Email this article Determination of Parameters of Hidden Threats of Early Detection in Information Systems for Machine Learning Tasks
- Authors: Zolotukhina M.A.1, Zykov S.V.1,2
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Affiliations:
- Russian Technological University – MIREA
- Higher School of Economics
- Issue: Vol 10, No 3 (2023)
- Pages: 83-91
- Section: MATHEMATICAL AND SOFTWARE OF COMPUTЕRS, COMPLEXES AND COMPUTER NETWORKS
- URL: https://journal-vniispk.ru/2313-223X/article/view/252168
- DOI: https://doi.org/10.33693/2313-223X-2023-10-3-83-91
- EDN: https://elibrary.ru/RRZMLN
- ID: 252168
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Abstract
The purpose of the analysis is to identify new signs in which there is a probability of the presence of components of hidden threats in the system or a forecast of possible states of inactivity of system modules. The diversity of the software used and the problems that arise at the same time are described. The study is carried out under the conditions of creating a simulation model in Anylogic used to determine fault criteria. The detected dependencies are confirmed by output data in the form of graphs. Certain dependencies and features are a contribution for future research and publications, and the data are also applicable to the knowledge base being developed. The created query processing model showed the dependence of the characteristics of the input parameters on the time and noise of the data stream. The analysis also confirms the presence of a malfunction in the data processing flow. The existing solutions for detecting attacks are based on the introduction of software and hardware and on measures of a general nature of protection. In order to establish a hidden threat, such schemes may and will work effectively, but in conditions of long-term hidden threats, an assessment of the situation at different levels is needed, an analysis of signs of all stages of the malfunction state, the use of a predictive model and it is not enough to use disparate means of protection in the form of software, antiviruses, etc. Research in the field of finding dependencies and parameters for predicting cyberattacks on information systems is relevant due to the increasing complexity and frequency of cyberattacks. This allows you to promptly warn about possible threats, take measures to protect information systems, minimize economic losses and develop analytical capabilities in the field of cybersecurity. This direction retains its stability and uniqueness in the field of process research, namely the ability to learn and carry out in-depth analysis of parametric data. implementation of anomaly search within the intrusion detection system.
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##article.viewOnOriginalSite##About the authors
Maria A. Zolotukhina
Russian Technological University – MIREA
Author for correspondence.
Email: rtu_mary@mail.ru
ORCID iD: 0000-0001-9819-7435
graduate student
Russian Federation, MoscowSergey V. Zykov
Russian Technological University – MIREA; Higher School of Economics
Email: szykov@hse.ru
ORCID iD: 0000-0002-2115-5461
SPIN-code: 4149-8264
Doctor of Engineering, Associate Professor; Professor, Chief Researcher at the Department of Business Informatics of the Graduate School of Business
Russian Federation, Moscow; MoscowReferences
- Zykov S.V. Semantic data integration for security and integrity of corporate systems. Information Technology Security. 2009. No. 3. Pp. 16–19. (In Rus.)
- Isoboev Sh.I., Vezarko D.A., Chechelnickij A.S. Intelligent wireless network security monitoring system based on machine learning. Economics and Quality of Communication Systems. 2022. No. 1. Pp. 44–48. (In Rus.).
- Shananin V.A. The use of artificial intelligence systems in the protection of information. Innovation and Investment. 2022. No. 11. Pp. 201–205. (In Rus.).
- Avetisjan A.I. Cybersecurity in the context of artificial intelligence. Bulletin of the Russian Academy of Sciences. 2022. No. 92. Pp. 1119–1123. (In Rus.).
- Hasti T., Tibshirani R., Fridman J. Elements of statistical training. Data mining, logical inference and forecasting. 2th ed. Springer, 2009. 745 p.
- Luizi J.V. Pragmatic enterprise architecture: strategies for transforming information systems in the era of big data. Walthem, MA: Morgan Kaufmann, 2014. 372 p. ISBN: 9780128005026.
- Bachotti A. Stability and control of linear systems. Cham: Springer, 2019. 200 p. ISBN: 978-3-030-02405-5.
- Gudfellou Ja., Bendzhio I., Kurvill A. Deep learning. 2th ed., cor. Moscow: DMK Press, 2018. 652 p.
- Hasti T., Tibshirani R. Fundamentals of statistical training: Data mining, logical inference and forecasting. 2th ed. Springer, 2020. 770 p.
- Chzhan L., Zigler B.P., Lajli Ju. Development of models for modeling. Elsevier, 2019. 453 p.
- Hinkel G. NMF: Multiplatform modeling framework: International Conference on the Theory and Practice of Model Transformations. Cham: Springer, 2018. Pp. 184–194.
- Dej R., Rjej G., Balas V.E. Stability and stabilization of linear and fuzzy systems with time delay. An approach with linear matrix inequalities. New-York: Springer, 2018. 274 p.
- Brink H. Richards J. Feverolf M. Machine learning in the real world. St. Petersburg: Piter, 2017. 336с. ISBN: 978-5-496-02989-6.
- Burnashev R.A. et al. Research on the development of expert systems using artificial intelligence: International Conference on Architecture and Technologies of Information Systems. Cham: Springer, 2019. Pp. 233–242.
- Vitten I.H., Frjenk J., Holl M.A., Pjel K.J. Data mining. Practical tools and methods of machine learning. 4th ed. Elsevier, 2017. 621 p. ISBN: 0120884070.
- Sholle F. Deep learning in Python. St. Petersburg: Piter, 2018. 400 p.
- Butakova M.A., Chernov A.V., Govda A.N. et al. The method of knowledge representation for the design of an intelligent situational information system. Materials of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). A. Abraham, S. Kovalev, V. Tarasov, V. Snasel, A. Suhanov (eds.). Achievements in the Field of Intelligent Systems and Computing. 2018. No. 875. Pp. 225–235. (In Rus.) doi: 10.1007/978-3-030-01821-4_24.
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