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A Comparative Evaluation of Machine Learning Architectures for Detecting Attacks on Smart Meter Data

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Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024) (UCAmI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1212))

Abstract

Companies in the electrical sector are responsible for the production, transport, distribution, and management of thousands or even millions of simultaneous users. Smart meters have improved user consumption monitoring by enabling the automatic collection of data. However, they also increase the risk of data tampering. This paper proposes detection methods for various subtle attack types aimed at reducing apparent consumption and monthly bills through illicit actions, such as altering consumption values before they are sent to the company. The proposal only relies on energy consumption time series (without assuming the availability of personal and socioeconomic data). On this data, a multiclass detector based on XGBoost is compared in performance with two variants of a two-stage detection architecture, which first apply either a classifier or a clustering method (K-Means) to rate the presence of any type of attack, to then perform attack type identification in a second stage. Detection rates exceeding 80% with a low false positive rate were achieved using the two-stage clustering plus classification detector on the subtle collection of attacks proposed.

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Notes

  1. 1.

    “SmartMeter Energy Consumption Data in London Households – London Datastore.” https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households.

  2. 2.

    “Smart meters in London,” Kaggle, May 23, 2022. https://www.kaggle.com/datasets/jeanmidev/smart-meters-in-london?rvi=1.

  3. 3.

    “Low Carbon London - UKPN Innovation,” UKPN Innovation, Oct. 27, 2023. https://innovation.ukpowernetworks.co.uk/projects/low-carbon-london.

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Acknowledgements

Authors acknowledge funding under grant MIA.2021.M04.0008 by the Spanish Ministry of Economic Affairs and Digital Transformation and by EU Next Generation EU/PRTR programme.

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Authors and Affiliations

  1. Information Processing and Telecommunications Center, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, Madrid, Spain

    Carlos J. Gallego, Ana M. Bernardos & José R. Casar

Authors
  1. Carlos J. Gallego
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  2. Ana M. Bernardos
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  3. José R. Casar
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Corresponding author

Correspondence to Ana M. Bernardos .

Editor information

Editors and Affiliations

  1. Castilla-La Mancha University, Ciudad Real, Spain

    José Bravo

  2. Ulster University, Belfast, UK

    Chris Nugent

  3. Ulster University, Belfast, UK

    Ian Cleland

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Gallego, C.J., Bernardos, A.M., Casar, J.R. (2024). A Comparative Evaluation of Machine Learning Architectures for Detecting Attacks on Smart Meter Data. In: Bravo, J., Nugent, C., Cleland, I. (eds) Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024). UCAmI 2024. Lecture Notes in Networks and Systems, vol 1212. Springer, Cham. https://doi.org/10.1007/978-3-031-77571-0_78

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