False Data Injection Detection for Phasor Measurement Units

Saleh Almasabi; Turki Alsuwian; Muhammad Awais; Muhammad Irfan; Mohammed Jalalah; Belqasem Aljafari; Farid A. Harraz

doi:10.3390/s22093146

False Data Injection Detection for Phasor Measurement Units

Saleh Almasabi, Turki Alsuwian^*, Muhammad Awais, Muhammad Irfan, Mohammed Jalalah, Belqasem Aljafari, Farid A. Harraz

^*Corresponding author for this work

Computer Science

Najran University

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

9 Citations (Scopus)

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Abstract

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.

Original language	English
Article number	3146
Pages (from-to)	e3146
Journal	Sensors
Volume	22
Issue number	9
Early online date	20 Apr 2022
DOIs	https://doi.org/10.3390/s22093146
Publication status	Published - 20 Apr 2022

Keywords

cyber-physical security
false data injection attacks
machine learning
state estimation
phasor measurement units
smart grids

UN SDGs

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

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sensors-22-03146-v2.pdfFinal published version, 771 KBLicence: CC BY

Cite this

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title = "False Data Injection Detection for Phasor Measurement Units",

abstract = "Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.",

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author = "Saleh Almasabi and Turki Alsuwian and Muhammad Awais and Muhammad Irfan and Mohammed Jalalah and Belqasem Aljafari and Harraz, {Farid A.}",

note = "Funding Information: Acknowledgments: The authors would like to acknowledge the support of the Deputyship for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia for this research through a grant (NU/IFC/ENT/01/004) under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Funding Information: Funding: This resarch was supported by the Deputyship for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia. Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Almasabi, Saleh

AU - Alsuwian, Turki

AU - Awais, Muhammad

AU - Irfan, Muhammad

AU - Jalalah, Mohammed

AU - Aljafari, Belqasem

AU - Harraz, Farid A.

N1 - Funding Information: Acknowledgments: The authors would like to acknowledge the support of the Deputyship for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia for this research through a grant (NU/IFC/ENT/01/004) under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Funding Information: Funding: This resarch was supported by the Deputyship for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/4/20

Y1 - 2022/4/20

N2 - Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.

AB - Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.

KW - cyber-physical security

KW - false data injection attacks

KW - machine learning

KW - state estimation

KW - phasor measurement units

KW - smart grids

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JF - Sensors

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ER -

False Data Injection Detection for Phasor Measurement Units

Abstract

Keywords

UN SDGs

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