基于深度学习的车联网无线密钥生成系统

doi:10.11959/j.issn.2096-109x.2024012

Abstract

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 vehicles, the movement speed of devices impacts the autocorrelation of generated keys, requiring improvements to traditional 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 reflect 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 reconciliation scheme based on neural network auto-encoder was introduced to achieve a dynamic balance between reliability and confidentiality.Compared to the implementation of Slepian-Wolf low-density parity-check codes, the proposed method reduces the bit disagreement rate by approximately 30%.

Key words: cumulative distribution function, autoencoder, Slepian-Wolf coding, internet of vehicles

CLC Number:

TP309

Han WANG, Liquan CHEN, Zhongmin WANG, Tianyu LU. Wireless key generation system for internet of vehicles based on deep learning[J]. Chinese Journal of Network and Information Security, 2024, 10(1): 102-111.

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References 18

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随机性测试	二元矩阵秩检验测试值	二元矩阵秩检验测通过情况重叠模块匹配检验测试值	重叠模块匹配检验通过情况	线性复杂度检验测试值	线性复杂度检验通过情况	随机游动检验测试值	随机游动检验通过情况	随机游动状态频率检验测试值	随机游动状态频率检验通过情况
单门限量化	2.338×10^-6	不通过 0.000 2	不通过	1.360×10^-37	不通过	0.699 9	通过	0.617	通过
基于累积分布函数的值域量化	2.338×10^-6	不通过 0.000 18	不通过	1.5247×10^-8	不通过	0.776 5	通过	1	通过
基于累积分布函数的差分量化	0.413 5	通过0.126 1	通过	0.410 8	通过	0.849 1	通过	1	通过

Wireless key generation system for internet of vehicles based on deep learning

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