Vulnerability Knowledge Graph Construction and Completion with Dual-Modality Perception

ZHANG Yan; LUO Xiang-yu; QIN Zi-yue; ZHANG Miao; LI Zhi-fei

doi:10.12263/DZXB.20250485

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Vulnerability Knowledge Graph Construction and Completion with Dual-Modality Perception

PAPERS | 更新时间：2026-02-05

- Vulnerability Knowledge Graph Construction and Completion with Dual-Modality Perception
- ACTA ELECTRONICA SINICA Vol. 53, Issue 10, Pages: 3579-3592(2025)
- 作者机构：
  
  1.湖北大学计算机学院，湖北武汉 430062
  2.湖北大学网络空间安全学院，湖北武汉 430062
  3.大数据智能分析与行业应用湖北省重点实验室，湖北武汉 430062
  4.智能感知系统与安全教育部重点实验室，湖北武汉 430062
  5.智能网联汽车网络安全湖北省工程研究中心，湖北武汉 430062
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62207011;62407013);Natural Science Foundation of Hubei Province of China(2025AFB653);Major Science and Technology Project of Hubei Province(2024BAA008)
- DOI：10.12263/DZXB.20250485
  CLC： TP309;
- Received：06 June 2025，
  
  Revised：2025-10-09，
  
  Published：25 October 2025
- 稿件说明：
移动端阅览
张龑, 罗翔宇, 秦紫玥, 等. 融合双模态感知的漏洞知识图谱构建与补全方法[J]. 电子学报, 2025, 53(10): 3579-3592.

ZHANG Yan, LUO Xiang-yu, QIN Zi-yue, et al. Vulnerability Knowledge Graph Construction and Completion with Dual-Modality Perception[J]. Acta Electronica Sinica, 2025, 53(10): 3579-3592.
张龑, 罗翔宇, 秦紫玥, 等. 融合双模态感知的漏洞知识图谱构建与补全方法[J]. 电子学报, 2025, 53(10): 3579-3592. DOI：10.12263/DZXB.20250485

ZHANG Yan, LUO Xiang-yu, QIN Zi-yue, et al. Vulnerability Knowledge Graph Construction and Completion with Dual-Modality Perception[J]. Acta Electronica Sinica, 2025, 53(10): 3579-3592. DOI：10.12263/DZXB.20250485

摘要

漏洞知识图谱作为网络安全知识建模的重要工具，在漏洞分析、威胁建模、安全态势感知和攻击链追踪等关键任务中发挥着日益重要的作用.与通用知识图谱覆盖领域广、更新周期长，侧重于通用知识与关系建模不同的是，漏洞知识图谱更新频率高，面临着数据异构、语义歧义和知识稀疏的挑战，往往需要融合非结构化描述信息进行联合建模.然而，现有方法仍局限于三元组建模范式，忽略了网络安全知识库中丰富的安全文本信息，导致漏洞知识图谱补全与攻击链预测精度受限.为此，本文提出构建一种漏洞描述知识图谱（Vulnerability description Knowledge Graph，VKG-T），通过联合结构和语义信息，增强漏洞弱点信息的补全能力.同时，本文设计了一种双模态感知聚合的漏洞描述知识图谱补全模型（Vulnerability description Knowledge Graph Completion， VKGC-ST），该模型结合图注意力网络（Graph ATtention networks，GAT）与预训练语言模型，综合考虑实体的结构邻接特征与文本描述信息，并结合多层次负采样与对比学习机制，提升实体语义判别能力和结构关联建模效果.通过在漏洞描述知识图谱VKG-T以及通用数据集FB15K-237、WN18RR上的链接预测实验证明，VKGC-ST在所有指标上均取得最佳性能，其中在漏洞描述知识图谱数据集上平均提升率为9.42%，最大提升率15.51%，展现了优异的泛化能力与领域适应性.

Abstract

As a critical tool for cybersecurity knowledge modeling

vulnerability knowledge graphs play an increasingly important role in key tasks such as vulnerability analysis

threat modeling

security situational awareness and attack chain tracking. Unlike universal knowledge graphs

which cover a wide range of domains

have a long update cycle

and focus on generic knowledge and relationship modeling

vulnerability knowledge graphs are updated frequently

face the challenges of data heterogeneity

semantic ambiguity

and knowledge sparsity

and often need to incorporate unstructured descriptive information for joint modeling. However

the existing methods are still limited to the ternary formation modeling paradigm

ignoring the rich security text descriptions in the cybersecurity knowledge base

resulting in limited accuracy of vulnerability knowledge graph complementation and attack chain prediction. To address this

this paper proposes the construction of a vulnerability description knowledge graph (VKG-T)

which enhances the ability to complete vulnerability and weakness information by combining structural and semantic data. Additionally

we present a dual-modality perception aggregated model for vulnerability description knowledge graph completion (VKGC-ST). This model integrates graph attention networks (GAT) with pre-trained language models

considering both the structural adjacency features of entities and their textual descriptions

and employs multi-level negative sampling and contrastive learning mechanisms to improve semantic discrimination and structural correlation modeling. Through link prediction experiments on vulnerability knowledge graph VKG-T and general datasets FB15K-237

WN18RR

VKGC-ST achieves the best performance across all metrics

specifically

on the vulnerability description knowledge graph dataset

the average improvement rate is 9.42%

with a maximum improvement rate of 15.51%

showcasing excellent generalization ability and domain adaptability.

关键词

Keywords

references

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