Real-CATS：用于加密货币犯罪检测的标注数据集

文章 arXiv papers · 作者: Jiadong Shi et al.

总结

本文介绍Real-CATS数据集，旨在支持加密货币地址检测方法的开发与评估。该数据集将现实报告中识别出的犯罪地址与交易所客户关联的良性地址相结合，为真实世界数据难以获取的研究领域提供标注样本。

作者介绍了三种用途：比较检测方法、通过增加特征扩展数据集，以及在旨在反映真实部署的场景中评估方法。作者从全面性、可分类性、可定制性和可迁移性几个方面描述数据集质量。摘要提供了数据集规模和来源，但没有检测基准或证据表明某种方法表现良好。结果可能取决于标签的采集方式，以及样本在多大程度上代表部署时遇到的地址，因此该数据集是研究资源，并非实际运行准确性的证明。

核心观点

真实世界的标注地址有助于缓解加密货币犯罪研究中的数据稀缺问题。
该数据集将报告中的犯罪地址与交易所客户的良性地址配对。
该数据集旨在用于方法评估、特征扩展和面向部署的评估。
本文描述了数据集范围，但没有报告检测方法的比较表现。

Cybercriminals pose a significant threat to blockchain trading security, causing $40.9 billion in losses in 2024. However, the lack of an effective real-world address dataset hinders the advancement of cybercrime detection research. The anti-cybercrime efforts of researchers from broader fields, such as statistics and artificial intelligence, are blocked by data scarcity. In this paper, we present Real-CATS, a Real-world dataset of Cryptocurrency Addresses with Transaction profileS, serving as a practical training ground for developing and assessing detection methods. Real-CATS comprises 103,203 criminal addresses from real-world reports and 106,196 benign addresses from exchange customers. It satifies the C3R characteristics (Comprehensiveness, Classifiability, Customizability, and Real-world Transferability), which are fundemental for practical detection of cryptocurrency cybercrime. The dataset provides three main functions: 1) effective evaluation of detection methods, 2) support for feature extensions, and 3) a new evaluation scenario for real-world deployment. Real-CATS also offers opportunities to expand cybercrime measurement studies. It is particularly beneficial for researchers without cryptocurrency-related knowledge to engage in this emerging research field. We hope that studies on cryptocurrency cybercrime detection will be promoted by an increasing number of cross-disciplinary researchers drawn to this versatile data platform. All datasets are available at https://github.com/sjdseu/Real-CATS

在遵守原作品许可的前提下，附作者信息全文展示。许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。