The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Open accessAbstract
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source code available to develop a largescale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code. We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.
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Cites
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Cited by
Unified Pre-training for Program Understanding and Generation
Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642
LineVul: a transformer-based line-level vulnerability prediction
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Vulnerability detection with fine-grained interpretations
Li, Y., Wang, S., & Nguyen, T. N. (2021) · Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3468264.3468597 · Cited by 282
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
Ullah, S., Han, M., Pujar, S., Pearce, H., Coskun, A., & Stringhini, G. (2023) · arXiv · doi:10.48550/arxiv.2312.12575 · Cited by 132
Metadata
- Authors
- Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., Ellingwood, P., & McConley, M.
- Venue
- 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 757-762
- OpenAlex
- W2822734516
- Cited by
- 623
- References
- 11
- FWCI
- 27.90
- Type
- conference paper
- Language
- en
- Open access
- green
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- Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., Ellingwood, P., & McConley, M. (2018). Automated Vulnerability Detection in Source Code Using Deep Representation Learning. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/icmla.2018.00120
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