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Formal method-based analysis of the 5G Wireless Communication Protocol iscrucial for identifying logical vulnerabilities and facilitating anall-encompassing security assessment, especially in the design phase. NaturalLanguage Processing (NLP) assisted techniques and most of the tools are notwidely adopted by the industry and research community. Traditional formalverification through a mathematics approach heavily relied on manual logicalabstraction prone to being time-consuming, and error-prone. The reason that theNLP-assisted method did not apply in industrial research may be due to theambiguity in the natural language of the protocol designs nature iscontroversial to the explicitness of formal verification. To address thechallenge of adopting the formal methods in protocol designs, targeting (3GPP)protocols that are written in natural language, in this study, we propose ahybrid approach to streamline the analysis of protocols. We introduce atwo-step pipeline that first uses NLP tools to construct data and then usesconstructed data to extract identifiers and formal properties by using the NLPmodel. The identifiers and formal properties are further used for formalanalysis. We implemented three models that take different dependencies betweenidentifiers and formal properties as criteria. Our results of the optimal modelreach valid accuracy of 39% for identifier extraction and 42% for formalproperties predictions. Our work is proof of concept for an efficient procedurein performing formal analysis for largescale complicate specification andprotocol analysis, especially for 5G and nextG communications.

From Ambiguity to Explicitness: NLP-Assisted 5G Specification Abstraction for Formal Analysis