Methods for semantic analysis of the state of the process of functioning of a system for detecting, preventing and eliminating the consequences of computer attacks
https://doi.org/10.21822/2073-6185-2024-51-4-164-170
Abstract
Objective. The aim of the study is to determine the most effective method of semantic analysis of the state of the process of detection, prevention and elimination of consequences of computer attacks. Method. The study was conducted based on the methods of semantic analysis of the state of the process of SOPKA operation. Result. A structural model of the system of semantic analysis of the state of the process of SOPKA operation is proposed, which is capable of providing an analysis of the state of the process of detection, prevention and elimination of consequences of computer attacks. The most effective method for solving the problem of semantic analysis of the state of the process of SOPKA operation is machine learning using ontological modeling. Conclusion. The results indicate the need for further research of the system of semantic analysis of the state of the process of detection, prevention and elimination of consequences of computer attacks.
About the Authors
V. O. ShablyaRussian Federation
Vladimir O. Shablya, Postgraduate Student
4 Krasina St., Krasnodar 350063
S. A. Konovalenko
Russian Federation
Sergey A. Konovalenko, Doctoral Student, Cand. Sci. (Eng), Assoc. Prof.
4 Krasina St., Krasnodar 350063
E. O. Orlov
Russian Federation
Egor O. Orlov, 4th year Student
4 Krasina St., Krasnodar 350063
References
1. Konovalenko S.A., Korolev I.D., Shablya V.O. Analysis of the functioning of the departmental segment of the system for detecting, preventing and eliminating the consequences of computer attacks on the critical information infrastructure of the Armed Forces of the Russian Federation. Information security – an urgent problem of our time. Improving educational technologies for training specialists in the field of information security: materials of the XXIII Allround. interagency. NTC, Krasnodar, 2021 / ed., Doctor of Technical Sciences, prof. A.V. Krupenin. – Krasnodar: KVVU, 2021; 2: 80-90. (In Russ)
2. Shablya V.O., Konovalenko S.A., Edunov R.V. Analysis of the process of functioning of SIEM systems // Esco [Electronic resource]: Electronic periodical "E-scio.ru " - E-mail no. FS77-66730 - Access mode: http:e-scio.ru/wp-content/uploads/2022/05/Шабля-В.-О.-Коноваленко-С.-А.-Едунов-Р.-В.pdf (In Russ)
3. Konovalenko S.A., Shablya V.O., Titov G.O. Analysis of methods for monitoring the state of the process of functioning of complex technical systems [Electronic resource]. The science sphere. 2021;12 (2): 234. (In Russ)
4. Kulnevich A.D., Koshechkin A.A., Karev S.V., Zamyatin A.V. An approach to the recognition of named entities by the example of technological terms in a limited training sample. Bulletin of Tomsk State University. Management, Computer Engineering and Computer Science, (58):71-81. (In Russ)
5. Lesnikov, S.V. Thesaurus as a reflection of the consistency of language. Bulletin of the Chelyabinsk State University, 28; 52-61. (In Russ)
6. Osokina S.A. The network model of the language thesaurus: features of construction. Siberian Journal of Philology, 3; 191-198. (In Russ)
7. Lazutchenkova, E. A. Pragmatic analysis in lexical semantics. Polylingualism and Transcultural Practices, 1: 62-65. (In Russ)
8. Bobrova M.B., Mastilin A.E. Machine learning in cybersecurity. Scientific Interdisciplinary Research, 2: 24-29. (In Russ)
9. Magzhanova A.T. Integration of information sources using cluster analysis according to the machine learning scheme without a teacher. Theory and Practice of Modern Science, 6 (24:1037-1040. (In Russ)
10. Maksyutin P.A., Shulzhenko S.N. Review of text classification methods using machine learning. Engineering Bulletin of the Don, 12 (96):1-9. (In Russ)
11. Zhilenkov A.A., Silkin A.A., Serebryakov M.Yu., Kolesova S.V. Comparative analysis of systems deep reinforcement learning and teacher-led learning systems. Proceedings of Tula State University. Technical Sciences, 10:109-112. (In Russ)
12. Stolyarov A.S., Rajabov T.R. The development of AI, deep and machine learning. Theory and Practice of Modern Science, 8 (38): 70-80. (In Russ)
13. Papusha S.I. Ontology and graph databases. Problems of Economics and legal practice, (3), pp. 268-272. (In Russ)
14. Dobrov B.V., Ivanov V.V., Lukashevich N.V., Solovyov V. D. Ontologies and thesauruses: models, tools, applications: textbook [Electronic resource] / B. V. Dobrov, V.V. Ivanov, N.V. Lukashevich, V.D. Solovyov. — 2nd ed. (electronic) — Electron, dan. and progr. (3 Mb.) — Moscow: Internet University of Information Technologies; Saratov: University Education, 2017. (In Russ)
15. Hekalo T.V. The study of personal meanings of physico-chemical objects by the method of semantic differential. Questions of Psycholinguistics, 2016:256-265. (In Russ)
Review
For citations:
Shablya V.O., Konovalenko S.A., Orlov E.O. Methods for semantic analysis of the state of the process of functioning of a system for detecting, preventing and eliminating the consequences of computer attacks. Herald of Dagestan State Technical University. Technical Sciences. 2024;51(4):164-170. (In Russ.) https://doi.org/10.21822/2073-6185-2024-51-4-164-170