Software Vulnerability Recommendation Algorithm Based on Feature Fusion Lightweight Graph Convolutional Networks
HAN Jianqiang
WEI Jiayin
LU Youjun
Abstract：Aiming at the problem that traditional vulnerability recommendation algorithms did not consider the complex transformation relationship between vulnerabilities and the dynamically changing features of software,which led to their poor recommendation effect,a software vulnerability recommendation algorithm based on feature fusion lightweight graph convolutional network(SVR-FFLGCN)was proposed.First,the heterogeneous relationship graph between software and vulnerabilities was constructed,and the vulnerability similarity algorithm was incorporated to reduce the noise interference from neighboring nodes.Second,a lightweight graph convolutional network was used to capture the complex transformation relationship between vulnerabilities,and the dynamically changing local and global features of the software were adaptively fused,so as to obtain a more comprehensive feature representation for vulnerability recommendation.The experiments showed that when the number of recommended vulnerabilities was 10 and 20,compared with the baseline model,the hit rate(HR)metrics improved by 11.39％and 6.74％respectively,and the normalized cumulative discount gain(NDCG)metrics improved by 7.12％and 4.80％respectively.This study is important in improving the efficiency of developers as well as implementing effective defense measures.
Keywords：vulnerability similaritylightweight graph convolutional networkslocal featureglobal featurefeature fusionvulnerability recommendations
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 494-499 )