VulZoo: A Comprehensive Vulnerability Intelligence Dataset
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912041837527040 |
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| author | Ruan, Bonan Liu, Jiahao Zhao, Weibo Liang, Zhenkai |
| author_facet | Ruan, Bonan Liu, Jiahao Zhao, Weibo Liang, Zhenkai |
| contents | Software vulnerabilities pose critical security and risk concerns for many software systems. Many techniques have been proposed to effectively assess and prioritize these vulnerabilities before they cause serious consequences. To evaluate their performance, these solutions often craft their own experimental datasets from limited information sources, such as MITRE CVE and NVD, lacking a global overview of broad vulnerability intelligence. The repetitive data preparation process further complicates the verification and comparison of new solutions. To resolve this issue, in this paper, we propose VulZoo, a comprehensive vulnerability intelligence dataset that covers 17 popular vulnerability information sources. We also construct connections among these sources, enabling more straightforward configuration and adaptation for different vulnerability assessment tasks (e.g., vulnerability type prediction). Additionally, VulZoo provides utility scripts for automatic data synchronization and cleaning, relationship mining, and statistics generation. We make VulZoo publicly available and maintain it with incremental updates to facilitate future research. We believe that VulZoo serves as a valuable input to vulnerability assessment and prioritization studies. The dataset with utility scripts is available at https://github.com/NUS-Curiosity/VulZoo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VulZoo: A Comprehensive Vulnerability Intelligence Dataset Ruan, Bonan Liu, Jiahao Zhao, Weibo Liang, Zhenkai Cryptography and Security Software Engineering Software vulnerabilities pose critical security and risk concerns for many software systems. Many techniques have been proposed to effectively assess and prioritize these vulnerabilities before they cause serious consequences. To evaluate their performance, these solutions often craft their own experimental datasets from limited information sources, such as MITRE CVE and NVD, lacking a global overview of broad vulnerability intelligence. The repetitive data preparation process further complicates the verification and comparison of new solutions. To resolve this issue, in this paper, we propose VulZoo, a comprehensive vulnerability intelligence dataset that covers 17 popular vulnerability information sources. We also construct connections among these sources, enabling more straightforward configuration and adaptation for different vulnerability assessment tasks (e.g., vulnerability type prediction). Additionally, VulZoo provides utility scripts for automatic data synchronization and cleaning, relationship mining, and statistics generation. We make VulZoo publicly available and maintain it with incremental updates to facilitate future research. We believe that VulZoo serves as a valuable input to vulnerability assessment and prioritization studies. The dataset with utility scripts is available at https://github.com/NUS-Curiosity/VulZoo. |
| title | VulZoo: A Comprehensive Vulnerability Intelligence Dataset |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2406.16347 |