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Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems

Author

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  • Mahmoud M. Badr

    (Department of Network and Computer Security, College of Engineering, SUNY Polytechnic Institute, Utica, NY 13502, USA
    Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt)

  • Mohamed I. Ibrahem

    (Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
    Department of Cyber Security Engineering, George Mason University, Fairfax, VA 22030, USA)

  • Hisham A. Kholidy

    (Department of Network and Computer Security, College of Engineering, SUNY Polytechnic Institute, Utica, NY 13502, USA)

  • Mostafa M. Fouda

    (Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
    Center for Advanced Energy Studies (CAES), Idaho Falls, ID 83401, USA)

  • Muhammad Ismail

    (Department of Computer Science, Tennessee Technological University, Cookeville, TN 38501, USA)

Abstract

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid’s stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers’ privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers’ activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection.

Suggested Citation

  • Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2852-:d:1101587
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    References listed on IDEAS

    as
    1. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
    2. Hanem I. Hegazy & Adly S. Tag Eldien & Mohsen M. Tantawy & Mostafa M. Fouda & Heba A. TagElDien, 2022. "Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach," Energies, MDPI, vol. 15(14), pages 1-18, July.
    3. Arul Rajagopalan & Dhivya Swaminathan & Meshal Alharbi & Sudhakar Sengan & Oscar Danilo Montoya & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly, 2022. "Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods," Energies, MDPI, vol. 15(23), pages 1-18, November.
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    Cited by:

    1. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    2. Soohyun Kim & Youngghyu Sun & Seongwoo Lee & Joonho Seon & Byungsun Hwang & Jeongho Kim & Jinwook Kim & Kyounghun Kim & Jinyoung Kim, 2024. "Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review," Energies, MDPI, vol. 17(12), pages 1-23, June.

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