E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network

Authors

  • Haiye Wang Nanjing University of Information Science and Technology image/svg+xml
  • Zhiguo Qu Nanjing University of Information Science and Technology image/svg+xml
  • Le Sun Nanjing University of Information Science and Technology image/svg+xml

DOI:

https://doi.org/10.4108/eetsis.5056

Keywords:

vulnerability detection, graph neural network, pre-trained model, interpretable machine learning

Abstract

INTRODUCTION: Vulnerability detection is crucial for preventing severe security incidents like hacker attacks, data breaches, and network paralysis. Traditional methods, however, face challenges such as low efficiency and insufficient detail in identifying code vulnerabilities. 
OBJECTIVES: This paper introduces E-GVD, an advanced method for source code vulnerability detection, aiming to address the limitations of existing methods. The objective is to enhance the accuracy of function-level vulnerability detection and provide detailed, understandable insights into the vulnerabilities. 
METHODS: E-GVD combines Graph Neural Networks (GNNs), which are adept at handling graph-structured data, with residual connections and advanced Programming Language (PL) pre-trained models. 
RESULTS: Experiments conducted on the real-world vulnerability dataset CodeXGLUE show that E-GVD significantly outperforms existing baseline methods in detecting vulnerabilities. It achieves a maximum accuracy gain of 4.98%, indicating its effectiveness over traditional methods. 
CONCLUSION: E-GVD not only improves the accuracy of vulnerability detection but also contributes by providing fine-grained explanations. These explanations are made possible through an interpretable Machine Learning (ML) model, which aids developers in quickly and efficiently repairing vulnerabilities, thereby enhancing overall software security.

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Published

21-03-2024

How to Cite

1.
Wang H, Qu Z, Sun L. E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network. EAI Endorsed Scal Inf Syst [Internet]. 2024 Mar. 21 [cited 2024 Apr. 18];. Available from: https://publications.eai.eu/index.php/sis/article/view/5056

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Section

Research articles