Kharlamov Pavel S. (Research Assistant of the Department for additional education, lecturer at the Department of secondary vocational education, Smolensk branch of RANEPA
master's student, branch of the Federal State Budgetary Educational Institution of Higher Education "National Research University "MPEI" in Smolensk
)
Kharlamova Olga E. (Lecturer at the Department of secondary vocational education, Smolensk Branch of RANEPA)
Lavrova Elena V. (PhD (Econ.), Associate Professor;
Dean of the Faculty of Law, Smolensk Branch of RANEPA
)
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The object of the study is the information security of business processes of transferring data subject to trade secrets in complex economic systems, such as territorial scientific and industrial clusters. The subject of the study is to develop a method of user identification based on Siamese neural networks as a module (factor) of multifactor authentication to ensure information security of business processes of data transmission, with the regime of trade secrets, in complex economic systems. The relevance of the problems stems from the need to improve the existing mechanisms and tools for information security in the transfer of data containing trade secrets between organizations within a complex economic system, such as a territorial scientific and industrial cluster, as the existing methods have a number of significant vulnerabilities, such as a «dictionary attack», are characterized by low accuracy in carrying out user identification. The aim of the study is to develop a method of user identification based on Siamese neural networks as a module of multifactor authentication for complex economic systems. A methodological framework for the application of the method of user identification based on Siamese neural networks for complex economic systems is proposed. A method for user identification based on Siamese neural networks as a module for multifactor authentication of complex economic systems such as territorial scientific and industrial clusters is developed. The conclusion about the effectiveness of the developed method of user identification based on Siamese neural networks as a module of multifactor authentication in providing information security of data transfer processes, constituting trade secrets in complex economic systems is made.
Keywords:information security; multifactor authentication; complex socio-economic systems; territorial science and industry clusters; data validation; artificial neural networks; Siamese neural networks; business secrets data
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Citation link:
Kharlamov P. S., Kharlamova O. E., Lavrova E. V. SIAMESE NEURAL NETWORK-BASED USER IDENTIFICATION AS A MULTIFACTOR AUTHENTICATION MODULE FOR COMPLEX ECONOMIC SYSTEMS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2024. -№05. -С. 166-172 DOI 10.37882/2223-2966.2024.05.36 |
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