Artificial Intelligence Technologies in Solving Information Security Problems
https://doi.org/10.21822/2073-6185-2026-53-1-157-169
Abstract
Objective. The purpose of this study is to analyze the potential of artificial intelligence technologies for solving information security problems.
Method. The study is based on a proactive approach aimed at reducing the negative impact of internal and external threats; on the principles of solving information security problems; and on the features and capabilities of intelligent methods.
Result. The developed algorithm for implementing artificial intelligence technologies describes the key steps required to build intelligent information security subsystems.
Conclusion. The implementation of artificial intelligence technologies will enable the development of adaptive, intelligent security systems that quickly respond to threats, attacks, and incidents. Security professionals must manage risks and establish principles of accountability and transparency in the operation of intelligent information security subsystems.
About the Authors
D. A. Potienko
Don State Technical University
Russian Federation
Russian Federation
Daniil A. Potienko, Master's student, Department of Computing Systems and Information Security,
1 Gagarin Square, Rostov-on-Don 344003
D. S. Chmykhalo
Don State Technical University
Russian Federation
Russian Federation
Danil S. Chmykhalo, Master's student, Department of Computing Systems and Information Security,
1 Gagarin Square, Rostov-on-Don 344003
O. L. Legonko
Don State Technical University
Russian Federation
Russian Federation
Olga L. Legonko, Cand. Sci. (Eng.), Assoc. Prof., Department of Computing Systems and Information Security,
1 Gagarin Square, Rostov-on-Don 344003
References
1. Shinkaretskaya G.G., Berman A.M. Cyberattacks – Criminal Use of Digital Technologies // International Law. 2022. No. 1. P. 40-50. DOI: 10.25136/2644-5514.2022.1.37271. URL: https://nbpublish.com/libraryreadarticle.php?id=37271
2. Decree of the President of the Russian Federation dated October 10, 2019 No. 490 «On the Development of Artificial Intelligence in the Russian Federation» [Electronic resource] // GARANT. https://base.garant.ru/72838946/ (accessed: 21.03.2025)
3. GOST R 50922-2006. Information Protection. Basic Terms and Definitions. – Moscow: Standartinform, 2008. – 12 p.
4. Recommendations for Standardization R 50.1.056-2005. Technical Protection of Information. Basic Terms and Definitions. – Moscow: Standartinform, 2006. – 20 p.
5. GOST R 59710-2022. Information Protection. Management of Computer Incidents. General Provisions. – Moscow: Russian Institute for Standardization, 2022. – 16 p.
6. Milanovich E.A., Seleznev I.L. System for Analyzing Network Traffic to Ensure Network Security // Young Scientist. 2020. No. 15 (305). P. 86-89. URL: https://moluch.ru/archive/305/68701/ (accessed: 21.03.2025)
7. GOST R ISO/IEC 27002-2021. Information Technology. Security Techniques. Code of Practice for Information Security Controls. – Moscow: Standartinform, 2021. – 74 p.
8. Meshcheryakov R.V., Melnikov S.Yu., Peresypkin V.A., Khorev A.A. Promising Areas for the Application of Artificial Intelligence Technologies in Information Protection // Cybersecurity Issues. 2024; 4(62). DOI: 10.21681/2311-3456-2024-4-02-12
9. Dartmouth Conference on Artificial Intelligence [Electronic resource] // Psychology. – URL: https://psixologiya.org/blog/2504-dartmutskaya-konferenciya-po-iskusstvennomu-intellektu.html (accessed: 21.03.2025)
10. What is Artificial Intelligence and Its Types [Electr.resource]aisimple.ru. https://aisimple.ru/22-chto-takoe-ii.html (acces.: 21.03.2025)
11. Usamov I.R. , A.A. Albakova, A.A. Mustiev . The Role of Intelligent Information Systems in the Modern World. Current Issues of Modern Science: Theory, Technology, Methodology, and Practice: Materials of the International Scientific and Practical Online Conference Dedicated to the 60th Anniversary of Corresponding Member of the Academy of Sciences of the Chechen Republic, Dr. of Technical Sciences, Prof. Said-Alvi Yusupovich Murtazayev, Grozny, April 28, 2021. Grozny: Grozny State Oil Technical University named after Academician M.D. Millionshchikov,2021;267-272.DOI:10.34708/GSTOU.CONF..2021.10.35.053. EDN PMXCRD.
12. Reinhard Klette. Computer Vision. Theory and Algorithms.Translated from English by A.A. Slinkin. Moscow: DMK Press, 2019:506.
13. Proshina M.V. Modern Methods of Natural Language Processing: Neural Networks // Construction Economics. 2022; 5. URL: https://cyberleninka.ru/article/n/sovremennye-metody-obrabotki-estestvennogo-yazyka-neyronnye-seti (accessed: 21.03.2025)
14. Tjandra, A., Sakti, S., Nakamura,S. End-to-End Speech Recognition Sequence Training With Reinforcement Learning. IEEE Access, 2019;7:79758-79769.
15. Chechnev V.B. Use of Decision Support Systems in Decision-Making Automation. Electronic Libraries. 2025; 28(1):163-183. – DOI: 10.26907/1562-5419-2025-28-1-163-183. – EDN OGRDVD.
16. Pohl J. Artificial Superintelligence: Extinction or Nirvana? InterSymp-2015;27:1–20.
17. Ilyushin L.S., Torpashova N.A. Artificial Intelligence Technologies as a Resource for Transforming Educational Practices. Yaroslavl Pedagogical Bulletin. 2024; 3(138): 62-71. – DOI: 10.20323/1813-145X-2024-3-138-62. – EDN ADWMMG.
18. Goodfellow Ian, Pouget-Abadie Jean, Mirza Mehdi, Xu Bing, Warde-Farley David, Ozair Sherjil, Courville Aaron, Bengio Yoshua Generative Adversarial Nets. Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014; 2672–2680.
19. Balanov A.N. Comprehensive Information Security: Textbook for Higher Education Institutions. Saint Petersburg:Lan. 2025;400.
20. Dorgushaeva A.K., Dovgal V.A., Kozlova N.Sh., Kozlov R.S. Review of the Use of Machine Learning Technologies in Ensuring Data Information Security: Present and Future // Bulletin of Adyghe State University. Series 4: Natural-Mathematical and Technical Sciences. 2024. No. 1 (336). URL: https://cyberleninka.ru/article/n/obzor-ispolzovaniya-tehnologiy-mashinnogo-obucheniya-v-obespechenii-informatsionnoy-bezopasnosti-dannyh-nastoyaschee-i-buduschee (accessed: 23.03.2025)
21. Mukkamala, S., Janoski, G.I., & Sung, A.H. (2002). Intrusion detection using neural networks and support vector machines. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2, 1702-1707 vol.2.
22. Lee, W., & Stolfo, S. (2000). A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security (TISSEC), 3, 227 - 261.
23. Demertzis, K., & Iliadis, L.S. (2015). A Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security.
24. Koloveas, P., Chantzios, T., Alevizopoulou, S., Skiadopoulos, S., & Tryfonopoulos, C. (2021). inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence. Electronics.
25. Rani, N., Saha, B., Maurya, V., & Shukla, S.K. (2024). TTPXHunter: Actionable Threat Intelligence Extraction as TTPs from Finished Cyber Threat Reports. Digital Threats: Research and Practice, 5, 1 - 19.
26. Orbinato, V., Barbaraci, M., Natella, R., & Cotroneo, D. Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study: (Practical Experience Report). IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE), 2022; 181-192.
27. Ajvad Haneef K., Madhu Kumar S.D. Deep Learning Techniques for Malware Detection: A Comprehensive Survey. 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), 2023; 1-7.
28. Alomari, E., Nuiaa, R.R., Alyasseri, Z.A., Mohammed, H.J., Sani, N.S., Esa, M.I., & Musawi, B.A. Malware Detection Using Deep Learning and Correlation-Based Feature Selection. Symmetry, 2023; 15, 123.
29. He, Y., Kang, X., Yan, Q., & Li, E. (2024). ResNeXt+: Attention Mechanisms Based on ResNeXt for Malware Detection and Classification. IEEE Transactions on Information Forensics and Security, 19, 1142-1155.
30. Liu, A., Du, X., & Wang, N. (2021). Efficient Access Control Permission Decision Engine Based on Machine Learning. Secur. Commun. Networks, 2021, 3970485:1-3970485:11.
31. Afshar, M., Samet, S., & Usefi, H. (2021). Incorporating Behavior in Attribute Based Access Control Model Using Machine Learning. 2021 IEEE International Systems Conference (SysCon), 1-8.
32. Alqazzaz, A. Integrated Neutrosophic methodology and Machine Learning Models for Cybersecurity Risk Assessment: An exploratory study. International Journal of Neutrosophic Science. 2024.
33. Barlybayev, A., Sharipbay, A., Shakhmetova, G., & Zhumadillayeva, A. Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies. Applied Sciences. 2024.
34. Nastou, P.E., Papataxiarhis, V., Moutsis, S.N., Tsintotas, K.A., Petroudis, G., Papastamatiou, N., Mesaritakis, C., Gasteratos, A., Vouyioukas, D., & Gavathas, P. (2024). An Efficient Highly-Secure AI-Based System for Incident Management in Critical Infrastructures. 2024 IEEE International Conference on Imaging Systems and Techniques (IST), 1-6.
35. Las-Casas, P.H., Kumbhare, A.G., Fonseca, R., & Agarwal, S. LLexus: an AI agent system for incident management. ACM SIGOPS Operating Systems Review, 2024;58: 23 - 36.
36. Harshitha, T.S. Intrusion Detection and Prevention Using CNN-LSTM. International Journal of Science, Engineering and Technology. 2024.
37. Alhassan, S., Abdul-Salaam, G., Micheal, A., Missah, Y.M., Ganaa, E.D., & Shirazu, A.S. (2024). CFS-AE: Correlation-based Feature Selection and Autoencoder for Improved Intrusion Detection System Performance. J. Internet Serv. Inf. Secur., 14, 104-120.
38. Abualhaj, M.M., Abu-Shareha, A.A., & Rateb, R. (2025). Enhancing intrusion detection systems with hybrid HHO-WOA optimization and gradient boosting machine classifier. International Journal of Reconfigurable and Embedded Systems (IJRES).
39. Maheswaran N., Bose S., Gokulraj G., Anitha T., Shruthi T., Vijayaraj G. (2025). Intrusion Prevention System in SDN Environment for 6G Networks Using Deep Learning. 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), 2025; 53-61.
40. Sreenivasa Reddy, G., & Shyama Chandra Prasad, G. (2023). Intrusion detection system using clustering algorithms of neural networks. International Journal of Advanced Research.
41. Sana, L., Nazir, M.M., Yang, J., Hussain, L., Chen, Y., Ku, C.S., Alatiyyah, M.H., Alateyah, S.A., & Por, L.Y. (2024). Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers. IEEE Access, 12, 82443-82468.
42. Ravichandra A., Shivakumara T. (2023). Detecting and Real Time Threat Analysis in Smart Grid Networks. Interantional journal of scientific research in engineering and management.
43. Almutairi L Deep Learning based Frameworks for Real-time Cyber Threat Analysis.Journal of Engineering and Applied Sciences. 2023.
44. Xie, L., Liao, Z., & Li, H. (2024). Research and Design of an Automated Security Event Analysis and Handling Framework Based on Threat Intelligence. Scalable Comput. Pract. Exp., 25, 1872-1881.
45. Mohammed, S.Y., & Aljanabi, M. (2024). From Text to Threat Detection: The Power of NLP in Cybersecurity. SHIFRA.
46. Karat, G., Kannimoola, J.M., Nair, N., Vazhayil, A., G, S.V., & Poornachandran, P. (2024). CNN-LSTM Hybrid Model for Enhanced Malware Analysis and Detection. Procedia Computer Science.
47. Beg, R., Pateriya, R.K., & Tomar, D.S. (2024). Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models. IEEE Access, 12, 175032-175055.
48. Iqubal, A., & Payal, A. (2024). Malware Detection Technique for Android Devices Using Machine Learning Algorithms. 2024 International Conference on Computing, Sciences and Communications (ICCSC), 1-6.
49. Pawar, J., Avhankar, M.S., Gupta, A., Barve, A., Patil, H., & Maranan, R. Enhancing Network Security: Leveraging Isolation Forest for Malware Detection. 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2024;230-234.
50. Zhao, G., Li, X., & Li, H. (2024). A Trusted Authentication Scheme Using Semantic LSTM and Blockchain in IoT Access Control System. International Journal on Semantic Web and Information Systems.
51. Vincent, A., & Anitha, A. (2023). A Survey on Deep Learning Approach for Identity Recognition Using Finger Vein Biometrics. 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), 971-975.
52. Mounnan, O., Manad, O., Boubchir, L., Mouatasim, A.E., & Daachi, B. (2022). Deep Learning-Based Speech Recognition System using Blockchain for Biometric Access Control. 2022 Ninth International Conference on Software Defined Systems (SDS), 1-2.
53. Sato, R., Kawaguchi, H., & Nakatani, Y. (2025). Malcoda: Practical and Stochastic Security Risk Assessment for Enterprise Networks. IEEE Transactions on Dependable and Secure Computing, 22, 1383-1399.
54. Ngampunprasert, T., & Ketcham, M. (2024). Risk Analysis of Device Within the Organization that are Vulnerable to Cyber Security Attacks with Artificial Intelligence. 2024 IEEE International Conference on Cybernetics and Innovations (ICCI), 1-6.
55. Mohanraj, G., Nadesh, R.K., J, J., S, A., & Sathiyamoorthi, V. (2025). Monitoring Incident Response Using Real-Time Analytics. 2024 International Conference on IT Innovation and Knowledge Discovery (ITIKD), 1-8.
56. Anggraeni, R., Alzami, F., Nurhindarto, A., Budi, S., Megantara, R.A., Rizqa, I., & Muslih, M. (2025). Clustering IT Incidents Using K-Means: Improving Incident Response Time in Service Management. Sinkron.
57. Safronov D.A., Katser Y.D., Zaitsev K.S. Anomaly Detection Using Autoencoders // International Journal of Open Information Technologies. 2022. No. 8. URL: https://cyberleninka.ru/article/n/poisk-anomaliy-s-pomoschyu-avtoenkoderov (accessed: 13.07.2025).
58. Hossain, F., Hasan, K., Amin, A., & Mahmud, S. (2024). Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication.
59. Faheem, M., Awais, M., Iqbal, A., & Zia, H. (2025). Enhancing It incident management with natural language processing and predictive analytics. International Journal of Science and Research Archive.
Review
For citations:
Potienko D.A., Chmykhalo D.S., Legonko O.L. Artificial Intelligence Technologies in Solving Information Security Problems. Herald of Dagestan State Technical University. Technical Sciences. 2026;53(1):157-169. (In Russ.) https://doi.org/10.21822/2073-6185-2026-53-1-157-169
Views:
143
JATS XML
Email this article 
