False Data Injection Attack Detection for Control Systems Based on Correlation Analysis

Xixing Xue; Dong Zhao

doi:10.1109/IECON51785.2023.10311978

False Data Injection Attack Detection for Control Systems Based on Correlation Analysis

Xixing Xue
, Dong Zhao

School of Cyber Science and Technology

Beihang University

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

Abstract

The correlation of control system data is characterized by its intrinsic dynamics, which is pretty hard to forge by the attacker. In this paper, false data injection detection problem for linear time-invariant systems is studied from a perspective of correlation analysis. Two detection methods are proposed based on targeted data correction construction and analysis. First, a noise encryption-based correlation enhancement mechanism and the optimization-based attack detection method are proposed. Second, a coding-based data correlation construction mechanism is designed and analyzed, and the corresponding detection scheme is proposed. The effectiveness and performance are illustrated by simulation. The proposed correlation-based detection schemes require no control performance sacrifice and can be implemented easily.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10311978
State	Published - 2023
Event	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/Territory	Singapore
City	Singapore
Period	16/10/23 → 19/10/23

Keywords

attack detection
correlation analysis
cyber-physical systems
false data injection attack
state estimation

Access to Document

10.1109/IECON51785.2023.10311978

Cite this

@inproceedings{cba492c3a9804d699096771217c94094,

title = "False Data Injection Attack Detection for Control Systems Based on Correlation Analysis",

abstract = "The correlation of control system data is characterized by its intrinsic dynamics, which is pretty hard to forge by the attacker. In this paper, false data injection detection problem for linear time-invariant systems is studied from a perspective of correlation analysis. Two detection methods are proposed based on targeted data correction construction and analysis. First, a noise encryption-based correlation enhancement mechanism and the optimization-based attack detection method are proposed. Second, a coding-based data correlation construction mechanism is designed and analyzed, and the corresponding detection scheme is proposed. The effectiveness and performance are illustrated by simulation. The proposed correlation-based detection schemes require no control performance sacrifice and can be implemented easily.",

keywords = "attack detection, correlation analysis, cyber-physical systems, false data injection attack, state estimation",

author = "Xixing Xue and Dong Zhao",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10311978",

language = "英语",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

address = "美国",

}

Xue, X & Zhao, D 2023, False Data Injection Attack Detection for Control Systems Based on Correlation Analysis. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, 16/10/23. https://doi.org/10.1109/IECON51785.2023.10311978

False Data Injection Attack Detection for Control Systems Based on Correlation Analysis. / Xue, Xixing; Zhao, Dong.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

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

TY - GEN

T1 - False Data Injection Attack Detection for Control Systems Based on Correlation Analysis

AU - Xue, Xixing

AU - Zhao, Dong

PY - 2023

Y1 - 2023

N2 - The correlation of control system data is characterized by its intrinsic dynamics, which is pretty hard to forge by the attacker. In this paper, false data injection detection problem for linear time-invariant systems is studied from a perspective of correlation analysis. Two detection methods are proposed based on targeted data correction construction and analysis. First, a noise encryption-based correlation enhancement mechanism and the optimization-based attack detection method are proposed. Second, a coding-based data correlation construction mechanism is designed and analyzed, and the corresponding detection scheme is proposed. The effectiveness and performance are illustrated by simulation. The proposed correlation-based detection schemes require no control performance sacrifice and can be implemented easily.

AB - The correlation of control system data is characterized by its intrinsic dynamics, which is pretty hard to forge by the attacker. In this paper, false data injection detection problem for linear time-invariant systems is studied from a perspective of correlation analysis. Two detection methods are proposed based on targeted data correction construction and analysis. First, a noise encryption-based correlation enhancement mechanism and the optimization-based attack detection method are proposed. Second, a coding-based data correlation construction mechanism is designed and analyzed, and the corresponding detection scheme is proposed. The effectiveness and performance are illustrated by simulation. The proposed correlation-based detection schemes require no control performance sacrifice and can be implemented easily.

KW - attack detection

KW - correlation analysis

KW - cyber-physical systems

KW - false data injection attack

KW - state estimation

UR - https://www.scopus.com/pages/publications/85179503628

U2 - 10.1109/IECON51785.2023.10311978

DO - 10.1109/IECON51785.2023.10311978

M3 - 会议稿件

AN - SCOPUS:85179503628

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

Y2 - 16 October 2023 through 19 October 2023

ER -

False Data Injection Attack Detection for Control Systems Based on Correlation Analysis

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Keywords

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