VulCurator: a vulnerability-fixing commit detector

• Main Authors: NGUYEN, Truong Giang, LE, Cong Thanh, KANG, Hong Jin, LE, Xuan-Bach D., LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Vulnerability-fixing commits; Deep learning; BERT; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7741; https://ink.library.smu.edu.sg/context/sis_research/article/8744/viewcontent/VulCurator_A_Vulnerability_Fixing_Commit_Detector.pdf
• Institution: Singapore Management University

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time-consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, including commit messages, code changes and issue reports for vulnerability-fixing commit classification. Our experimental results show that VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly available at https://github.com/ ntgiang71096/VFDetector and https://zenodo.org/record/7034132# .Yw3MN-xBzDI, with a demo video at https://youtu.be/uMlFmWSJYOE.