Version 1
: Received: 12 April 2023 / Approved: 12 April 2023 / Online: 12 April 2023 (07:32:24 CEST)
Version 2
: Received: 14 April 2023 / Approved: 14 April 2023 / Online: 14 April 2023 (07:13:14 CEST)
How to cite:
Tsmots, I.; Teslyuk, V.; Łukaszewicz, A.; Lukashchuk, Y.; Kazymyra, I.; Holovatyy, A.; Opotyak, Y. Neural Network Technology for Cryptographic Protection of Data Transmission at UAV. Preprints2023, 2023040252. https://doi.org/10.20944/preprints202304.0252.v1
Tsmots, I.; Teslyuk, V.; Łukaszewicz, A.; Lukashchuk, Y.; Kazymyra, I.; Holovatyy, A.; Opotyak, Y. Neural Network Technology for Cryptographic Protection of Data Transmission at UAV. Preprints 2023, 2023040252. https://doi.org/10.20944/preprints202304.0252.v1
Tsmots, I.; Teslyuk, V.; Łukaszewicz, A.; Lukashchuk, Y.; Kazymyra, I.; Holovatyy, A.; Opotyak, Y. Neural Network Technology for Cryptographic Protection of Data Transmission at UAV. Preprints2023, 2023040252. https://doi.org/10.20944/preprints202304.0252.v1
APA Style
Tsmots, I., Teslyuk, V., Łukaszewicz, A., Lukashchuk, Y., Kazymyra, I., Holovatyy, A., & Opotyak, Y. (2023). Neural Network Technology for Cryptographic Protection of Data Transmission at UAV. Preprints. https://doi.org/10.20944/preprints202304.0252.v1
Chicago/Turabian Style
Tsmots, I., Andriy Holovatyy and Yurii Opotyak. 2023 "Neural Network Technology for Cryptographic Protection of Data Transmission at UAV" Preprints. https://doi.org/10.20944/preprints202304.0252.v1
Abstract
The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.
Keywords
neural network technology; cryptographic protection; UAV; UAS, onboard system; encryption; decryption; tabular-algorithmic method; scalar product; real time
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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