MANAGEMENT OF INFORMATION FLOWS IN A CYBERSECURITY SYSTEM

Authors

DOI:

https://doi.org/10.18372/2410-7840.26.20016

Keywords:

cybersecurity, information flows, Markov chains, information characteristics, compression coefficient, regularization, adaptive control, quasi-stationarity, forecasting, optimization, data transfer systems

Abstract

Methods for controlling information flows in cybersecurity systems using probabilistic models and optimization algorithms have been studied. A mathematical framework has been developed for analyzing and stabilizing data flows in multichannel systems. Markov chains have been used to model the phase states of flows, and the information characteristics of the system, compression coefficients, and quasi-stationarity intervals have been mathematically estimated. The influence of tolerance parameters, phase state probabilities, and discretization levels on information transfer has been considered. Regularization mechanisms have been introduced to prevent singularities and ensure system stability. An adaptive approach to flow control has been proposed, allowing for dynamic adjustment of system parameters in the face of changing threats while maintaining the accuracy of data transmission.

Author Biographies

Andriy Fesenko, State university «Kyiv aviation institute»

PhD in Technical Sciences, Associate Professor, Dean of the Faculty of Computer Science and Technologies of the State non-commercial company state university «Kyiv aviation institute», Kyiv, Ukraine.

Nataliya Vyshnevska, State university «Kyiv aviation institute»

Senior Lecturer at the Department of Cybersecurity of the State non-commercial company state university «Kyiv aviation institute», Kyiv, Ukraine.

Diana Kozlovska, State university «Kyiv aviation institute»

Third-year Student of the Faculty of Computer Science and Technologies of the State non-commercial company state university «Kyiv aviation institute», Kyiv, Ukraine.

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Published

2025-05-20

Issue

Vol. 26 No. 2 (2024): Ukrainian Information Security Research Journal

Section

Articles

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