Data Authentication Based on Discrete Random Convolutional Neural Network

Abstract

Data authentication is an efficient way to enhance the security of data transmission, especially for the resource-limited terminal devices in edge computing system. Authentication through physical layer channel characteristics is a lightweight method compared with that based on cryptography. With the popularity of deep learning, this paper proposed a discrete random convolutional neural network applicable for the channel matrix, which extracts more feature information in convolution step without increasing the required memory by discrete random movement of the convolutional kernel. It’s suitable for protecting the data transmission between micro terminals in edge computing.