Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models
| Autor: | Eugeny Yu. Shchetinin, Tatyana R. Velieva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Discrete and Continuous Models and Applied Computational Science, Vol 30, Iss 3, Pp 258-268 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2658-4670 2658-7149 |
| DOI: | 10.22363/2658-4670-2022-30-3-258-268 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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