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Substantiation of self-organization approaches in information networks to strengthen cyber resilience

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Parent Category: 2026

Category: Content №1 2026

Created on 27 February 2026

Last Updated on 27 February 2026

Published on 30 November -0001

Written by S. V. Onyshchenko, Ye. O. Zhyvylo, A. D. Hlushko, O. S. Gaydash

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Authors:

S. V. Onyshchenko, orcid.org/0000-0002-6173-4361, National University “Yuri Kondratyuk Poltava Polytechnic” Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ye. O. Zhyvylo, orcid.org/0000-0003-4077-7853, National University “Yuri Kondratyuk Poltava Polytechnic” Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A. D. Hlushko*, orcid.org/0000-0002-4086-1513, National University “Yuri Kondratyuk Poltava Polytechnic” Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. S. Gaydash, orcid.org/0009-0009-0030-1528, National University “Yuri Kondratyuk Poltava Polytechnic” Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (1): 138 - 146

https://doi.org/10.33271/nvngu/2026-1/138

Abstract:

Purpose. To formalize mathematical approaches and models that can be effectively applied to key self-organization methods of information networks within economic entities, considering the estimated dependencies between operational parameters and controllable variables across various levels of the OSI model.

Methodology. This research proposes an approach to modeling information infrastructure resilience based on the application of route optimization algorithms (Dijkstra, Bellman-Ford), Markov process theory, machine learning tools (SVM, neural networks), as well as entropic analysis and asymmetric encryption methods (RSA, ECC). The systematic approach is implemented through the analysis of interdependencies between the layers of the OSI network model to identify vulnerable segments and risk control points.

Findings. Methods of self-organization of information networks have been developed that ensure early detection of anomalies, effective management of routing and data encryption, as well as adaptability to changes in the external environment and reduction of the risk of cyber-attacks. An information infrastructure protection architecture has been developed that covers seven levels of the OSI model, ensuring the integrity, availability, and confidentiality of data in the information networks of economic entities.

Originality. This research proposes an approach to ensuring the resilience of information networks, distinguishing itself from existing methods by the coordinated application of mathematical, cryptographic, and cognitive methods within the context of the OSI network layer hierarchy. The expediency of incorporating entropic control as an indicator of system randomness and potential vulnerability has been substantiated.

Practical value. The practical significance of this research lies in the applicability of its results in developing information and cybersecurity policies for economic entities. The proposed solutions contribute to strengthening not only information security but also financial and personnel security amidst digital transformation, as well as minimizing the consequences of cyber incidents.

Keywords: information security, information infrastructure, OSI model, entropy, economic entity

References.

1. Onyshchenko, S., Hlushko, A., Maslii, O., & Chumak, O. (2024). Digital transformation of the national economy in the context of information environment development in Ukraine. Transformations of national economies under conditions of instability, (6), 169-197. Scientific Route OÜ. https://doi.org/10.21303/978-9916-9850-6-9.ch6

2. World Economic Forum (2025). The global risks report 2025 (20 ^th ed.). Retrieved from https://www.weforum.org/publications/global-risks-report-2025/

3. Onyshchenko, S., Yanko, A., Hlushko, A., & Maslii, O. (2023). Economic cyber security of business in Ukraine: Strategic directions and implementation mechanism. In Economic and cyber security, (pp. 30-58). https://doi.org/10.15587/978-617-7319-98-5.CH2

4. Operatyvnyi tsentr reahuvannia na kyberintsydenty Derzhavnoho tsentru kyberzakhystu Derzhavnoi sluzhby spetsialnoho zviazku ta zakhystu informatsii Ukrainy (2024). Systems for vulnerability detection and response to cyber incidents and cyberattacks: Annual report 2024. Retrieved from https://scpc.gov.ua/api/files/72e13298-4d02-40bf-b436-46d927c88006

5. Onyshchenko, S., Yanko, A., & Hlushko, А. (2023). Improving the efficiency of diagnosing errors in computer devices for processing economic data functioning in the class of residuals. Eastern-European Journal of Enterprise Technologies, 5(4(125)), 63-73. https://doi.org/10.15587/1729-4061.2023.289185

6. Shefer, O., Laktionov, O., Pents, V., Hlushko, A., & Kuchuk, N. (2024). Practical principles of integrating artificial intelligence into the technology of regional security predicting. Advanced Information Systems, 8(1), 86-93. https://doi.org/10.20998/2522-9052.2024.1.11

7. Zhu, Q. (2024). Foundations of cyber resilience: The confluence of game, control, and learning theories. Electrical Engineering and Systems Science. https://doi.org/10.48550/arXiv.2404.01205

8. Araujo, M. S. d., Machado, B. A. S., & Passos, F. U. (2024). Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance. Applied Sciences, 14(5), 2116. https://doi.org/10.3390/app14052116

9. Collier, Z. A., DiMase, D., Walters, S., Tehranipoor, M. M., Lambert, J. H., & Linkov, I. (2014). Cybersecurity standards: Managing risk and creating resilience. Computer, 47(9), 70-76. https://doi.org/10.1109/MC.2013.448

10.      Ferdinand, J. (2015). Building organisational cyber resilience: A strategic knowledge-based view of cyber security management. Journal of business continuity & emergency planning, 9(2), 185-195. https://doi.org/10.69554/PRJY4917

11.      Huang, Y., Huang, L., & Zhu, Q. (2022). Reinforcement learning for feedback-enabled cyber resilience. Annual Reviews in Control, 53, 273-295. https://doi.org/10.1016/j.arcontrol.2022.01.001

12.      Kheddar, H., Dawoud, D. W., Awad, A. I., Himeur, Y., & Khan, M. K. (2024). Reinforcement-learning-based intrusion detection in communication networks: A review. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2024.3484491

13.      Gueriani, A., Kheddar, H., & Mazari, A. C. (2024). Adaptive cyber-attack detection in IIoT using attention-based LSTM-CNN ­models. 2024 International Conference on Telecommunications and Intelligent Systems (ICTIS), (pp. 1-6). IEEE. https://doi.org/10.1109/ICTIS62692.2024.10894509

14.      Gaydash, O. (2025). Comparative Analysis of External and Internal Factors Affecting the Formation of Personnel Security in Ukrainian Enterprises. Bulletin of the Academy of Labor, Social Relations and Tourism. Series: Economics, Psychology and Management, 6. https://doi.org/10.54929/3041-2390-2025-06-04-02

15.      Onyshchenko, S., Zhyvylo, Y., Cherviak, A., & Bilko, S. (2023). Determination of the peculiarities peculiarities of using information security systems in financial institutions in order to increase the financial security level. Eastern-European Journal of Enterprise Technologies, 5(13(125)), 65-76. https://doi.org/10.15587/1729-4061.2023.288175

16.      Trenchev, I., Dimitrov, W., Dimitrov, G., Ostrovska, T., & Trencheva, M. (2023). Mathematical Approaches Transform Cybersecurity from Protoscience to Science. Applied Sciences, 13(11), 6508. https://doi.org/10.3390/app13116508

17.      Alyami, H., Nadeem, M., Alharbi, A., Alosaimi, W., ­Ansari, M. T. J., Pandey, D., Kumar, R., & Khan, R. A. (2021). The Evaluation of Software Security through Quantum Computing Techniques: A Durability Perspective. Applied Sciences, 11(24), 11784. https://doi.org/10.3390/app112411784

18.      Miller, T., Durlik, I., Kostecka, E., Sokołowska, S., Kozlovska, P., & Zwolak, R. (2025). Artificial Intelligence in Maritime Cybersecurity: A Systematic Review of AI-Driven Threat Detection and Risk Mitigation Strategies. Electronics, 14(9), 1844. https://doi.org/10.3390/electronics14091844

19.      Cherkaoui Dekkaki, K., Tasic, I., & Cano, M.-D. (2024). Exploring Post-Quantum Cryptography: Review and Directions for the Transition Process. Technologies, 12(12), 241. https://doi.org/10.3390/technologies12120241

20.      Fernández-Caramés, T. M., & Fraga-Lamas, P. (2020). Towards Post-Quantum Blockchain: A Review on Blockchain Cryptography Resistant to Quantum Computing Attacks. IEEE Access, 8, 21091-21116. https://doi.org/10.1109/ACCESS.2020.2968985

21.      Hamarsheh, A. (2024). An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning. Applied Sciences, 14(11), 4530. https://doi.org/10.3390/app14114530

22.      Tang, T., Yao, J., Wang, Y., Sha, Q., Feng, H., & Xu, Z. (2025). Application of deep generative models for anomaly detection in complex financial transactions. Proceedings of the 2025 4 ^th International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID), (pp. 133-137). IEEE. https://doi.org/10.1109/icaid65275.2025.11034573

23.      Maslii, O., Buriak, A., Chaikina, A., & Cherviak, A. (2025). Improving conceptual approaches to ensuring state economic security under conditions of digitalization. Eastern European Journal of Enterprise Technologies, 1(13(133)), 35-45. https://doi.org/10.15587/1729-4061.2024.319256

24.      Moon, P. S., Deshmukh, A. B., Sarode, H. J., Sharma, S., Birare, K. M., & Anandpwar, W. N. (2025). Implementation of machine learning techniques for predictive security analytics. V. Bhateja, M. Dey, & R. Senkerik (Eds.), Proceedings of FICTA 2024 – Innovations in information and decision sciences, 422. Springer. https://doi.org/10.1007/978-981-96-0147-9_41

25.      Yanko, A., Krasnobayev, V., & Martynenko, A. (2023). Influence of the number system in residual classes on the fault tolerance of the computer system. Radioelectronic and Computer Systems, 3(107), 159-172. https://doi.org/10.32620/reks.2023.3.13

26.      Bannister, M. J., & Eppstein, D. (2012). Randomized speedup of the Bellman–Ford algorithm. Proceedings of the 2012 Meeting on Analytic Algorithmics and Combinatorics (ANALCO12), (pp. 41-47). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611973020.6

27.      Ren, H., Ye, Z., & Li, Z. (2017). Anomaly detection based on a dynamic Markov model. Information sciences, 411, 52-65. https://doi.org/10.1016/j.ins.2017.05.021

28.      Abuali, K. M., Nissirat, L., & Al-Samawi, A. (2023). Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection. Sensors, 23(21), 8959. https://doi.org/10.3390/s23218959

29.      Monteiro, V. (2024). The importance of entropy sources in cryptography: Randomness to secure communications. Journal of Information Technology & Software Engineering, 14, 393. https://doi.org/10.35248/2165-7866.24.14.393

30.      Maslii, O., & Maksymenko, A. (2025). Digital transformation and economic deindustrialisation: Impact on state financial security. Financial and Credit Activity: Problems of Theory and Practice, 1(60), 401-414. https://doi.org/10.55643/fcaptp.1.60.2025.4599

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