NEUROCRYPTOGRAPHIC INFORMATION PROTECTION

Arina V. Nikishova

Candidate of Sciences (Engineering), Associate Professor,

Department of Information Security,

Volgograd State University

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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation

Ekaterina M. Glybina

Lecturer,

Volgograd Branch of the Moscow State University of Humanities and Economics

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Poddubnogo St, 15, 400040 Volgograd, Russian Federation

Mikhail Yu. Umnitsyn

Head of the Analytical Group and Information Security Assessment,

EC Regional Systems

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Sotsialistheskaya St, 17, 400001 Volgograd, Russian Federation

Abstract. Along with the rapid development of digital communication technologies, which allowed to transmit 
messages in different forms over the network, the need to protect transmitted data from access by third 
parties has increased. One of the main ways to protect data is encryption. The main reason for encryption is 
that users must be aware of encryption methods and keys, without which information is meaningless in the 
form of symbols. As the efficiency of cryptanalysis methods has increased with computational performance, 
there has been a need for more sophisticated approaches to encryption. In particular, the use of such a 
promising approach as neural networks for data encryption – neurocryptography. Due to the fact that the 
increased power of technological tools continues to grow, today’s neural networks have been used in practice.
Any encryption algorithm is based on generating different variants of a distorted code that can be recognized 
or reconstructed by a neural network with specified characteristics, and includes the following stages: 
preliminary, performing preliminary data processing and formation of a training sample; formation of a neural 
network, including training; and the main one, performing encryption or decryption. The article deals with the 
issue of increasing the efficiency of data protection by means of neurocryptography. Improving efficiency is 
achieved by selecting a group of cryptographic primitives, the implementation of which in the form of a neural 
network is the most effective. Efficiency in this case means the ratio of data encryption speed to the time of 
formation of the neural network.
Key words: encryption, neural network, replacement, permutation, block single permutation.

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