Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

Ahmed S. Musleh; Mahdi Debouza; Haris M. Khalid; Ahmed Al-Durra

doi:10.1109/IECON.2019.8927453

Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

Ahmed S. Musleh
, Mahdi Debouza
, Haris M. Khalid
, Ahmed Al-Durra

Electrical Engineering

University of New South Wales
Higher Colleges of Technology

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

16 Scopus citations

Abstract

False Data Injection (FDI) is one of the most dangerous attacks on cyber-physical systems as it could lead to disastrous consequences in the operation of the power grids. In this paper, a comprehensive investigation of the (FDI) attacks in smart grids is presented. A detection algorithm is utilized in analyzing the FDI attacks in real-time environment based on Principle Component Analysis (PCA). It provides an adequate solution to the FDI problem for its ability to extract information about correlation of the collected measurements. This provides a more accurate and sensitive response than the previous FDI detection techniques. Furthermore, the light computations associated with this algorithm make it a very good candidate for real-time environment testing. The results concluded in the paper illustrate a very promising future for the PCA-based realtime FDI attack detection schemes.

Original language	British English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
Pages	2958-2963
Number of pages	6
ISBN (Electronic)	9781728148786
DOIs	https://doi.org/10.1109/IECON.2019.8927453
State	Published - Oct 2019
Event	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal Duration: 14 Oct 2019 → 17 Oct 2019

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2019-October

Conference

Conference	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/Territory	Portugal
City	Lisbon
Period	14/10/19 → 17/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

cyber security
Cyber-physical systems
false data injection attack (FDIA)
phasor measurement units (PMU)
principle component analysis (PCA)
real-time implementation
smart grid

Access to Document

10.1109/IECON.2019.8927453

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Musleh, A. S., Debouza, M., Khalid, H. M., & Al-Durra, A. (2019). Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 2958-2963). Article 8927453 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IECON.2019.8927453

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title = "Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis",

abstract = "False Data Injection (FDI) is one of the most dangerous attacks on cyber-physical systems as it could lead to disastrous consequences in the operation of the power grids. In this paper, a comprehensive investigation of the (FDI) attacks in smart grids is presented. A detection algorithm is utilized in analyzing the FDI attacks in real-time environment based on Principle Component Analysis (PCA). It provides an adequate solution to the FDI problem for its ability to extract information about correlation of the collected measurements. This provides a more accurate and sensitive response than the previous FDI detection techniques. Furthermore, the light computations associated with this algorithm make it a very good candidate for real-time environment testing. The results concluded in the paper illustrate a very promising future for the PCA-based realtime FDI attack detection schemes.",

keywords = "cyber security, Cyber-physical systems, false data injection attack (FDIA), phasor measurement units (PMU), principle component analysis (PCA), real-time implementation, smart grid",

author = "Musleh, \{Ahmed S.\} and Mahdi Debouza and Khalid, \{Haris M.\} and Ahmed Al-Durra",

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Musleh, AS, Debouza, M, Khalid, HM & Al-Durra, A 2019, Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis. in Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society., 8927453, IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, IEEE Computer Society, pp. 2958-2963, 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019, Lisbon, Portugal, 14/10/19. https://doi.org/10.1109/IECON.2019.8927453

Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis. / Musleh, Ahmed S.; Debouza, Mahdi; Khalid, Haris M. et al.
Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2019. p. 2958-2963 8927453 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October).

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

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T1 - Detection of False Data Injection Attacks in Smart Grids

T2 - 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019

AU - Musleh, Ahmed S.

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AU - Al-Durra, Ahmed

PY - 2019/10

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N2 - False Data Injection (FDI) is one of the most dangerous attacks on cyber-physical systems as it could lead to disastrous consequences in the operation of the power grids. In this paper, a comprehensive investigation of the (FDI) attacks in smart grids is presented. A detection algorithm is utilized in analyzing the FDI attacks in real-time environment based on Principle Component Analysis (PCA). It provides an adequate solution to the FDI problem for its ability to extract information about correlation of the collected measurements. This provides a more accurate and sensitive response than the previous FDI detection techniques. Furthermore, the light computations associated with this algorithm make it a very good candidate for real-time environment testing. The results concluded in the paper illustrate a very promising future for the PCA-based realtime FDI attack detection schemes.

AB - False Data Injection (FDI) is one of the most dangerous attacks on cyber-physical systems as it could lead to disastrous consequences in the operation of the power grids. In this paper, a comprehensive investigation of the (FDI) attacks in smart grids is presented. A detection algorithm is utilized in analyzing the FDI attacks in real-time environment based on Principle Component Analysis (PCA). It provides an adequate solution to the FDI problem for its ability to extract information about correlation of the collected measurements. This provides a more accurate and sensitive response than the previous FDI detection techniques. Furthermore, the light computations associated with this algorithm make it a very good candidate for real-time environment testing. The results concluded in the paper illustrate a very promising future for the PCA-based realtime FDI attack detection schemes.

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KW - Cyber-physical systems

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KW - phasor measurement units (PMU)

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KW - smart grid

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Musleh AS, Debouza M, Khalid HM, Al-Durra A. Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society. 2019. p. 2958-2963. 8927453. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON.2019.8927453

Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

Abstract

Publication series

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UN SDGs

Keywords

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