MSGVUL: Multi-semantic integration vulnerability detection based on relational graph convolutional neural networks-Reference-Cited by-同舟云学术

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MSGVUL: Multi-semantic integration vulnerability detection based on relational graph convolutional neural networks

Published:2024-06 Issue: Volume:170 Page:107442
ISSN:0950-5849
Container-title:Information and Software Technology
language:en
Short-container-title:Information and Software Technology

Author:

Xiao Wei,Hou Zhengzhang,Wang Tao,Zhou Chengxian,Pan Chao^ORCID

Publisher

Elsevier BV

Reference37 articles.

1. Joern, 2019. https://github.com/joernio/joern.

2. Distributed representations of words and phrases and their compositionality;Mikolov;Advances in Neural Information Processing Systems,2013

3. node2vec: scalable feature learning for networks;Grover,2016

4. A survey of static analysis methods for identifying security vulnerabilities in software systems;Pistoia;IBM Syst. J.,2007

5. Automated whitebox fuzz testing;Godefroid;NDSS,2008

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An empirical study on the impact of graph representations for code vulnerability detection using graph learning;2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR);2024-08-15

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