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The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Author

Listed:
  • Mohamad El Hariri

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

  • Eric Harmon

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

  • Tarek Youssef

    (Department of Electrical and Computer Engineering, University of West Florida, Pensacola, Fl 32514, USA)

  • Mahmoud Saleh

    (Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL 33805, USA)

  • Hany Habib

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

  • Osama Mohammed

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

Abstract

The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3731-:d:272153
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    Citations

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    Cited by:

    1. Junho Hong & Tai-Jin Song & Hyojong Lee & Aydin Zaboli, 2022. "Automated Cybersecurity Tester for IEC61850-Based Digital Substations," Energies, MDPI, vol. 15(21), pages 1-17, October.
    2. Tarazi, Hussam & Sutton, Sara & Olinjyk, John & Bond, Benjamin & Rrushi, Julian, 2024. "A watchdog model for physics-based anomaly detection in digital substations," International Journal of Critical Infrastructure Protection, Elsevier, vol. 44(C).
    3. Abrar Mahi-al-rashid & Fahmid Hossain & Adnan Anwar & Sami Azam, 2022. "False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting," Energies, MDPI, vol. 15(13), pages 1-17, July.
    4. Christos-Minas Mathas & Costas Vassilakis & Nicholas Kolokotronis & Charilaos C. Zarakovitis & Michail-Alexandros Kourtis, 2021. "On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids," Energies, MDPI, vol. 14(10), pages 1-27, May.
    5. 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.

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