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Annals of Telecommunications

Advanced data-driven anomalies detection and diagnosis for cyber-physical energy systems

  • 21-10-2025
Published in:
Authors
Hassan N. Noura
Zaid Allal
Ola Salman
Ali Chehab
Published in
Annals of Telecommunications | Issue 11-12/2025

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Abstract

This article delves into the critical need for advanced anomaly detection and diagnosis in cyber-physical energy systems (CPES), driven by the increasing integration of renewable energy sources. It highlights the shift from centralized to decentralized energy systems and the subsequent challenges in maintaining system reliability and security. The article presents a novel two-tier approach using tree-based machine learning models, achieving exceptional performance in both anomaly detection and diagnosis. Key topics include the importance of feature analysis, the effectiveness of tree-based models, and the practical implementation of the proposed framework in a smart grid scenario. The results demonstrate the robustness and efficiency of the approach, setting a new benchmark for anomaly detection in CPES.
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Title
Advanced data-driven anomalies detection and diagnosis for cyber-physical energy systems
Authors
Hassan N. Noura
Zaid Allal
Ola Salman
Ali Chehab
Publication date
21-10-2025
Publisher
Springer International Publishing
Published in
Annals of Telecommunications / Issue 11-12/2025
Print ISSN: 0003-4347
Electronic ISSN: 1958-9395
DOI
https://doi.org/10.1007/s12243-025-01123-y
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