C&SA | Contents

Volume 61 >>> № 6 NOVEMBER — DECEMBER 2025

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UDC 519.7:65.011

A.O. Azarova
Vinnytsia National Technical University, Vinnytsia, Ukraine,
azarova.angelika@gmail.com

Iu.V. Krak
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine;
V.M. Glushkov Institute of Cybernetics, National Academy
of Sciences of Ukraine, Kyiv, Ukraine, iurii.krak@knu.ua

O.G. Murashchenko
Vinnytsia National Technical University, Vinnytsia, Ukraine,
Oleksandr.Murashchenko@gmail.com

L.O. Nikiforova
Vinnytsia National Technical University, Vinnytsia, Ukraine,
nikiforovalilia@gmail.com

O.V. Ruzakova
Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University, Vinnytsia, Ukraine, olgarkv81@gmail.com

INFORMATION SYSTEM VULNERABILITY ASSESSMENT
USING HAMMING NEURAL NETWORK

Abstract. The article reviews existing theoretical developments in the field of assessing the security level of information systems based on various approaches, including the use of neural networks and artificial intelligence. This enabled the authors of the article to develop their own mathematical and structural models of the process of assessing the level of information security of IS and the method of their formalization based on a systems approach, a Hamming neural network, and elements of artificial intelligence. A set of evaluation parameters of the level of security of modern IS was substantiated, and a digital technology was developed for identifying vulnerabilities in IS security from various fields of application using a Hamming neural network and artificial intelligence. The experimental verification pool for the developed model comprises the information systems of 10 companies.

Keywords: digital technology, information security, vulnerability of information systems, Hamming neural network, artificial intelligence.

full text

REFERENCES

1. Khilenko V., Akhmetov B., Berdibayev R. et al. Increasing the speed of banking cybersecurity systems based on intelligent data analysis and artificial intelligence algorithms for predicting cyberattacks. I. Cybernetics and Systems Analysis. 2023. Vol. 59, N 4. P. 519–525. https://doi.org/10.1007/s10559-023-00587-x.
2. Alfaadhel A., Almomani I., Ahmed M. Risk-based cybersecurity compliance assessment system (RC2AS). Applied Sciences. 2023. Vol. 13, N 10. Article number 6145. https://doi.org/10.3390/app13106145.
3. Zhang D., Chen J., He Y., Lan X., Chen X., Dong C., Li J. A chunked and disordered data privacy protection algorithm: Application to resource platform systems. Applied Sciences. 2023. Vol. 13, N 10. Article number 6017. https://doi.org/10.3390/app13106017.
4. Azarova A.O., Krak I.V., Nikiforova L.O., Belyakova K.S. Applying systems analysis and mathematical apparatus of fuzzy logic to model the process of evaluating the financial state of the enterprise. Cybernetics and Systems Analysis. 2024. Vol. 60, N 6. P. 978–990. https://doi.org/10.1007/s10559-024-00734-y.
5. Azarova A., Azarova L., Krak I., Ruzakova O., Azarova V. Information system for assessing the level of human capital management. Informatyka Automatyka Pomiary w Gospodarce i Ochronie . 2024. Vol. 14, N 3. P. 123–128. https://doi.org/10.35784/iapgos.6159.
6. Mashima D., Chen Y., Roomi M., Lakshminarayana S., Chen D. Cybersecurity for modern smart grid against emerging threats. Foundations and Trends® in Privacy and Security. 2023. Vol. 5, N 4. P. 189–285. http://dx.doi.org/10.1561/3300000035.
7. Azarova A., Azarova L., Nikiforova L., Azarova V., Teplova O., Kryvinska N. Neural network technologies of investment risk estimation taking into account the legislative aspect. CEUR Workshop Proceedings. 2020. Vol. 2805. P. 308–323. URL: https://ceur-ws.org/Vol-2805/paper23.pdf.
8. Azarova A., Azarova L., Pavlov S., Savina N., Kaplun I., WЛjcik W., Smailova S., Kalizhanova A. Information technologies for assessing the quality of IT-specialties graduates’ training of university by means of fuzzy logic and neural networks. International Journal of Electronics and Telecommunications. 2020. Vol. 66, N 3. P. 411–416. https://doi.org/10.24425/ijet.2020.131893.
9. Zhenxiang H., Zhenyu Z., Ke C., Yanlin L. Smart contract vulnerability detection method based on feature graph and multiple attention mechanisms. Computers, Materials & Continua. 2024. Vol. 79, N 2. P. 3023–3045. https://doi.org/10.32604/cmc.2024.050281.
10. Letychevskyi O. Congnitive networks, their properties and applications in attack detection and prevention systems. Cybernetics and Systems Analysis. 2023. Vol. 59, N 5. P. 859–866. https://doi.org/10.1007/s10559-023-00621-y.
11. Dubin R. Disarming attacks inside neural network models. IEEE Access. 2023. Vol. 11. P. 124295–124303. https://doi.org/10.1109/ACCESS.2023.3330141.
12. Wu H., Dong H., He Y., Duan Q. Smart contract vulnerability detection based on hybrid attention mechanism model. Applied Sciences. 2023. Vol. 13, N 2. Article number 770. https://doi.org/10.3390/app13020770.
13. Wen S.-F., Shukla A., Katt B. Artificial intelligence for system security assurance: A systematic literature review. International Journal of Information Security. 2025. Vol. 24, Iss. 1. https://doi.org/10.1007/s10207-024-00959-0.
14. Nitaj A., Rachidi T. Applications of neural network-based AI in cryptography. Cryptography. 2023. Vol. 7, N 3. Article number 39. https://doi.org/10.3390/cryptography7030039.
15. Berghoff C., Neu M., Twickel A. Vulnerabilities of connectionist AI applications: Evaluation and defense. Front. Big Data. 2020. Vol. 3. Article number 23. https://doi.org/10.3389/fdata.2020.00023.
16. Cybersecurity — Ukraine | Statista market forecast. Statista. URL: https://www.statista.com/outlook/tmo/cybersecurity/ukraine.
17. Alam S. Cybersecurity: Past, present and future. LAP LAMBERT Academic Publishing, 2022. 123 p.
18. Hashim N.A., Abidin Z.Z., Zakaria N.A., Puvanasvaran A.P., Ahmad R. Risk assessment method for insider threats in cyber security. International Journal of Advanced Computer Science and Applications. 2018. Vol. 9, N 11. P. 126–130. https://dx.doi.org/10.14569/IJACSA.2018.091119.
19. Shyian А.А., Nikiforova L.O. Ensuring information security and cybersecurity in the modern information space: models and methods. Vinnytsia: VNTU, 2024. 135 p.
20. Kuzminykh I., Ghita B., Sokolov V., Bakhshi T. Information security risk assessment. Encyclopedia. 2021. Vol. 1, N 3. P. 602–617. https://doi.org/10.3390/encyclopedia1030050.
21. Yevseiev S., Khokhlachova Yu., Ostapov S. et al. Models of socio-cyber-physical systems security. Kharkiv: PC TECHNOLOGY CENTER, 2023. 168 p. https://doi.org/10.15587/978-617-7319-72-5.
22. EBIOS Risk Manager. 2024. 100 p. URL: https://cyber.gouv.fr/sites/default/files/document/20240326_np_anssi_guide_ebios_fr_final_web.pdf (in French).
23. Pandit P. Nessus: Study of a tool to assess network vulnerabilities. 2021. http://doi.org/10.13140/RG.2.2.14758.65604.
24. Nisha P. Vulnerability scanning with OpenVAS: Unveiling cybersecurity insights. 2023. URL: https://eunishap.medium.com/vulnerability-scanning-with-openvas-unveiling-cybersecurity-insights-eff56de79276.
25. Raj S., Walia N.K. A study on metasploit framework: A pen-testing tool. International Conference on Computational Performance Evaluation (ComPE). 2020. P. 296–302. http://doi.org/10.1109/ComPE49325.2020.9200028.
26. Soepeno R. Wireshark: An effective tool for network analysis. 2023. http://doi.org/10.13140/RG.2.2.34444.69769.
27. Introduction to Burp Suite, the tool dedicated to web application security. January 15, 2024 URL: https://www.vaadata.com/blog/introduction-to-burp-suite-the-tool-dedicated-to-web-application-security/.
28. Mezher L.S. Hamming neural network application with FPGA device. International Journal of Reconfigurable and Embedded Systems (IJRES). 2021. Vol. 10, N 1. P. 37–46. http://doi.org/10.11591/ijres.v10.i1.pp37-46.
29. Next-generation firewall overview. URL: https://www.cisco.com/site/NGFW.
30. IDS/IPS. Cisco. URL: https://search.cisco.com/IDS/IPS.
31. What is the role of AI and ML in modern SIEM solutions? URL: https://www.paloaltonetworks.com/cyberpedia/role-of-artificial-intelligence-ai-and-machine-learning-ml-in-siem?utm_source= chatgpt.com.
32. CORTEX XDR URL: https://www.paloaltonetworks.com/cortex/cortex-xdr?utm_source=chatgpt.com.
33. Azarova A.O., Smolyak I.A. Information technology for assessing information security vulnerabilities of modern IS. Abstracts of the International Scientific and Practical Internet Conference of Students, Postgraduate Students and Young Scientists “Youth in Science: Research, Problems, Prospects”. Vinnytsia: VNTU, 2024. URL: https://conferences.vntu.edu.ua/index.php/mn/mn2024/paper/view/19660/16275.
34. Sablatzky T. The delphi method. Hypothesis: Research Journal for Health Information Professionals. 2022. Vol. 34, N 1. https://doi.org/10.18060/26224.
35. ERP «Вправно». URL: https://vpravno.com.ua/uk/.
36. ERP «Master». URL: https://masterbuh.com/.
37. «Своя CRM». URL: https://svoya.in.ua/.
38. HRM «Hurma». URL: https://hurma.work/.

UDC 519.7:65.011

INFORMATION SYSTEM VULNERABILITY ASSESSMENT USING HAMMING NEURAL NETWORK

REFERENCES

INFORMATION SYSTEM VULNERABILITY ASSESSMENT
USING HAMMING NEURAL NETWORK