DIVE:一个多标签智能合约漏洞数据集
Shikah J Alsunaidi1, Hamoud Aljamaan2,3, Mohammad Hammoudeh1,4
1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
Scientific data
|March 13, 2026
概括
本研究介绍了DIVE,这是一个用于检测智能合约 (SC) 漏洞的新数据集. DIVE提供了大量,多样化的真实世界SCs集合,具有全面的功能,以提高机器学习模型的可靠性.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 网络安全 网络安全
背景情况:
- 智能合约 (SC) 的漏洞带来了重大风险,导致财务损失和功能失败.
- 现有用于SC漏洞检测的数据集通常受到大小,不平衡,不一致的标签和非标准化的特征的限制,这阻碍了可靠的机器学习 (ML) 模型开发.
- 当前的特征表示往往忽略了合同生命周期的不同阶段,影响了模型的概括性和基准准确性.
研究的目的:
- 引入DIVE,这是一种新的多标签数据集,旨在克服现有的SC漏洞数据集的局限性.
- 为培训和评估用于SC漏洞检测的ML模型提供全面的资源.
- 在智能合约生命周期的不同阶段实现更可靠和更普遍的漏洞检测.
主要方法:
- DIVE包含了在2016年至2024年间部署的22,330个现实世界SC,涵盖了主要的Solidity编译器版本.
- 根据去中心化应用安全项目 (DASP) 的十大分类,SCs对八种漏洞类型进行了注释.
- 使用基于功率的投票和后期过的标准化多工具标签管道被采用,纠正了拒绝服务 (DoS) 和时间操纵漏洞中的重大错误阳性.
主要成果:
- DIVE数据集提供了221个部署前和176个部署后的功能,提供了生命周期特定的功能集.
- 标签管道成功纠正了DoS中的14.3%的假阳性和时间操纵漏洞中的24.9%.
- 该数据集支持通过开源框架进行可重现的基准测试,促进与不断变化的脆弱性模式保持一致的定期重建.
结论:
- DIVE解决了现有的SC漏洞数据集中的关键结构和特征级限制.
- 数据集的综合性和生命周期特征提高了基于ML的SC漏洞检测的可靠性和通用性.
- 在智能合约安全的不断变化的环境中,DIVE促进可复制的研究和可适应的漏洞检测方法.
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