Dataset of Cryptographic Algorithms for UAV Image Encryption based on Artificial Neural Networks

Abstract

Equipped with the latest image processing systems, sensors, and high-resolution cameras, they can conduct real-time aerial photography, monitor enemy activity, and gather critical intelligence without putting the military at risk. UAVs make it possible to conduct long-term operations in conditions of secrecy, providing commanders with valuable information for making strategic decisions. However, the issue of ensuring the confidentiality of critical data (images) collected using UAVs remains unresolved. In this paper universal dataset of cryptographic algorithms is proposed and it uses a neural network model to select the optimal encryption algorithm. To form such a dataset, it was necessary to evaluate the speed and security of the cryptographic algorithms as well as other important parameters. The developed dataset in synthesis with a neural network model can be used to select the optimal crypto algorithm. In further research, the authors plan to determine the criteria for using the generated dataset by neural networks and develop a knowledge base for neural network training.