Detection of biasing attacks on distributed estimation networks

Mohammad Deghat; Valery Ugrinovskii; Iman Shames; Cédric Langbort

doi:10.1109/CDC.2016.7798579

Detection of biasing attacks on distributed estimation networks

Mohammad Deghat, Valery Ugrinovskii, Iman Shames, Cédric Langbort

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

11 Citations (Scopus)

Abstract

The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an H_∞ approach.

Original language	English
Title of host publication	2016 IEEE 55th Conference on Decision and Control, CDC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2134-2139
Number of pages	6
ISBN (Electronic)	9781509018376
DOIs	https://doi.org/10.1109/CDC.2016.7798579
Publication status	Published - 27 Dec 2016
Externally published	Yes
Event	55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016

Publication series

Name	2016 IEEE 55th Conference on Decision and Control, CDC 2016

Conference

Conference	55th IEEE Conference on Decision and Control, CDC 2016
Country/Territory	United States
City	Las Vegas
Period	12/12/16 → 14/12/16

Access to Document

10.1109/CDC.2016.7798579

Cite this

Deghat, M., Ugrinovskii, V., Shames, I., & Langbort, C. (2016). Detection of biasing attacks on distributed estimation networks. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 2134-2139). Article 7798579 (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798579

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title = "Detection of biasing attacks on distributed estimation networks",

abstract = "The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an H∞ approach.",

author = "Mohammad Deghat and Valery Ugrinovskii and Iman Shames and C{\'e}dric Langbort",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 55th IEEE Conference on Decision and Control, CDC 2016 ; Conference date: 12-12-2016 Through 14-12-2016",

year = "2016",

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doi = "10.1109/CDC.2016.7798579",

language = "English",

series = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Deghat, M, Ugrinovskii, V, Shames, I & Langbort, C 2016, Detection of biasing attacks on distributed estimation networks. in 2016 IEEE 55th Conference on Decision and Control, CDC 2016., 7798579, 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 2134-2139, 55th IEEE Conference on Decision and Control, CDC 2016, Las Vegas, United States, 12/12/16. https://doi.org/10.1109/CDC.2016.7798579

Detection of biasing attacks on distributed estimation networks. / Deghat, Mohammad; Ugrinovskii, Valery; Shames, Iman et al.
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2134-2139 7798579 (2016 IEEE 55th Conference on Decision and Control, CDC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

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AU - Deghat, Mohammad

AU - Ugrinovskii, Valery

AU - Shames, Iman

AU - Langbort, Cédric

PY - 2016/12/27

Y1 - 2016/12/27

N2 - The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an H∞ approach.

AB - The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an H∞ approach.

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DO - 10.1109/CDC.2016.7798579

M3 - Conference Paper

T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016

SP - 2134

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BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 55th IEEE Conference on Decision and Control, CDC 2016

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ER -

Detection of biasing attacks on distributed estimation networks

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