DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection-Reference-Cited by-同舟云学术

DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
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Author:

Wang Wenbo¹,Nguyen Tien N.²,Wang Shaohua¹,Li Yi¹,Zhang Jiyuan³,Yadavally Aashish²

Affiliation:

1. New Jersey Institute of Technology,Department of Informatics,New Jersey,USA

2. The University of Texas at Dallas,Computer Science Department,Texas,USA

3. University of Illinois Urbana-Champaign,Computer Science Department,Illinois,USA

Publisher

IEEE

Link

Reference58 articles.

2. Automated software vulnerability detection with machine learning;harer;ArXiv Preprint,2018

Cited by 6 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

3. MegaVul: A C/C++ Vulnerability Dataset with Comprehensive Code Representations;Proceedings of the 21st International Conference on Mining Software Repositories;2024-04-15

4. Towards Causal Deep Learning for Vulnerability Detection;Proceedings of the IEEE/ACM 46th International Conference on Software Engineering;2024-04-12

5. Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems;Proceedings of the IEEE/ACM 46th International Conference on Software Engineering;2024-04-12