Krasnogorsk, Moscow, Russian Federation
Russian Federation
UDC 004.7
The article deals with the application of mathematical models of the neural network learning process, which makes it possible to ensure the required protection of the information exchange system (IES) of critical information infrastructure objects (CIIO). Presented the analysis of models of various types of cyber attacks. It is determined that random cyber attacks are a fundamental element of the neural network learning process to control the security of IES CIIO. The rationale for the need to identify accidental cyber attacks before the security parameters exceed the warning tolerance is given. It has been established that it is advisable to use the characteristics of external program requests to determine security parameters. An approach to determining the fundamental expediency of using neural network tools for assessing security parameters is considered. The issue of optimization of neural network models is raised.
information exchange system, cyber attacks, neural network, critical information infrastructure objects.
1. Shcheglov K.A., Shcheglov A.Y. Reliability of information security. basic concepts and formulation of the modeling problem // Information security issues. - 2017. - № 1 (116). - Pp. 27-35.
2. Orlov A. I. High statistical technologists / A. I. Orlov // Factory Laboratory. - 2003. - Vol. 69, No. 11. - pp. 55-60.
3. Orlov A. I. Applied Statistics, Moscow: KnoRus, 2015.. - 656 p .
4. Kendall M. Multidimensional statistical analysis and time series / Kendall M., Stewart A.; translated from English - Moscow: Nauka, 1976. - 722 p.
5. Shaporev S.D. Mathematical methods of applied statistics: a textbook; Ministry of Education and Science of the Russian Federation, Federal State Autonomous Educational Institution of Higher Education. St. Petersburg State University of Aerospace Engineering. - St. Petersburg : GUAP, 2016. - 407 p.
6. Ignatov V. A. Elements of the theory of optimal maintenance of technical products / V. A. Ignatov, G. G. Manshin, V. V. Kostanovsky. Minsk: Science and Technology, 1974. 192 p.
7. Korchenko A.A. A system for detecting abnormal conditions in computer networks / A.A. Korchenko // Safety. - 2012. - № 2 (18). - Pp. 80-84.
8. Rudenko O.G. Artificial neural networks. Textbook / A.G. Rudenko, E.V. Bodyansky. Kharkiv: SMITH Company LLC, 2016. 404 p.
9. Bogdanova E.A. Engineering of knowledge: a textbook; Volga State University of Telecommunications and Informatics, Department of Economics and Information Systems. Samara : Volga Region State University of Telecommunications and Informatics, 2016, p. 93.
10. Bogush V.N. Information security: a terminological educational handbook / V. Bogush, V. G. Krivutsa, A.M. Kudin. Moscow: PEC, 2004. 508 p.
11. Marienkov A.N. Ensuring information security of computing networks : an educational and methodological guide for students of the training area 10.03.01 "Information security" / [A. N. Marienkov, V. G. Lim] ; Ministry of Education and Science of the Russian Federation, Federal State Budgetary Educational Institution of Higher Education "Astrakhan State University, Faculty of Mathematics and Information Technologies, Department of Information Security. - Astrakhan : publisher Sorokin R. V., 2018. - 72 p.
12. Bubnov A.A. Fundamentals of information security : a textbook for students studying in the specialty group of secondary vocational education "Information security" / A. A. Bubnov, V. N. Przhegorlinsky, O. A. Savinkin. - 2nd ed., ster. Moscow : Akademiya Publ., 2019. 253 p.
13. Yezhov A. A. Neurocomputing and its applications in economics and business / A. A. Yezhov, S. A. Shumsky, Moscow: MEPhI, 1998, 224 p.
14. Lukatsky A.V. Detection of attacks / A.V. Lukatsky. - St. Petersburg. BHV-Petersburg, 2003. - 624 p.
15. Cross-layered distributed data-driven framework for enhanced smart grid cyber-physical security / A. Starke, K. Nagaraj, C. Ruben [et al.] // IET Smart Grid. – 2022. – Vol. 5, No. 6. – P. 398-416. – DOIhttps://doi.org/10.1049/stg2.12070. – EDN BDFPTU.
16. Guest editorial: Applications of advanced machine learning and big data techniques in renewable energy-based power grids / M. Dabbaghjamanesh, A. Kavousi-Fard, Zh. Ya. Dong, A. Jolfaei // IET Renewable Power Generation. – 2022. – Vol. 16, No. 16. – P. 3445-3448. – DOIhttps://doi.org/10.1049/rpg2.12622. – EDN FLBGTO.
17. Joint Security and Resources Allocation Scheme Design in Edge Intelligence Enabled CBTCs: A Two-Level Game Theoretic Approach / Ya. Li, Li. Zhu, H. Wang [et al.] // IEEE Transactions on Intelligent Transportation Systems. – 2023. – Vol. 24, No. 12. – P. 13948-13961. – DOIhttps://doi.org/10.1109/tits.2023.3294546. – EDN LESEIK.
18. Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment / C. L. Kumar, S. Betam, D. Pustokhin [et al.] // Scientific Reports. – 2025. – Vol. 15, No. 1. – P. 14166. – DOIhttps://doi.org/10.1038/s41598-025-96153-w. – EDN RLPTJY.
19. Towards symbiotic autonomous systems / O. Kaynak, W. He, Z. Liu, F. Flammini // Philosophical Transactions: Mathematical, Physical and Engineering Sciences (Series A). – 2021. – Vol. 379, No. 2207. – P. 20200359. – DOIhttps://doi.org/10.1098/rsta.2020.0359. – EDN BXKCKG.
20. Guest editorial: Resilient fuzzy control synthesis of non-linear networked systems against various cyber-attacks / X. Xie, T. H. Lee, J. Xia [et al.] // IET Control Theory and Applications. – 2024. – DOIhttps://doi.org/10.1049/cth2.12729. – EDN QPGFRB.
21. Security, Privacy, and Trust Management on Decentralized Systems and Networks / W. Meng, S. K. Katsikas, J. Chen, Ch. Chen // International Journal of Network Management. – 2024. – Vol. 34, No. 6. – DOIhttps://doi.org/10.1002/nem.2311. – EDN QYCHBC.
22. MalDroid: Secure DL-enabled intelligent malware detection framework / I. Ul. Haq, T. A. Khan, A. Akhunzada, X. Liu // IET Communications. – 2022. – Vol. 16, No. 10. – P. 1160-1171. – DOIhttps://doi.org/10.1049/cmu2.12265. – EDN FKFDJD.
23. Zahednejad, B. An improved integral distinguisher scheme based on neural networks / B. Zahednejad, L. Lyu // International Journal of Intelligent Systems. – 2022. – Vol. 37, No. 10. – P. 7584-7613. – DOIhttps://doi.org/10.1002/int.22895. – EDN JQCUYT.
24. Emerging applications of IoT and cybersecurity for electrical power systems / M. M. F. Darwish, M. Elsisi, M. M. Fouda [et al.] // IET Generation, Transmission & Distribution. – 2023. – Vol. 17, No. 20. – P. 4453-4456. – DOIhttps://doi.org/10.1049/gtd2.13012. – EDN ZBRVVC.
