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Copyright © 2021 Junfeng Miao et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

When mobile network enters 5G era, 5G networks have a series of unparalleled advantages. Therefore, the application of 5G network technology in the Internet of Vehicles (IoV) can promote more intelligently vehicular networks and more efficiently vehicular information transmission. However, with the combination of 5G networks and vehicular networks technology, it requires safe and reliable authentication and low computation overhead. Therefore, it is a challenge to achieve such low latency, security, and high mobility. In this paper, we propose a secure and efficient lightweight authentication protocol for vehicle group. The scheme is based on the extended chaotic map to achieve authentication, and the Chinese remainder theorem distributes group keys. Scyther is used to verify the security of the scheme, and the verification results show that the security of the scheme can be guaranteed. In addition, through security analysis, the scheme can not only effectively resist various attacks but also guarantee security requirements such as anonymity and unlinkability. Finally, by performance analysis and comparison, our scheme has less computation and communication overhead.

Details

Title
A Secure and Efficient Lightweight Vehicle Group Authentication Protocol in 5G Networks
Author
Miao, Junfeng 1   VIAFID ORCID Logo  ; Wang, Zhaoshun 1   VIAFID ORCID Logo  ; Xue Miao 1   VIAFID ORCID Logo  ; Xing, Longyue 1   VIAFID ORCID Logo 

 The School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China 
Editor
Weizhi Meng
Publication year
2021
Publication date
2021
Publisher
John Wiley & Sons, Inc.
e-ISSN
15308677
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2578641746
Copyright
Copyright © 2021 Junfeng Miao et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.