Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models
- Autores: Shchetinin E.Y.1, Velieva T.R.2
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Afiliações:
- Financial University under the Government of Russian Federation
- Peoples’ Friendship University of Russia (RUDN University)
- Edição: Volume 30, Nº 3 (2022)
- Páginas: 258-268
- Seção: Articles
- URL: https://journals.rcsi.science/2658-4670/article/view/315369
- DOI: https://doi.org/10.22363/2658-4670-2022-30-3-258-268
- ID: 315369
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Resumo
Modern smart energy grids combine advanced information and communication technologies into traditional energy systems for a more efficient and sustainable supply of electricity, which creates vulnerabilities in their security systems that can be used by attackers to conduct cyber-attacks that cause serious consequences, such as massive power outages and infrastructure damage. Existing machine learning methods for detecting cyber-attacks in intelligent energy networks mainly use classical classification algorithms, which require data markup, which is sometimes difficult, if not impossible. This article presents a new method for detecting cyber-attacks in intelligent energy networks based on weak machine learning methods for detecting anomalies. Semi-supervised anomaly detection uses only instances of normal events to train detection models, which makes it suitable for searching for unknown attack events. A number of popular methods for detecting anomalies with semisupervised algorithms were investigated in study using publicly available data sets on cyber-attacks on power systems to determine the most effective ones. A performance comparison with popular controlled algorithms shows that semi-controlled algorithms are more capable of detecting attack events than controlled algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by enhancing deep autoencoder model.
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Sobre autores
Eugeny Shchetinin
Financial University under the Government of Russian Federation
Autor responsável pela correspondência
Email: riviera-molto@mail.ru
ORCID ID: 0000-0003-3651-7629
Doctor of Physical and Mathematical Sciences, Lecturer of Department of Mathematics
49, Leningradsky Prospect, Moscow, 125993, Russian FederationTatyana Velieva
Peoples’ Friendship University of Russia (RUDN University)
Email: velieva-tr@rudn.ru
ORCID ID: 0000-0003-4466-8531
Candidate of Sciences in Physics and Mathematics, Senior lecturer of Department of Applied Probability and Informatics
6, Miklukho-Maklaya St., Moscow, 117198, Russian FederationBibliografia
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