An Approach to the Implementation of a Neural Network for Cryptographic Protection of Data Transmission at UAV

Abstract

An approach to the implementation of a neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable, especially for onboard communication systems in unmanned aerial vehicles (UAV), because of its suitability for hardware implementation. In this study, we evaluate the possibility of building such a system in hardware implementation at FPGA. Onboard implementation-oriented information technology of real-time neuro-like cryptographic data protection with symmetric keys (masking codes, neural network architecture, and matrix of weighting coefficients) has been developed. Due to the pre-calculation of matrices of weighting coefficients and tables of macro-partial products and the use of tabular-algorithmic implementation of neuro-like elements and dynamic change of keys, it provides increased cryptographic stability and hardware–software implementation on FPGA. The table-algorithmic method of calculating the scalar product has been improved. By bringing the weighting coefficients to the greatest common order, pre-computing the tables of macro-partial products, and using operations of memory read, fixed-point addition, and shift operations instead of floating-point multiplication and addition operations, it provides a reduction in hardware costs for its implementation and calculation time as well. Using a processor core supplemented with specialized hardware modules for calculating the scalar product, a system of neural network cryptographic data protection in real-time has been developed, which, due to the combination of universal and specialized approaches, software, and hardware, ensures the effective implementation of neuro-like algorithms for cryptographic encryption and decryption of data in real-time. 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, and it requires 3053 logic elements and 745 registers. The execution time of exclusively software realization of NN cryptographic data encryption procedure using a NanoPi Duo microcomputer based on the Allwinner Cortex-A7 H2+ SoC was about 20 ms. The hardware–software implementation of the encryption, taking into account the pre-calculations and settings, requires about 1 msec, including hardware encryption on the FPGA of four 2-bit inputs, which is performed in 160 nanoseconds.

Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission

“…Routing problems are widely covered in the world's scientific literature. These are works of a system-wide nature, which analyze, for example, static and dynamic routing 2 of 16 or centralized and decentralized routing [2,3], as well as works that analyze, compare, and improve specific protocols under certain conditions. In particular, many works are devoted to the implementation of the widely used OSPF protocol in Wide Area Networks (WANs) [4][5][6][7][8][9].…”

“…To provide security at data transmission, different approaches are proposed. For example, a neural network for real-time cryptographic data protection oriented on unmanned aerial vehicles is presented in [16], for which data protection from damage or loss in the wireless channel between it and the remote control center is critical. The study in [17] proposes the concept of calculating the info-communication system reliability indicator of operating under the probable impact of typical cyber-physical attacks.…”

“…The main ones are limited computational abilities; ensuring requirements for the limited energy consumption, dimensions, and weight of the equipment; provision of data transmission in real time; ensuring the requirements for the cost; problems of encryption on regular transmissions; and a change in environmental conditions that affects the quality of data transmissions such as weather conditions, electromagnetic noise, temperature, etc. [16,[25][26][27].…”

Power Sources for Unmanned Aerial Vehicles: A State-of-the Art

“…An unmanned aerial vehicle (UAV), also referred to as a flying robot or drone, possesses the ability to operate independently or under remote control for specific missions [1]. In recent times, UAVs have garnered considerable attention as a result of advancements in microprocessors [2,3] and artificial intelligence (AI) [4][5][6][7], sensors [8][9][10][11][12][13][14], and the design [15][16][17][18] and manufacturing process [16][17][18][19][20], facilitating the creation of intelligent UAVs. The term "intelligent UAVs" refers to these drones' capacity to operate autonomously or semi-autonomously, making real-time decisions based on data from their sensors and AI algorithms.…”

Power Sources for Unmanned Aerial Vehicles: State - of - the Art Review

“…An unmanned aerial vehicle (UAV), also referred to as a flying robot or drone, possesses the ability to operate independently or under remote control for specific missions [1]. In recent times, UAVs have garnered considerable attention as a result of advancements in microprocessors [2,3] and artificial intelligence (AI) [4][5][6], sensors [7] as well as design [8][9][10][11][12][13][14][15] and manufacturing process [16][17][18][19][20] facilitating the creation of intelligent UAVs [21]. These technologically advanced UAVs offer a multitude of benefits, including cost efficiency and exceptional maneuverability, leading to their utilization in diverse military and civilian domains such as mine clearance, surveillance, delivery services, wireless connectivity, and agriculture [22,23].…”