A Machine Learning Approach for Vulnerability Curation

@article{Chen2020AML,
  title={A Machine Learning Approach for Vulnerability Curation},
  author={Yang Chen and Andrew E. Santosa and Ang Ming Yi and Abhishek Sharma and Asankhaya Sharma and D. Lo},
  journal={Proceedings of the 17th International Conference on Mining Software Repositories},
  year={2020}
}
  • Yang Chen, A. Santosa, D. Lo
  • Published 29 June 2020
  • Computer Science
  • Proceedings of the 17th International Conference on Mining Software Repositories
Software composition analysis depends on database of open-source library vulerabilities, curated by security researchers using various sources, such as bug tracking systems, commits, and mailing lists. We report the design and implementation of a machine learning system to help the curation by by automatically predicting the vulnerability-relatedness of each data item. It supports a complete pipeline from data collection, model training and prediction, to the validation of new models before… 
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