IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i6p3196-d517023.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/6/3196/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/6/3196/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    2. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    3. Haidar, Ahmed M.A. & Muttaqi, Kashem & Sutanto, Danny, 2015. "Smart Grid and its future perspectives in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1375-1389.
    4. Yanshan Yu & Jin Yang & Bin Chen, 2012. "The Smart Grids in China—A Review," Energies, MDPI, vol. 5(5), pages 1-18, May.
    5. Emad Ebeid & Rune Heick & Rune Hylsberg Jacobsen, 2017. "Deducing Energy Consumer Behavior from Smart Meter Data," Future Internet, MDPI, vol. 9(3), pages 1-25, July.
    6. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    7. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    8. K. Habibul Kabir & Shafquat Yasar Aurko & Md. Saifur Rahman, 2021. "Smart Power Management in OIC Countries: A Critical Overview Using SWOT-AHP and Hybrid MCDM Analysis," Energies, MDPI, vol. 14(20), pages 1-50, October.
    9. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "The Effect of Renewable Energy Development on Carbon Emission Reduction: An Empirical Analysis for the EU-15 Countries," IZA Discussion Papers 7989, Institute of Labor Economics (IZA).
    10. van de Kaa, G. & Fens, T. & Rezaei, J. & Kaynak, D. & Hatun, Z. & Tsilimeni-Archangelidi, A., 2019. "Realizing smart meter connectivity: Analyzing the competing technologies Power line communication, mobile telephony, and radio frequency using the best worst method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 320-327.
    11. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
    12. Pereira, Guillermo Ivan & Specht, Jan Martin & Silva, Patrícia Pereira & Madlener, Reinhard, 2018. "Technology, business model, and market design adaptation toward smart electricity distribution: Insights for policy making," Energy Policy, Elsevier, vol. 121(C), pages 426-440.
    13. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    14. Erlinghagen, Sabine & Lichtensteiger, Bill & Markard, Jochen, 2015. "Smart meter communication standards in Europe – a comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1249-1262.
    15. Wang, Yong & Li, Lin, 2016. "Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies," Applied Energy, Elsevier, vol. 175(C), pages 40-53.
    16. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    17. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    18. Muhammad Salman Saeed & Mohd Wazir Mustafa & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Ilyas Khan & Samer Atawneh & Nawaf N. Hamadneh, 2020. "An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities," Energies, MDPI, vol. 13(12), pages 1-19, June.
    19. Rezaeimozafar, Mostafa & Monaghan, Rory F.D. & Barrett, Enda & Duffy, Maeve, 2022. "A review of behind-the-meter energy storage systems in smart grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    20. Aslam, Waleed & Soban, Muhammad & Akhtar, Farwa & Zaffar, Nauman A., 2015. "Smart meters for industrial energy conservation and efficiency optimization in Pakistan: Scope, technology and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 933-943.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3196-:d:517023. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.