VDGraph2Vec: Vulnerability Detection in Assembly Code using Message Passing Neural Networks

Author:

Diwan Ashita1,Li Miles Q.1,Fung Benjamin C. M.2

Affiliation:

1. McGill University,School of Computer Science,Montreal,Canada

2. McGill University,School of Information Studies,Montreal,Canada

Funder

Canada Research Chairs

Publisher

IEEE

Reference41 articles.

1. Extremely randomized trees

2. Structure2vec: Deep learning for security analytics over graphs;song,2018

3. CodeBERT: A Pre-Trained Model for Programming and Natural Languages

4. Automated software vulnerability detection with machine learning;harer,2018

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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

2. Automating shareable cyber threat intelligence production for closed source software vulnerabilities: a deep learning based detection system;International Journal of Information Security;2024-07-08

3. VulDet-BC: Binary Software Vulnerability Detection Based on BiGRU and CNN;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

4. VulEXplaineR: XAI for Vulnerability Detection on Assembly Code;Lecture Notes in Computer Science;2024

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