jul 28 2016
Externally published	Yes
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Other

Other	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	7/6/16 → 7/8/16

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2016.7524933

Cite this

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

Robust and resilient estimation for Cyber-Physical Systems under adversarial attacks. / Yong, Sze Zheng; Foo, Ming Qing; Frazzoli, Emilio.
2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 308-315 7524933 (Proceedings of the American Control Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yong, SZ, Foo, MQ & Frazzoli, E 2016, Robust and resilient estimation for Cyber-Physical Systems under adversarial attacks. in 2016 American Control Conference, ACC 2016., 7524933, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 308-315, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7524933

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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.",

author = "Yong, {Sze Zheng} and Foo, {Ming Qing} and Emilio Frazzoli",

note = "Funding Information: ACKNOWLEDGMENTS: The work presented in this paper was supported by the National Science Foundation, grant #1239182. Publisher Copyright: {\textcopyright} 2016 American Automatic Control Council (AACC).; 2016 American Control Conference, ACC 2016 ; Conference date: 06-07-2016 Through 08-07-2016",

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AB - 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.

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

Abstract

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