基于深度学习的车联网无线密钥生成系统
Wireless key generation system for internet of vehicles based on deep learning
汪涵 1陈立全 1王忠民 2陆天宇1
作者信息
- 1. 东南大学网络空间安全学院,江苏南京 211189
- 2. 江苏省人民医院(南京医科大学第一附属医院),江苏南京 210029
- 折叠
摘要
近年来,车联网技术的应用越来越广泛,并因其通信的高复杂和点对点特性备受关注.敏感而重要的车辆信息在不同的车联网设备之间传递,为了确保通信安全,有必要在合法节点之间建立安全可靠的轻量级密钥,从而对关键信息进行加密和解密.传统的密钥生成方案,在车联网中存在不灵活、不能扩展的缺陷.基于无线信道的物理层密钥生成技术因其轻量级的特性受到欢迎,并且以信息论安全性作为理论基础.在车联网环境中,设备运动速度对生成密钥的自相关性存在影响,传统的信道建模方法需要改进.同时,车联网对生成的无线密钥的随机性、一致性提出更高的要求.对基于无线物理层的密钥生成系统进行了研究,提出基于视线和多径衰落的信道建模,反映了车辆速度对自相关性的影响.提出基于累积分布函数的差分量化方法,改进了生成密钥的随机性.提出一种基于神经网络自动编码器的信息协调方案,实现可靠性和保密性的动态平衡.相较于Slepian-Wolf低密度奇偶检验码实现的方案,所提方案将比特不一致率降低30％左右.
Abstract
In recent years,the widespread application of internet of vehicles technology has garnered attention due to its complex nature and point-to-point communication characteristics.Critical and sensitive vehicle information is transmitted between different devices in internet of vehicles,necessitating the establishment of secure and reliable lightweight keys for encryption and decryption purposes in order to ensure communication security.Traditional key generation schemes have limitations in terms of flexibility and expandability within the vehicle network.A popular alternative is the physical layer key generation technology based on wireless channels,which offers lightweight characteristics and a theoretical basis of security in information theory.However,in the context of internet of vehi-cles,the movement speed of devices impacts the autocorrelation of generated keys,requiring improvements to tradi-tional channel modeling methods.Additionally,the randomness and consistency of generated wireless keys are of higher importance in applications in internet of vehicles.This research focused on a key generation system based on the wireless physical layer,conducting channel modeling based on line-of-sight and multipath fading effects to re-flect the impact of vehicle speed on autocorrelation.To enhance the randomness of key generation,a differential quantization method based on cumulative distribution function was proposed.Furthermore,an information reconcil-iation scheme based on neural network auto-encoder was introduced to achieve a dynamic balance between reliabil-ity and confidentiality.Compared to the implementation of Slepian-Wolf low-density parity-check codes,the pro-posed method reduces the bit disagreement rate by approximately 30％.
关键词
累积分布函数/自动编码器/Slepian-Wolf编码/车联网Key words
cumulative distribution function/autoencoder/Slepian-Wolf coding/internet of vehicles引用本文复制引用
基金项目
国家重点研发计划(2020YFE0200600)
出版年
2024
国家科技期刊平台
NSTL
万方数据