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Optimising source code vulnerability detection using deep learning and deep graph network
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DOI：10.1080/09540091.2024.2447373.png)
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Abstract
En 中文
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%.
Keywords:
Source code vulnerability detection
code Property graph
node feature
edge feature
feature profile
deep learning graph network
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3.4
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854
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1.5K
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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