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基于CSI的对称加密端到端通信系统

安永丽 李宗瑞 白海飞 米雪玉

河北科技大学学报2025,Vol.46Issue(4):405-415,11.
河北科技大学学报2025,Vol.46Issue(4):405-415,11.DOI:10.7535/hbkd.2025yx04006

基于CSI的对称加密端到端通信系统

CSI-based symmetric encryption end-to-end communication system

安永丽 1李宗瑞 2白海飞 2米雪玉3

作者信息

  • 1. 华北理工大学人工智能学院,河北 唐山 063000||唐山市空地智慧交通重点实验室,河北 唐山 063000
  • 2. 华北理工大学人工智能学院,河北 唐山 063000
  • 3. 唐山市空地智慧交通重点实验室,河北 唐山 063000||华北理工大学应急管理与安全工程学院,河北 唐山 063000
  • 折叠

摘要

Abstract

To address the issue of information leakage caused by key theft during transmission,a symmetric encryption end-to-end communication system based on channel state information(CSI)was proposed.The proposed system employed convolutional neural networks to construct the transmitter,receiver,and key generator,optimizing the encoding and decoding process in an end-to-end manner.At the same time,it leveraged the reciprocity,random time-variability,and spatial uniqueness of wireless channels to measure the CSI and generate keys from legitimate users,encrypting the original information.The simulation results demonstrate that under Rayleigh fading,Rician fading,and frequency-selective multipath fading channels,the bit error rates(BER)of the proposed system is lower than that of baseline models such as the symmetric encryption system based on deep convolutional generative adversarial networks within the tested signal-to-noise ratio(SNR)range.In high-SNR scenarios,the improvement of BER can reach 18 dB.Additionally,in four attack scenarios,such as brute force and key leakage attacks,the BER of eavesdropper is approximately 0.5,indicating an inability to decrypt the information.The proposed system eliminates the need for key distribution,reducing the BER while enhancing eavesdropping resistance,thus providing a novel approach for secure communication.

关键词

无线通信技术/端到端通信/物理层安全/自编码器/对称加密

Key words

wireless communication technology/end-to-end communication/physical layer security/AutoEncoder/symmet-ric encryption

分类

信息技术与安全科学

引用本文复制引用

安永丽,李宗瑞,白海飞,米雪玉..基于CSI的对称加密端到端通信系统[J].河北科技大学学报,2025,46(4):405-415,11.

基金项目

国家科技部重点研发专项(2017YFE0135700) (2017YFE0135700)

河北省高层次人才工程项目(A201903011) (A201903011)

河北科技大学学报

OA北大核心

1008-1542

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