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A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid

Author

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  • Shahid Tufail

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Imtiaz Parvez

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Shanzeh Batool

    (School of Computer Science and Engineering, Vellore Institute of Technology, Sehore 466114, India)

  • Arif Sarwat

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

Abstract

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at three different levels. In addition to that, we suggest techniques to minimize the chances of cyberattack at all three levels.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5894-:d:637568
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    References listed on IDEAS

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    1. A. Reeves & P. Delfabbro & D. Calic, 2021. "Encouraging Employee Engagement With Cybersecurity: How to Tackle Cyber Fatigue," SAGE Open, , vol. 11(1), pages 21582440211, March.
    2. Zhang, Hongwei & Wang, Jinsong & Ding, Yuemin, 2019. "Blockchain-based decentralized and secure keyless signature scheme for smart grid," Energy, Elsevier, vol. 180(C), pages 955-967.
    3. Kimani, Kenneth & Oduol, Vitalice & Langat, Kibet, 2019. "Cyber security challenges for IoT-based smart grid networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 36-49.
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    Cited by:

    1. Berghout, Tarek & Benbouzid, Mohamed & Muyeen, S.M., 2022. "Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    2. Maciej Sawka & Marcin Niemiec, 2022. "A Sponge-Based Key Expansion Scheme for Modern Block Ciphers," Energies, MDPI, vol. 15(19), pages 1-18, September.
    3. 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.
    4. Mikołaj Gwiazdowicz & Marek Natkaniec, 2023. "Feature Selection and Model Evaluation for Threat Detection in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-25, June.
    5. Smitha Joyce Pinto & Pierluigi Siano & Mimmo Parente, 2023. "Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection," Energies, MDPI, vol. 16(4), pages 1-24, February.
    6. Wojciech Szczepanik & Marcin Niemiec, 2022. "Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    7. Jianguo Ding & Attia Qammar & Zhimin Zhang & Ahmad Karim & Huansheng Ning, 2022. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions," Energies, MDPI, vol. 15(18), pages 1-37, September.
    8. Seppo Borenius & Pavithra Gopalakrishnan & Lina Bertling Tjernberg & Raimo Kantola, 2022. "Expert-Guided Security Risk Assessment of Evolving Power Grids," Energies, MDPI, vol. 15(9), pages 1-25, April.
    9. 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.
    10. Berghout, Tarek & Benbouzid, Mohamed, 2022. "EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.

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