ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 6. Vol. 29. 2023

DOI: 10.17587/it.29.296-306

A. D. Kozlov, Researcher, N. L. Noga, Leading Software Engineer,
V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation

The Methodology for Determining the Most Critical Nodes of the Information Network Infrastructures in Order to Ensure Cybersecurity

Based on the conducted research of the influence of various parameters on the values of information and cybersecurity risks in the functioning of enterprises and organizations that widely use elements of the digital economy and have a network structure, the need for timely identification of critical nodes in the network structures used has been established. The authors propose the methodology for determining the most critical nodes of network information infrastructures to ensure their operability and the necessary level of cybersecurity. The methodology bases on the combined use of fuzzy logic, regression analysis and ranking methods in the context of the dependence of information and cybersecurity risk on many different parameters. The authors propose to solve the problem of determining the most critical nodes of network information infrastructures in two stages. At the first stage, the methods of fuzzy logic and regression analysis determine the set of parameters that most affect the value of the cybersecurity risk for the considerate network nodes. At the second stage, we identify the most critical nodes by the method of ranking nodes according to the parameters determined at the first stage. The use of the proposed methodology makes it possible to identify the most critical nodes of the network infrastructure in a timely manner, to plan the set of measures to maintain it at the required level of operability, as well as minimize possible damage and consequences from the implementation of existing threats.
Keywords: network information infrastructures, information security, the most critical nodes, multiple regression, fuzzy logic, ranking

P. 296-306

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