The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
Open accessAbstract
We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 18,945 vulnerable functions spanning 150 CWEs and 330,492 non-vulnerable functions extracted from 7,514 commits. Our dataset covers 295 more projects than all previous datasets combined.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · arXiv · doi:10.48550/arxiv.2109.00859 · Cited by 1,495
Cited by
Large Language Models for Software Engineering: A Systematic Literature Review
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3695988 · Cited by 1,029
Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M., Lestable, T., & Thandi, N. S. (2024) · IEEE Access · doi:10.1109/access.2024.3363469 · Cited by 312
When LLMs meet cybersecurity: a systematic literature review
Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 383
“It compares the original code with LLM-generated code to flag notable differences as anomalies for further inspection.”
Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities
Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.01.001 · Cited by 148
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
Khare, A., Dutta, S., Li, Z., Solko-Breslin, A., Alur, R., & Naik, M. (2023) · arXiv · doi:10.48550/arxiv.2311.16169 · Cited by 64
Metadata
- Authors
- Chen, Y., Ding, Z., Alowain, L., Chen, X., & Wagner, D.
- Venue
- Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses
- Publisher
- ACM
- Published
- 2023-10-16
- Pages
- 654-668
- OpenAlex
- W4387298393
- Cited by
- 238
- References
- 2
- FWCI
- 63.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- The paper
- Chen, Y., Ding, Z., Alowain, L., Chen, X., & Wagner, D. (2023). DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection. Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses. https://doi.org/10.1145/3607199.3607242
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