Neural Cryptographic Information Security System of Recurrent Convergent Neural Networks

. The purpose: to construct an algorithm for information transformation by recurrent convergent neural networks with a given set of local minima of the energy functional for its subsequent application in the field of information security. Method: system analysis of the existing neural network paradigms that can be used for classification of images. Neural cryptographic system synthesis is with analogy methods, recurrent convergent neural networks, noise- resistant encoding and block ciphers algorithms. The result: a promising neural cryptographic system is proposed that can be used to develop an algorithm for noise-resistant coding, symmetric or stream data encryption based on the generation of various variants of the distorted image representing the sequence of bits to mask the original message. An algorithm for block symmetric data encryption based on Hopfield-type neural networks has been created. Key information includes information on the selected (using radial basic functions) structural characteristics of the potential with a given set of energy minima, which determines the dynamics of the neural network as a potential dynamic system, whose attractors are symbols (several symbols) of the alphabet of the input text. The size of the key depends on the power of the alphabet of the original message and the form of representation of the energy functional. The presented neural cryptographic system can also be used in the authentication system.