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Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks.

Ali Alshehri1, Mahmoud M Badr2,3, Mohamed Baza4

  • 1Department of Computer Science, University of Tabuk, Tabuk 71491, Saudi Arabia.

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Summary

This study introduces a federated learning (FL) framework for detecting electricity theft. It uses deep anomaly detection to identify energy theft while preserving consumer privacy and detecting new cyber-attacks.

Keywords:
anomaly detectionelectricity theftprivacy preservationsmart citiessmart gridszero-day attacks

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Area of Science:

  • Cybersecurity
  • Energy Systems
  • Machine Learning

Background:

  • Smart power grids face significant financial losses and stability threats due to electricity theft via compromised smart meters (SMs).
  • Existing machine learning (ML) solutions for electricity theft detection often rely on supervised learning, which requires impractical labeled datasets and struggles with novel attack scenarios.
  • Current methods lack robust consumer privacy protection.

Purpose of the Study:

  • To propose a federated learning (FL)-based deep anomaly detection framework for practical, reliable, and privacy-preserving energy theft detection.
  • To address the limitations of supervised learning and the privacy concerns in existing electricity theft detection methods.

Main Methods:

  • Developed a federated learning (FL) framework where consumers train local deep autoencoder-based anomaly detectors on their private electricity usage data.
  • Consumers share only trained detector parameters with an aggregation server to collaboratively build a global anomaly detector.
  • Employed deep anomaly detection techniques to identify deviations from normal electricity consumption patterns.

Main Results:

  • The proposed FL-based anomaly detector significantly outperforms traditional supervised detectors in identifying electricity theft.
  • The framework demonstrates a strong capability to detect zero-day attacks (novel cyberattack scenarios) effectively.
  • Consumer privacy is preserved as only model parameters, not raw data, are shared.

Conclusions:

  • Federated learning offers a viable solution for developing effective and privacy-preserving energy theft detection systems.
  • Deep anomaly detection within an FL framework enhances the detection of sophisticated and previously unseen electricity theft methods.
  • The proposed approach provides a practical and scalable method for utility companies to combat electricity theft while respecting consumer privacy.