Improving Smart Grid security: Spectral and fractal analysis as tools for detecting cyberattacks
https://doi.org/10.32362/2500-316X-2025-13-1-7-15
EDN: DUNSTG
Abstract
Objectives. Cyberattacks are major potential sources of disturbances in modern electrical networks (Smart Grid). However, distinguishing between the various kinds of harmonic distortions and malicious interventions can be challenging. The objective of this work is to develop an effective tool for detecting and quantifying the differences between harmonic and anomalous signals. This will permit the identification of cyberattacks associated with harmonic signal distortions to provide a more accurate classification of patterns characteristic of malicious impacts.
Methods. A comparative analysis of various anomaly detection methods was conducted, including fractal analysis, multifractal analysis, Shannon entropy calculation, and power spectral density (PSD) analysis.
Results. Harmonic distortions and anomalous signals caused by cyberattacks may share similar fractal and multifractal characteristics, making it harder to distinguish between them. The use of the Shannon entropy method does not fully capture the complexity and uncertainty of harmonic and anomalous signals. To gain a deeper understanding of the nature of these signals, a comprehensive approach was applied, including analysis of their frequency characteristics and the use of other uncertainty assessment methods, such as multifractal analysis and PSD. Use of the PSD method revealed significant differences in energy distribution between these signals, permitting a more accurate identification of cyberattacks.
Conclusions. For the effective detection of cyberattacks associated with harmonic signal distortions in power systems, a comprehensive approach is required, including time series analysis, frequency analysis, and machine learning methods. This approach not only detects anomalies in signals but also provides their quantitative assessment to improve the accuracy of classifying malicious impacts. The integration of these methods enhances the reliability and security of power systems, making them less vulnerable to cyberattacks.
About the Authors
Sergey V. Kochergin,Russian Federation
Sergey V. Kochergin, Cand. Sci. (Eng.), Associate Professor, Information Protection Department, Institute of Cybersecurity and Digital Technologies,
78, Vernadskogo pr., Moscow, 119454.
Competing Interests:
The authors declare no conflicts of interest.
Svetlana V. Artemova
Russian Federation
Svetlana V. Artemova, Dr. Sci. (Eng.), Associate Professor, Head of Information Protection Department, Institute of Cybersecurity and Digital Technologies,
78, Vernadskogo pr., Moscow, 119454.
Scopus AuthorID: 6508256085.
Competing Interests:
The authors declare no conflicts of interest.
Anatoly A. Bakaev
Russian Federation
Anatoly A. Bakaev, Dr. Sci. (Hist.), Cand. Sci. (Juri.), Associate Professor, Director of the Institute of Cybersecurity and Digital Technologies,
78, Vernadskogo pr., Moscow, 119454.
Scopus AuthorID: 57297341000.
Competing Interests:
The authors declare no conflicts of interest.
Evgeny S. Mityakov
Russian Federation
Evgeny S. Mityakov, Dr. Sci. (Econ.), Professor, Acting Head of the Department “Subject-Oriented Information Systems,” Institute of Cybersecurity and Digital Technologies,
78, Vernadskogo pr., Moscow, 119454.
Scopus AuthorID: 55960540500.
Competing Interests:
The authors declare no conflicts of interest.
Zhanna G. Vegera
Russian Federation
Zhanna G. Vegera, Cand. Sci. (Phys.-Math.), Associate Professor, Head of the Department of Higher Mathematics, Institute of Cybersecurity and Digital Technologies,
78, Vernadskogo pr., Moscow, 119454.
Scopus AuthorID: 57212931836.
Competing Interests:
The authors declare no conflicts of interest.
Elena A. Maksimova
Russian Federation
Elena A. Maksimova, Dr. Sci. (Phys.-Math.), Associate Professor, Head of Department “Intelligent Information Security Systems,” Institute of Cybersecurity and Digital Technologies,
78, Vernadskogo pr., Moscow, 119454.
Scopus AuthorID: 57219701980.
Competing Interests:
The authors declare no conflicts of interest.
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Supplementary files
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1. Comparison of PSD distribution of harmonic and anomalous signals | |
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Type | Исследовательские инструменты | |
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Indexing metadata ▾ |
- In order to detect and quantify the differences between harmonic and anomalous signals, a comparative analysis of various anomaly detection methods was conducted, including fractal analysis, multifractal analysis, Shannon entropy calculation, and power spectral density (PSD) analysis.
- Use of the Shannon entropy method does not fully capture the complexity and uncertainty of harmonic and anomalous signals.
- Use of the PSD method revealed significant differences in energy distribution between these signals, permitting a more accurate identification of cyberattacks.
- For the effective detection of cyberattacks associated with harmonic signal distortions in power systems, a comprehensive approach is required, including time series analysis, frequency analysis, and machine learning methods.
Review
For citations:
Kochergin, S.V., Artemova S.V., Bakaev A.A., Mityakov E.S., Vegera Zh.G., Maksimova E.A. Improving Smart Grid security: Spectral and fractal analysis as tools for detecting cyberattacks. Russian Technological Journal. 2025;13(1):7-15. https://doi.org/10.32362/2500-316X-2025-13-1-7-15. EDN: DUNSTG