Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models

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Abstract

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.

About the authors

Eugeny Yu. Shchetinin

Financial University under the Government of Russian Federation

Author for correspondence.
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 Federation

Tatyana R. 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 Federation

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