Abstract
It is proposed to develop a neural-like system of real-time data protection and transmission using an integrated approach, which includes: research and development of theoretical bases of neural-like data encrypting-decryption and synthesis of noise-like codes; development of new algorithms for the calculation of basic neural operations and structures oriented on the VLSI technology for the implementation of neural-like elements; use of computer-aided design software. It is chosen to use the following principles to develop a neural-like data protection and transmission system: the variability of the equipment, modularity, pipelining and spatial concurrency, open-source software, specialization and adaptation of hardware and software, programmability of the architecture. Neural-like networks have been adapted based on the principal component analysis for neural network data encryption-decryption tasks. Means of calculating weights for neural network training using the principal component analysis have been developed. The structure of data protection and transmission system with the usage of noise-like codes has been developed, which provides high noise immunity, real-time operation and high technical and economic characteristics due to programmability of neural-like network architecture and generation of noise-like codes of different bit-widths. The tabular-algorithmic method of calculating scalar products has been improved, which provides fast calculation of the scalar product for input data with both fixed and floating point due to bringing the weights to the greatest common order and forming tables of macro-partial products for them. Neural-like methods of real-time data encryption and decryption have been developed, which provide their hardware implementation with high technical and economic indicators.
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Tsmots, I., Rabyk, V., Skorokhoda, O., Tsymbal, Y. (2021). Neural-like Real-Time Data Protection and Transmission System. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_8
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