IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v6y2025i1d10.1007_s43069-025-00414-3.html
   My bibliography  Save this article

Modeling and Mitigating Billing Attacks in Scalable Smart Grids with Distributed and Intelligent Systems

Author

Listed:
  • Abdelfattah Abassi

    (Mouly Ismail University)

  • Mostapha El Jai

    (Euromed University of Fes (UEMF)
    ENSAM-Meknes, Mouly Ismail University)

  • Ahmed Arid

    (Mouly Ismail University)

  • Hussain Benazza

    (Mouly Ismail University
    Moulay Ismail University)

Abstract

With the expansion of smart grids, maintenance and control costs have grown significantly. For this reason, some innovative strategies have been implemented to strengthen the control within the grid. Among these strategies, the implementation of distributed models coupled to intelligent billing systems is of high interest; it aims at achieving optimality in the system while maintaining billing fairness. Despite the advantages offered by smart grid’s paradigm, it still shows weaknesses in terms of theft and users’ fraudulence behavior detections. That is to say, the present paper focusses on three types of attacks, mainly existing in the literature, that malicious users can exploit to minimize their personal bills along with the development of related technique to troubleshot and solve such issues. Simulations and mathematical models are proposed to model these attacks, along with a mitigation strategy that has been developed.

Suggested Citation

  • Abdelfattah Abassi & Mostapha El Jai & Ahmed Arid & Hussain Benazza, 2025. "Modeling and Mitigating Billing Attacks in Scalable Smart Grids with Distributed and Intelligent Systems," SN Operations Research Forum, Springer, vol. 6(1), pages 1-34, March.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-025-00414-3
    DOI: 10.1007/s43069-025-00414-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-025-00414-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-025-00414-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Gustavo Pacheco Epifanio & José Federico Vizcaíno González & Fábio Luiz Usberti & Luís Tarrataca & Laura Silva Assis, 2023. "Switch Allocation Problem in Power Distribution Systems with Distributed Generation," SN Operations Research Forum, Springer, vol. 4(3), pages 1-24, September.
    3. Mohammed Zakariae El Khattabi & Mostapha El Jai & Youssef Lahmadi & Lahcen Oughdir, 2024. "Geometry-Inference Based Clustering Heuristic: New k-means Metric for Gaussian Data and Experimental Proof of Concept," SN Operations Research Forum, Springer, vol. 5(1), pages 1-26, March.
    4. Korhonen, Pekka & Moskowitz, Herbert & Wallenius, Jyrki, 1992. "Multiple criteria decision support - A review," European Journal of Operational Research, Elsevier, vol. 63(3), pages 361-375, December.
    5. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
    6. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
    Full references (including those not matched with items on IDEAS)

    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. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    2. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    3. Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Malin Lachmann & Jaime Maldonado & Wiebke Bergmann & Francesca Jung & Markus Weber & Christof Büskens, 2020. "Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System," Energies, MDPI, vol. 13(8), pages 1-42, April.
    5. Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, vol. 11(3), pages 1-17, February.
    6. Többen, Johannes & Schröder, Thomas, 2018. "A maximum entropy approach to the estimation of spatially and sectorally disaggregated electricity load curves," Applied Energy, Elsevier, vol. 225(C), pages 797-813.
    7. Imam, M. & Jamasb, T. & Llorca, M. & Llorca, M., 2018. "Power Sector Reform and Corruption: Evidence from Electricity Industry in Sub-Saharan Africa," Cambridge Working Papers in Economics 1801, Faculty of Economics, University of Cambridge.
    8. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    9. Daví-Arderius, Daniel & Sanin, María-Eugenia & Trujillo-Baute, Elisa, 2017. "CO2 content of electricity losses," Energy Policy, Elsevier, vol. 104(C), pages 439-445.
    10. Costa-Campi, Maria Teresa & Daví-Arderius, Daniel & Trujillo-Baute, Elisa, 2018. "The economic impact of electricity losses," Energy Economics, Elsevier, vol. 75(C), pages 309-322.
    11. J. Granat & M. Makowski, 1998. "ISAAP - Interactive Specification and Analysis of Aspiration-Based Preferences," Working Papers ir98052, International Institute for Applied Systems Analysis.
    12. Netzah Calamaro & Yuval Beck & Ran Ben Melech & Doron Shmilovitz, 2021. "An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment," Sustainability, MDPI, vol. 13(19), pages 1-38, September.
    13. Haapalinna, Ilkka, 2003. "How to allocate funds within the army," European Journal of Operational Research, Elsevier, vol. 144(1), pages 224-233, January.
    14. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    15. Min, Brian & Golden, Miriam, 2014. "Electoral cycles in electricity losses in India," Energy Policy, Elsevier, vol. 65(C), pages 619-625.
    16. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
    17. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
    18. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    19. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    20. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-025-00414-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.