Economic analysis of data protection in systems with complex architecture using neural network methods

Economic Annals-ХХI: Volume 185, Issue 9-10, Pages: 178-188

Citation information:
Nelub, V., Gantimurov, A., & Borodulin, A. (2020). Economic analysis of data protection in systems with complex architecture using neural network methods. Economic Annals-XXI, 185(9-10), 178-188. doi:

Vladimir Nelub
PhD (Engineering),
Bauman Moscow State Technical University;
Interdisciplinary Engineering Centre «Composites of Russia» of the Bauman Moscow State Technical University
1 Lefortovskaya Embankment, Moscow, 105005, Russian Federation

Andrey Gantimurov
PhD Student (Economics),
Bauman Moscow State Technical University;
Chief Technology Officer,
1 Lefortovskaya Embankment, Moscow, 105005, Russian Federation

Alexey Borodulin

PhD (Engineering),
Bauman Moscow State Technical University;
Deputy Director,
Interdisciplinary Engineering Centre «Composites of Russia» of the Bauman Moscow State Technical University
1 Lefortovskaya Embankment, Moscow, 105005, Russian Federation

Economic analysis of data protection in systems with complex architecture using neural network methods

Abstract. Introduction. In the United States, Europe, and Asia, there have been spikes in cyber attacks on protected and confidential information (including bank data, personal data, and confidential business information) over the period 2000-2020.

Data protection in systems with complex architectures is a complex and non-trivial solution, which is suitable for flexible self-tuning and self-learning tools, such as neural networks as state modern studies.

The described above state of things stipulates the importance and topicality of the direction of our research.

Our research raises the question about the economic study of the data but attempts to create a mathematical apparatus to cause a loss of data in digital form, not made before.

The purpose of the study is to propose and test the calculation of the economic value of the data loss and economic benefit from data protection with multi-level neural networks.

Results. Nowadays, almost every business that has access to the Internet has a threat of losing protected data, including banking data. To prevent this, the company must comply with the data protection management regulations. As an example, we studied the process of neural network reaction to an attack and its detection, when two types of attacks are threatened: botnet and UDP flood. Due to the result of a quick response to detect suspicious activity, neural network methods are excellent for use in economic analysis, because the neural network logs every event for every millisecond.

Conclusions. As a result of using neural network tools for economic analysis of data protection in modern systems with complex architecture, we were able to obtain stable results of responding to an emerging attack in order to obtain protected data. Such protection is a preventive measure for the possibility of reducing business losses in the event of data loss. As we can see, the management of protection of systems with complex architecture is necessary for each company with the specified data level.

Keywords: Information Security; Data Protection; Neural Network; Economic Analysis; Cyber Attack

JEL Classіfіcatіon: C15; L78

Acknowledgements and Funding: The authors received no direct funding for this research.

Contribution: The authors contributed equally to this work.



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