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Mutual authentication for vehicular network in complex and uncertain driving

  • S.I. : Brain- Inspired computing and Machine learning for Brain Health
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Abstract

With the rapid development of big data and cloud computing, vehicular is connected to the Internet in the complex and uncertain driving environment. The rapid growth of the types of services used by vehicles has made the problem of inefficient of traditional driving environment architecture more and more obvious. The vehicle has to register and remember a large number of usernames and passwords to each server. Authentication schemes for multi-server architectures have been proposed and applied to a wide range of areas, but there has been little research on the Internet of vehicles. The long-term evolution for vehicle (LTE-V) is a wireless network architecture and can be used for cooperative communication in vehicular network. Communications and authentication for LTE-V have the high request in complex and uncertain driving environment. To meet the needs of complex and uncertain driving environments, this paper proposes a novel mutual authentication and the key agreement scheme (LEANDER) under multi-server architecture. In this scheme, elliptic curve is used to reduce the computational complexity, and a more concise authentication method is constructed. Random anonymity supports multi-server for two-way authentication and key agreement, so as to effectively protect the privacy of the vehicle. Moreover, it can be use BAN logic to prove and analyze the effectiveness of this scheme. The performance analysis results show that the proposed mutual authentication scheme is effective and more secure than other state-of-the-art methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61871039), the Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (Grant No. IDHT20170511), the Talents Cultivation and Cooperation Oriented to Intelligent Vehicle Industrialization (Grant No. UK-CIAPP\324), Newton Fund Project supported by Royal Academy of Engineering of UK, the Joint Funds of National Natural Science Foundation of China and Xinjiang (Grant No. U1603261), and the National Key Technology R&D Program (Grant No. 2015BAH55F03).

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Authors and Affiliations

  1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China

    Cheng Xu & Hongzhe Liu

  2. Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Cheng Xu & Yong Zhang

  3. Communication and Information Centre, State Administration of Work Safety, Beijing, China

    Pengfei Wang

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  1. Cheng Xu
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  2. Hongzhe Liu
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  3. Yong Zhang
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  4. Pengfei Wang
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Correspondence to Hongzhe Liu.

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Xu, C., Liu, H., Zhang, Y. et al. Mutual authentication for vehicular network in complex and uncertain driving. Neural Comput & Applic 32, 61–72 (2020). https://doi.org/10.1007/s00521-018-3743-3

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  • DOI: https://doi.org/10.1007/s00521-018-3743-3

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