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

TL;DR

This paper presents a hybrid NLP-based approach to convert natural language 5G protocol specifications into formal models, enabling more efficient and accurate formal analysis of complex communication protocols.

Contribution

It introduces a two-step pipeline combining NLP tools and models for extracting formal properties from natural language specifications, improving the formal analysis process for 5G protocols.

Findings

Achieved 39% accuracy in identifier extraction

Achieved 42% accuracy in formal property prediction

Demonstrated the approach as a proof of concept for large-scale protocol analysis

Abstract

Formal method-based analysis of the 5G Wireless Communication Protocol is crucial for identifying logical vulnerabilities and facilitating an all-encompassing security assessment, especially in the design phase. Natural Language Processing (NLP) assisted techniques and most of the tools are not widely adopted by the industry and research community. Traditional formal verification through a mathematics approach heavily relied on manual logical abstraction prone to being time-consuming, and error-prone. The reason that the NLP-assisted method did not apply in industrial research may be due to the ambiguity in the natural language of the protocol designs nature is controversial to the explicitness of formal verification. To address the challenge of adopting the formal methods in protocol designs, targeting (3GPP) protocols that are written in natural language, in this study, we propose a hybrid approach to streamline the analysis of protocols. We introduce a two-step pipeline that first uses NLP tools to construct data and then uses constructed data to extract identifiers and formal properties by using the NLP model. The identifiers and formal properties are further used for formal analysis. We implemented three models that take different dependencies between identifiers and formal properties as criteria. Our results of the optimal model reach valid accuracy of 39% for identifier extraction and 42% for formal properties predictions. Our work is proof of concept for an efficient procedure in performing formal analysis for largescale complicate specification and protocol analysis, especially for 5G and nextG communications.