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

Author

Listed:
  • Abdellah Chehri

    (Department of Applied Sciences, University of Quebec in Chicoutimi (UQAC), Chicoutimi, QC G7H 2B1, Canada)

  • Issouf Fofana

    (Department of Applied Sciences, University of Quebec in Chicoutimi (UQAC), Chicoutimi, QC G7H 2B1, Canada)

  • Xiaomin Yang

    (College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China)

Abstract

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.

Suggested Citation

  • Abdellah Chehri & Issouf Fofana & Xiaomin Yang, 2021. "Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3196-:d:517023
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    References listed on IDEAS

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    1. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Smart meters for power grid: Challenges, issues, advantages and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2736-2742, August.
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    1. Mehrdad Aslani & Hamed Hashemi-Dezaki & Abbas Ketabi, 2021. "Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios," Sustainability, MDPI, vol. 13(10), pages 1-30, May.
    2. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    3. Jiadi Yang & Jinjin Wang, 2022. "TV program innovation and teaching under big data background in all media era," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1031-1041, December.
    4. Qiang Wang & Dong Yu & Jinyu Zhou & Chaowu Jin, 2023. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    5. Anna Kwiotkowska & Bożena Gajdzik & Radosław Wolniak & Jolita Vveinhardt & Magdalena Gębczyńska, 2021. "Leadership Competencies in Making Industry 4.0 Effective: The Case of Polish Heat and Power Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    6. Sumeet Sahay & Hemant Kumar Kaushik & Shikha Singh, 2023. "Discovering themes and trends in electricity supply chain area research," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1525-1560, September.
    7. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    8. Tomáš Loveček & Lenka Straková & Katarína Kampová, 2021. "Modeling and Simulation as Tools to Increase the Protection of Critical Infrastructure and the Sustainability of the Provision of Essential Needs of Citizens," Sustainability, MDPI, vol. 13(11), pages 1-18, May.
    9. Aparna Kumari & Rushil Kaushikkumar Patel & Urvi Chintukumar Sukharamwala & Sudeep Tanwar & Maria Simona Raboaca & Aldosary Saad & Amr Tolba, 2022. "AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    10. Philippe Funk, 2022. "Artificial Intelligence And Cybersecurity Implications For Business Management," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 16(1), pages 252-261.

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