@article{0f2610e757c04e8093c176e7b3483bd9,
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.",
keywords = "cyber-physical security, false data injection attacks, machine learning, state estimation, phasor measurement units, smart grids",
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.",
year = "2022",
month = apr,
day = "20",
doi = "10.3390/s22093146",
language = "English",
volume = "22",
pages = "e3146",
journal = "Sensors",
issn = "1424-3210",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "9",
}