An optimal hybrid learning approach for attack detection in linear networked control systems

Haifeng Niu; Avimanyu Sahoo; Chandreyee Bhowmick; S. Jagannathan

doi:10.1109/JAS.2019.1911762

An optimal hybrid learning approach for attack detection in linear networked control systems

Haifeng Niu
, Avimanyu Sahoo
, Chandreyee Bhowmick
, S. Jagannathan

Alphabet Inc.
Oklahoma State University
Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	8894751
Pages (from-to)	1404-1416
Number of pages	13
Journal	IEEE/CAA Journal of Automatica Sinica
Volume	6
Issue number	6
DOIs	https://doi.org/10.1109/JAS.2019.1911762
State	Published - Nov 2019
Externally published	Yes

ASJC Scopus Subject Areas

Control and Systems Engineering
Information Systems
Control and Optimization
Artificial Intelligence

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10.1109/JAS.2019.1911762

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title = "An optimal hybrid learning approach for attack detection in linear networked control systems",

author = "Haifeng Niu and Avimanyu Sahoo and Chandreyee Bhowmick and S. Jagannathan",

note = "Publisher Copyright: {\textcopyright} 2014 Chinese Association of Automation.",

year = "2019",

month = nov,

doi = "10.1109/JAS.2019.1911762",

language = "English",

volume = "6",

pages = "1404--1416",

journal = "IEEE/CAA Journal of Automatica Sinica",

issn = "2329-9266",

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T1 - An optimal hybrid learning approach for attack detection in linear networked control systems

AU - Niu, Haifeng

AU - Sahoo, Avimanyu

AU - Bhowmick, Chandreyee

AU - Jagannathan, S.

PY - 2019/11

Y1 - 2019/11

UR - https://www.scopus.com/pages/publications/85074971428

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U2 - 10.1109/JAS.2019.1911762

DO - 10.1109/JAS.2019.1911762

M3 - Article

AN - SCOPUS:85074971428

SN - 2329-9266

VL - 6

SP - 1404

EP - 1416

JO - IEEE/CAA Journal of Automatica Sinica

JF - IEEE/CAA Journal of Automatica Sinica

IS - 6

M1 - 8894751

ER -

An optimal hybrid learning approach for attack detection in linear networked control systems

ASJC Scopus Subject Areas

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