Work

DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection

Year: 2023

Type: conference-paper

Abstract: 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.

Source: International Symposium on Research in Attacks, Intrusions and Defenses (RAID)

DOI: 10.1145/3607199.3607242

Authors: Yizheng Chen, Zhoujie Ding, Lamya Alowain, Xinyun Chen, David Wagner

Institutions: University of Maryland, College Park, University of California, Berkeley, King Abdulaziz City for Science and Technology

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, King Abdulaziz City for Science and Technology

Award: Collaborative Research: SaTC: CORE: Small: Machine Learning for Cybersecurity: Robustness Against Concept Drift