Problem
Research questions and friction points this paper is trying to address.
O-RAN
security
complexity
attack surface
manual cross-referencing
A Queryable Graph-Based Security Analysis Framework for O-RAN
🤖 AI Summary
本文提出一种基于图的可查询框架，用于O-RAN安全分析，通过集成结构化和非结构化数据，解决手动评估复杂且易出错的问题。
📝 Abstract
The Open Radio Access Network (O-RAN) replaces vendor-locked RANs with a modular and interoperable architecture that fosters competition and accelerates innovation. With this openness comes increased complexity and a larger attack surface, making security a critical concern. Today, assessing O-RAN security requires manually cross-referencing dozens of specifications, vendor whitepapers, and academic studies, which is error-prone and static. In this paper, we present a graph-based framework that transforms this static corpus into a single, queryable database. Our graph representation contains over 350 nodes and more than 1,250 relationships, distilled from specifications, academic papers, open-source projects, and vulnerability databases. To keep this resource current, we integrate a hybrid data extraction pipeline that couples deterministic parsing of structured specifications with Large Language Model (LLM)-assisted extraction for evolving specifications and unstructured literature. Querying the graph reveals three actionable findings within our curated corpus: critical infrastructure such as the O-DU, SMO, and O-Cloud carries dozens of specification-level threats yet has little or no empirical coverage; memory-safety weaknesses account for 11 of the 21 CWE occurrences associated with the analyzed CVEs; and fuzzing uncovered 18 of the 20 CVEs attributed to research papers. We provide the database, pipeline, and queries as open-source artifacts.
Innovation
Methods, ideas, or system contributions that make the work stand out.
graph-based framework
queryable database
hybrid data extraction pipeline
Large Language Model (LLM)-assisted extraction
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