CNNs-Based End-to-End Asymmetric Encrypted Communication System

Abstract

In this paper, we propose an asymmetric encrypted end-to-end communication system based on convolutional neural networks to solve the problem of secure transmission in the end-to-end wireless communication system. The system generates a key generator through a convolutional neural network as a bridge. The private and public keys establish a key pair relationship of arbitrary length sequence information. The transmitter and receiver consist of autoencoders based on convolutional neural networks. For data confidentiality requirements, we design the loss function of the end-to-end communication model based on a convolutional neural network. We also design bugs based on different predictions about the information the system eavesdropper has. Simulation results show that the system performs well on additive Gaussian white noise and Rayleigh fading channels. A legitimate party can establish a secure transmission under a designed communication system; an illegal eavesdropper without a key cannot accurately decode it.