Detection of False Data Injection Attack in Smart Grids via Interval Observer

Xinyu Wang; Xiaoyuan Luo; Mingyue Zhang; Zhongping Jiang; Xinping Guan

doi:10.1109/CCDC.2019.8832350

Detection of False Data Injection Attack in Smart Grids via Interval Observer

Xinyu Wang, Xiaoyuan Luo, Mingyue Zhang, Zhongping Jiang, Xinping Guan

Electrical and Computer Engineering

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

Abstract

This paper focuses on the detection of false data inject attacks (FDIAs) in smart grids. An interval observer-based detection scheme against the FDIAs is proposed, by considering the stealthy characteristics of FDIAs. Based on the constructed physical dynamics grid model, we design interval observer to estimate the interval state of physical dynamics accurately. To address the limitation of precomputed threshold, an interval residual-based detection standard is proposed. The residual evaluation functions and detection threshold in traditional attack detection methodologies are replaced by the interval residuals which can be regarded as the time-varying detection threshold. Finally, the effectiveness of the developed detection method is demonstrated by using a detailed study on the IEEE 37-bus smart grid system.

Original language	English (US)
Title of host publication	Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3238-3243
Number of pages	6
ISBN (Electronic)	9781728101057
DOIs	https://doi.org/10.1109/CCDC.2019.8832350
State	Published - Jun 2019
Event	31st Chinese Control and Decision Conference, CCDC 2019 - Nanchang, China Duration: Jun 3 2019 → Jun 5 2019

Publication series

Name	Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019

Conference

Conference	31st Chinese Control and Decision Conference, CCDC 2019
Country/Territory	China
City	Nanchang
Period	6/3/19 → 6/5/19

Keywords

Smart grid
attack detection
false data inject attack
nonlinear interval observer

ASJC Scopus subject areas

General Decision Sciences
Control and Systems Engineering
Control and Optimization

Access to Document

10.1109/CCDC.2019.8832350

Cite this

Wang, X., Luo, X., Zhang, M., Jiang, Z., & Guan, X. (2019). Detection of False Data Injection Attack in Smart Grids via Interval Observer. In Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019 (pp. 3238-3243). Article 8832350 (Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC.2019.8832350

Detection of False Data Injection Attack in Smart Grids via Interval Observer. / Wang, Xinyu; Luo, Xiaoyuan; Zhang, Mingyue et al.
Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3238-3243 8832350 (Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019).

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

Wang, X, Luo, X, Zhang, M, Jiang, Z & Guan, X 2019, Detection of False Data Injection Attack in Smart Grids via Interval Observer. in Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019., 8832350, Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 3238-3243, 31st Chinese Control and Decision Conference, CCDC 2019, Nanchang, China, 6/3/19. https://doi.org/10.1109/CCDC.2019.8832350

Wang X, Luo X, Zhang M, Jiang Z, Guan X. Detection of False Data Injection Attack in Smart Grids via Interval Observer. In Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3238-3243. 8832350. (Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019). doi: 10.1109/CCDC.2019.8832350

@inproceedings{d0c27814431f4635a9cdc0480fd52051,

title = "Detection of False Data Injection Attack in Smart Grids via Interval Observer",

abstract = "This paper focuses on the detection of false data inject attacks (FDIAs) in smart grids. An interval observer-based detection scheme against the FDIAs is proposed, by considering the stealthy characteristics of FDIAs. Based on the constructed physical dynamics grid model, we design interval observer to estimate the interval state of physical dynamics accurately. To address the limitation of precomputed threshold, an interval residual-based detection standard is proposed. The residual evaluation functions and detection threshold in traditional attack detection methodologies are replaced by the interval residuals which can be regarded as the time-varying detection threshold. Finally, the effectiveness of the developed detection method is demonstrated by using a detailed study on the IEEE 37-bus smart grid system.",

keywords = "Smart grid, attack detection, false data inject attack, nonlinear interval observer",

author = "Xinyu Wang and Xiaoyuan Luo and Mingyue Zhang and Zhongping Jiang and Xinping Guan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 31st Chinese Control and Decision Conference, CCDC 2019 ; Conference date: 03-06-2019 Through 05-06-2019",

year = "2019",

month = jun,

doi = "10.1109/CCDC.2019.8832350",

language = "English (US)",

series = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",

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

pages = "3238--3243",

booktitle = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",

}

TY - GEN

T1 - Detection of False Data Injection Attack in Smart Grids via Interval Observer

AU - Wang, Xinyu

AU - Luo, Xiaoyuan

AU - Zhang, Mingyue

AU - Jiang, Zhongping

AU - Guan, Xinping

PY - 2019/6

Y1 - 2019/6

N2 - This paper focuses on the detection of false data inject attacks (FDIAs) in smart grids. An interval observer-based detection scheme against the FDIAs is proposed, by considering the stealthy characteristics of FDIAs. Based on the constructed physical dynamics grid model, we design interval observer to estimate the interval state of physical dynamics accurately. To address the limitation of precomputed threshold, an interval residual-based detection standard is proposed. The residual evaluation functions and detection threshold in traditional attack detection methodologies are replaced by the interval residuals which can be regarded as the time-varying detection threshold. Finally, the effectiveness of the developed detection method is demonstrated by using a detailed study on the IEEE 37-bus smart grid system.

AB - This paper focuses on the detection of false data inject attacks (FDIAs) in smart grids. An interval observer-based detection scheme against the FDIAs is proposed, by considering the stealthy characteristics of FDIAs. Based on the constructed physical dynamics grid model, we design interval observer to estimate the interval state of physical dynamics accurately. To address the limitation of precomputed threshold, an interval residual-based detection standard is proposed. The residual evaluation functions and detection threshold in traditional attack detection methodologies are replaced by the interval residuals which can be regarded as the time-varying detection threshold. Finally, the effectiveness of the developed detection method is demonstrated by using a detailed study on the IEEE 37-bus smart grid system.

KW - Smart grid

KW - attack detection

KW - false data inject attack

KW - nonlinear interval observer

UR - http://www.scopus.com/inward/record.url?scp=85073098921&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073098921&partnerID=8YFLogxK

U2 - 10.1109/CCDC.2019.8832350

DO - 10.1109/CCDC.2019.8832350

M3 - Conference contribution

T3 - Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019

SP - 3238

EP - 3243

BT - Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st Chinese Control and Decision Conference, CCDC 2019

Y2 - 3 June 2019 through 5 June 2019

ER -

Detection of False Data Injection Attack in Smart Grids via Interval Observer

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this