Optimising source code vulnerability detection using deep learning and deep graph network

作     者：Xuan, Cho Do Luong, Tran Thi Thanh, Ma Cong 

作者机构：Posts & Telecommun Inst Technol Fac Informat Secur Hanoi Vietnam Acad Cryptog Tech Hanoi Vietnam 

出 版 物：《CONNECTION SCIENCE》 (Connect. Sci.)

年 卷 期：2025年第37卷第1期

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Posts and Telecommunications Institute of Technology  Vietnam 

主　　题：Source code vulnerability detection code Property graph node feature edge feature feature profile deep learning graph network 

摘      要：To enhance the effectiveness of vulnerability detection in software developed using C and C++ programming languages, our study introduces a novel correlation calculation method for analyzing and evaluating Code Property Graphs (CPG). The intelligent computation method proposed in this study comprises three key stages. In the first stage, we present a method for extracting features from the CPG source code. To accomplish this, we integrate three distinct data exploration methods: employing Graph Convolutional Neural (GCN) to extract node features from CPG, utilizing Convolutional Neural Network (CNN) to extract edge features from CPG, and finally employing the Doc2vec natural language processing algorithm to extract source code from CPG nodes. The second stage involves proposing a method for synthesizing CPG source code features. Building on the features acquired in the first stage, our paper introduces a synthesis and construction method to generate feature vectors for the source code. The final stage, stage three, executes the detection of source code vulnerabilities. The experimental results demonstrate that our proposed model in this study achieves higher efficiency compared to other studies, with an improvement ranging from 3% to 4%.