CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications
Mirza Masfiqur Rahman, Imtiaz Karim, Elisa Bertino

TL;DR
CellularLint is a semi-automatic framework that leverages advanced NLP techniques and large language models to detect inconsistencies in 4G and 5G cellular network specifications, improving security analysis.
Contribution
The paper introduces CellularLint, a novel NLP-based tool that automates inconsistency detection in cellular network standards using few-shot learning on domain-adapted language models.
Findings
Detected 157 inconsistencies with 82.67% accuracy
Validated inconsistencies impact security and interoperability
Scalable analysis of protocol specifications
Abstract
In recent years, there has been a growing focus on scrutinizing the security of cellular networks, often attributing security vulnerabilities to issues in the underlying protocol design descriptions. These protocol design specifications, typically extensive documents that are thousands of pages long, can harbor inaccuracies, underspecifications, implicit assumptions, and internal inconsistencies. In light of the evolving landscape, we introduce CellularLint--a semi-automatic framework for inconsistency detection within the standards of 4G and 5G, capitalizing on a suite of natural language processing techniques. Our proposed method uses a revamped few-shot learning mechanism on domain-adapted large language models. Pre-trained on a vast corpus of cellular network protocols, this method enables CellularLint to simultaneously detect inconsistencies at various levels of semantics and…
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Taxonomy
TopicsGene Regulatory Network Analysis · Simulation Techniques and Applications · Cellular Automata and Applications
MethodsFocus
