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

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

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

1. Enhancing Deep Learning Vulnerability Detection through Imbalance Loss Functions: An Empirical Study;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

2. Natural Is the Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models;Proceedings of the ACM on Software Engineering;2024-07-12

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

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