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Quantitative BAN Logic Based on Belief Degree

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Artificial Intelligence Logic and Applications (AILA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1657))

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Abstract

Authentication protocols are the basis for secure communication in many distributed systems but are highly prone to errors in their design, preventing them from working properly. It is therefore necessary to analyze an authentication protocol to determine whether the designed protocol meets the requirements. Much attention has been paid to mathematical logic to analyze cryptographic protocols, particularly the logic proposed by Burrows, Abadi, and Needham (BAN logic). This logic has been successful in identifying weaknesses in various examples of authentication protocols. In this paper, we give a concept of “belief” for BAN logic based on the idea of possibility computation and further propose a quantitative BAN logic. It is also applied to the formal analysis and computation of a Radio Frequency Identification (RFID) authentication protocol to show how it works. The quantitative results on belief show that the proposed quantitative approach of BAN logic based on belief can more objectively reflect the security property of the authentication protocol.

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Notes

  1. 1.

    Since \(M_2=h(T_A)\oplus T_A \oplus T_B\), for the sake of convenience for representation, we use \(M_2\) to replace \(h(T_A)\oplus T_A \oplus T_B\) if needed as follows.

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Acknowledgement

This work is supported by the East China Normal University - Huawei Trustworthiness Innovation Center and the Shanghai Trusted Industry Internet Software Collaborative Innovation Center.

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Authors and Affiliations

  1. Software Engineering Institute, East China Normal University, Shanghai, China

    Kaixuan Li, Jinyi Xu & Yixiang Chen

  2. National Engineering Research Center of Trustworthy Embedded Software, Shanghai, China

    Kaixuan Li, Jinyi Xu & Yixiang Chen

  3. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China

    Hengyang Wu

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  1. Kaixuan Li
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Correspondence to Yixiang Chen .

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Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yixiang Chen

  2. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

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Li, K., Wu, H., Xu, J., Chen, Y. (2022). Quantitative BAN Logic Based on Belief Degree. In: Chen, Y., Zhang, S. (eds) Artificial Intelligence Logic and Applications. AILA 2022. Communications in Computer and Information Science, vol 1657. Springer, Singapore. https://doi.org/10.1007/978-981-19-7510-3_2

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  • DOI: https://doi.org/10.1007/978-981-19-7510-3_2

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