计算机工程与科学2025，Vol.47Issue(5)：851-863,13.DOI:10.3969/j.issn.1007-130X.2025.05.009
基于双曲图卷积神经网络的切片级漏洞检测方法
A slice-level vulnerability detection method based on hyperbolic graph convolutional neural network
摘要
Abstract
Addressing the challenges in the field of source code vulnerability detection,particularly the shortcomings of existing methods in accurately embedding code graphs and capturing their complex hierarchical structures,this paper proposes an innovative slice-level source code vulnerability detection method based on hyperbolic graph convolutional neural network(HGCN),termed VulDHGCN.This method integrates the powerful expressive capabilities of graph convolutional neural networks and hy-perbolic geometry to more comprehensively embed and preserve the structural features of source code,effectively reducing information distortion during the code graph embedding process.To comprehensive-ly evaluate the effectiveness of VulDHGCN,three traditional rule-based static vulnerability detection methods and three advanced model-based vulnerability detection methods are selected as comparison baselines.Experimental results demonstrate that VulDHGCN outperforms the baseline methods across multiple key performance indicators.Specifically,VulDHGCN achieves accuracy,precision,recall,and F,scores of 96.52％,92.31％,85.12％,and 88.57％,respectively.Compared to the baseline vulnera-bility detection methods,VulDHGCN exhibits a significant advantage with an improvement in F1 score ranging from 6.62％to 153.92％.This not only validates the effectiveness of the VulDHGCN method but also provides a new perspective and approach for the further application of deep learning in the field of source code vulnerability detection.关键词
漏洞检测/切片级别/低失真嵌入/双曲空间/图卷积神经网络Key words
vulnerability detection/slice-level/low distortion embedding/hyperbolic space/graph con-volutional neural network分类
信息技术与安全科学引用本文复制引用
陈旭,陈子雄,景永俊,王叔洋,宋吉飞..基于双曲图卷积神经网络的切片级漏洞检测方法[J].计算机工程与科学,2025,47(5):851-863,13.基金项目
北方民族大学中央高校基本科研业务费专项资金(2023ZRLG13) （2023ZRLG13）
宁夏回族自治区重点研发项目(2023BDE02017) （2023BDE02017）
