ARTIFICIAL INTELLIGENCE TECHNOLOGIES AS A TOOL FOR ENSURING THE CYBERSECURITY OF CRITICAL INFORMATION INFRASTRUCTURE

M. Komarov, N. Zaika, I. Martinyk

Èlektron. model. 2026, 48(1):87-98

https://doi.org/10.15407/emodel.48.01.087

ABSTRACT

The application of artificial intelligence and machine learning technologies for ensuring the cybersecurity of critical information infrastructure objects has been researched. A methodology for training machine learning models for the detection and classification of cyber incidents has been developed and generalized. Approaches to the formulation of a family of models and methods for representing events in a multidimensional feature space have been considered. The use of linear decision boundaries and logistic (sigmoid) functions for estimating the probability of fraudulent events is described. Loss functions for classification and regression tasks are analyzed, in particular, the sum of squared errors and negative logarithmic likelihood. First- and second-order optimization algorithms are researched, including gradient descent, stochastic gradient descent, and its variations of AdaGrad and Adam. The principle of the gradient descent method is demonstrated, and practical recommendations are given for selecting optimization algorithms depending on the type of task and data size. An example of the proposed methodology application for analyzing transactions in the energy sector to detect fraud is given.

KEYWORDS

artificial intelligence, machine learning, cybersecurity, critical information infrastructure, classification, loss function, optimization, gradient descent.

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