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Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

1
Department of Applied Sciences, University of Quebec in Chicoutimi (UQAC), Chicoutimi, QC G7H 2B1, Canada
2
College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
*
Author to whom correspondence should be addressed.
Academic Editor: J. C. Hernandez
Sustainability 2021, 13(6), 3196; https://doi.org/10.3390/su13063196
Received: 21 December 2020 / Revised: 17 February 2021 / Accepted: 3 March 2021 / Published: 15 March 2021
(This article belongs to the Special Issue Big Data in a Sustainable Smart City)
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented. View Full-Text
Keywords: smart grid; cybersecurity; machine learning; optimization; deep learning; cybersecurity risks; automated distribution network smart grid; cybersecurity; machine learning; optimization; deep learning; cybersecurity risks; automated distribution network
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MDPI and ACS Style

Chehri, A.; Fofana, I.; Yang, X. Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence. Sustainability 2021, 13, 3196. https://doi.org/10.3390/su13063196

AMA Style

Chehri A, Fofana I, Yang X. Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence. Sustainability. 2021; 13(6):3196. https://doi.org/10.3390/su13063196

Chicago/Turabian Style

Chehri, Abdellah, Issouf Fofana, and Xiaomin Yang. 2021. "Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence" Sustainability 13, no. 6: 3196. https://doi.org/10.3390/su13063196

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