The "model of successive geometric transformations" paradigm has been adapted for the implementation of parallel-streaming neural network encryption-decryption of data in real time. A model and structure of a parallel-streaming neural-like element for the mode have been developed. Keywords-intensive data stream; neural networks; geometric transformations model I. INTRODUCTION The latest information technologies are becoming global in the modern world. Their development and development of communications provides ever-widening opportunities for access to information resources and the transfer of large amounts of data for unlimited distances. In the context of the intensive development of the market for information products and services, information has become a full-fledged product that has its own consumer properties and cost characteristics. The widespread introduction of information technology makes a relevant problem for the protection of the transmission of information using cryptographic methods that provide encryption of the ready-to-transmit information. The encrypted information is transmitted by a communication channel to an authorized user, who, after receiving it, performs decryption using a reverse transformation. Cryptographic transformations are carried out using special algorithms. In order to encrypt and decrypt real-time data streams, it is suggested to use neural-like network algorithms, the key in which is the network architecture, weighting factors, and masking codes.
Background::
The modern stage of the development data protection and transmission systems is characterized by an extension of the application areas, most of which require encryption (decryption) and encoding (decoding) in real time on hardware that satisfy the restrictions on the dimensions, energy, cost and development time. In this connection, the problem of choosing an elemental base for the synthesis of neuro-like structures of symmetric encryption-decryption of data and means of encoding-decoding when transmitting data using noise-like codes becomes especially relevant.
Methods::
Software implementation of data protection and transmission using noise-like codes involves the use of universal and functionally oriented microprocessors. In the programmatic implementation of neuro-like algorithms for encryption and decryption of data, computational processes are mostly expandable in time with a large volume of information transfer between the RAM and operating devices.
Results::
In this work were performed series of element base selection simulations and synthesis of DPTS, with various technical parameters. During each simulation, the microcontroller, memory blocks, operating nodes and communication interfaces are selected. The simulation was performed on a MacBook Pro 2015 with a Core i7 processor and 16 GB of RAM.
Conclusion::
It has been proposed to implement the real-time DPTS using noise-like codes by combining universal and special approaches based on the processor core supplemented by hardware with a table-algorithmic implementation of encryption (decryption) processes. The method of selection of the elemental base for synthesis of DPTS has been improved. A simulation model of element base selection for the synthesis of the DPTS has been developed. The system of synthesis of data protection and data transmission using noise-like codes has been developed.
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