Information Security

Analysis of modern research on protection against adversarial attacks in energy systems

I. Kotenko^a, I. Saenko^a, O. Lauta^b, V. Sadovnikov^a, E. Ichetovkin^a, W. Li<sup>c

a</sup> St. Petersburg Federal Research Center of the Russian Academy of Sciences
^b Admiral Makarov State University of Maritime and Inland Shipping
^c Harbin Engineering University

Abstract: Machine learning-based systems, or machine learning systems, are currently attractive targets for attackers, since disruption of such systems can cause crucial consequences for critical infrastructure, in particular, energy systems. Therefore, the number of different types of cyber attacks against machine learning systems, which are called adversarial attacks, is continuously increasing, and these attacks are the subject of study for many researchers. Accordingly, many publications devoted to reviews of adversarial attacks and defense methods against them appear every year. Many types of adversarial attacks and defense methods in these review articles overlap. However, more recent studies contain information about new types of attacks and defense methods. The purpose of this article is to analyze the research conducted over the past six years in highly ranked journals, with an emphasis on review papers. The result of the study is a refined classification of adversarial attacks, characteristics of the most common attacks, as well as a refined classification and characteristics of defense methods against these attacks. The analysis focuses on adversarial attacks that target energy systems. The article concludes with a discussion of the advantages and disadvantages of various adversarial defense methods.

Keywords: cyber attacks, artificial intelligence, machine learning, adversarial attacks, threat model, defense methods, overview, energy systems, classification.

UDC: 004.056.5

Received: 30.03.2025

Language: English

DOI: 10.15622/ia.24.6.8