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      Password-Guessing Attack-Aware Authentication Scheme Based on Chinese Remainder Theorem for 5G-Enabled Vehicular Networks

      , , ,
      Applied Sciences
      MDPI AG

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          Abstract

          The new fifth-generation (5G) cellular networks dramatically improve the speed of message transmissions. Most existing authentication schemes that secure 5G communication rely heavily on the vehicle’s tamper-proof device (TPD) and roadside units (RSUs) to store the system’s master key. However, it only takes a single compromised TPD to render the whole system insecure. We propose a password-guessing attack-aware authentication scheme based on the Chinese Remainder Theorem (CRT) to secure inter-vehicle communication on 5G-enabled vehicular networks to address this issue. The trusted authorities (TAs) in the proposed scheme generate and broadcast new group keys to the vehicles assisted by CRT. Moreover, since the system’s master key does not need to be preloaded, the proposed scheme only requires realistic TPDs. The proposed scheme overcomes password-guessing attacks and guarantees top-level security for entire 5G-enabled vehicular networks. The security analysis indicates that the proposed scheme is secure against adaptive chosen-message attacks under the random oracle model and meets the security requirements of a 5G-enabled vehicular network. Since cryptographic operations based on elliptic curve cryptography are employed, the performance evaluation shows that the proposed scheme outperforms the eight existing schemes in terms of computation and communication costs.

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          What Will 5G Be?

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            Security Arguments for Digital Signatures and Blind Signatures

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              Securing vehicular ad hoc networks

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                Author and article information

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                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                February 2022
                January 27 2022
                : 12
                : 3
                : 1383
                Article
                10.3390/app12031383
                d1658156-796d-4127-903b-ef9f878704bc
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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