Enhancing Deep Learning Vulnerability Detection through Imbalance Loss Functions: An Empirical Study-Reference-Cited by-同舟云学术

Enhancing Deep Learning Vulnerability Detection through Imbalance Loss Functions: An Empirical Study

Published:2024-07-24 Issue: Volume:32 Page:85-94
ISSN:
Container-title:Proceedings of the 15th Asia-Pacific Symposium on Internetware
language:
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Author:

He Yanzhong¹^ORCID,Lin Guancheng²^ORCID,Ma Xiaoxue³^ORCID,Keung Jacky Wai³^ORCID,Tan Cheng¹^ORCID,Hu Wenhua¹^ORCID,Li Fuyang⁴^ORCID

Affiliation:

1. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China

2. School of Cyber Science and Engineering, Wuhan University, China

3. Department of Computer Science, City University of Hong Kong, China

4. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China and Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology, China

Funder

the National Natural Science Foundation of China

the National Training Program of Innovation and Entrepreneurship for Undergraduates

the Start-up Grant from Wuhan University of Technology

the General Research Fund of the Research Grants Council of Hong Kong and the research funds of the City University of Hong Kong

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3671016.3671379

Reference56 articles.

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3. Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, and Tengyu Ma. 2019. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 1565–1576.

4. MVD

5. Deep Learning based Vulnerability Detection: Are We There Yet