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Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid

Author

Listed:
  • Matthew Boeding

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Kelly Boswell

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Michael Hempel

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Hamid Sharif

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Juan Lopez

    (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Kalyan Perumalla

    (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

Abstract

The convergence of Information Technologies and Operational Technology systems in industrial networks presents many challenges related to availability, integrity, and confidentiality. In this paper, we evaluate the various cybersecurity risks in industrial control systems and how they may affect these areas of concern, with a particular focus on energy-sector Operational Technology systems. There are multiple threats and countermeasures that Operational Technology and Information Technology systems share. Since Information Technology cybersecurity is a relatively mature field, this paper emphasizes on threats with particular applicability to Operational Technology and their respective countermeasures. We identify regulations, standards, frameworks and typical system architectures associated with this domain. We review relevant challenges, threats, and countermeasures, as well as critical differences in priorities between Information and Operational Technology cybersecurity efforts and implications. These results are then examined against the recommended National Institute of Standards and Technology framework for gap analysis to provide a complete approach to energy sector cybersecurity. We provide analysis of countermeasure implementation to align with the continuous functions recommended for a sound cybersecurity framework.

Suggested Citation

  • Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8692-:d:977686
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    References listed on IDEAS

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    1. Kyung Choi & Xinyi Chen & Shi Li & Mihui Kim & Kijoon Chae & JungChan Na, 2012. "Intrusion Detection of NSM Based DoS Attacks Using Data Mining in Smart Grid," Energies, MDPI, vol. 5(10), pages 1-19, October.
    2. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    3. Villar-Rodriguez, Esther & Del Ser, Javier & Oregi, Izaskun & Bilbao, Miren Nekane & Gil-Lopez, Sergio, 2017. "Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis," Energy, Elsevier, vol. 137(C), pages 118-128.
    4. Mohamad El Hariri & Eric Harmon & Tarek Youssef & Mahmoud Saleh & Hany Habib & Osama Mohammed, 2019. "The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets," Energies, MDPI, vol. 12(19), pages 1-24, September.
    5. Shahid Tufail & Imtiaz Parvez & Shanzeh Batool & Arif Sarwat, 2021. "A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid," Energies, MDPI, vol. 14(18), pages 1-22, September.
    6. Saad, Ahmed A. & Faddel, Samy & Mohammed, Osama, 2019. "A secured distributed control system for future interconnected smart grids," Applied Energy, Elsevier, vol. 243(C), pages 57-70.
    7. Skodvin, Tora, 2010. ""Pivotal politics" in US energy and climate legislation," Energy Policy, Elsevier, vol. 38(8), pages 4214-4223, August.
    8. Razavi, Rouzbeh & Gharipour, Amin & Fleury, Martin & Akpan, Ikpe Justice, 2019. "A practical feature-engineering framework for electricity theft detection in smart grids," Applied Energy, Elsevier, vol. 238(C), pages 481-494.
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    Cited by:

    1. 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.
    2. Boeding, Matthew & Hempel, Michael & Sharif, Hamid & Lopez, Juan & Perumalla, Kalyan, 2023. "A flexible OT testbed for evaluating on-device implementations of IEC-61850 GOOSE," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).

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