跳至正文
返回文库全部文档

通过交易统计模式识别比特币混币器

文章 arXiv papers · 作者: Ardeshir Shojaeenasab et al.
阅读原文

总结

本研究探讨比特币混币服务如何通过交易和地址掩盖资金流向。作者介绍了追踪混币活动，以及识别混币交易、混币器地址、发送方和接收方地址的方法，并区分被描述为“脏”或已清洗的资金。该方法依赖混币服务相关活动中观察到的统计模式。

由于缺少带标签的样本，研究人员通过实际使用这些服务进行交易来生成带标签的数据。他们报告发现了可靠模式，并将其整合为一个检测算法。这为真实标签有限的分类问题提供了实用方法，但文中没有说明抽样涉及哪些服务、具体模式、评估指标或对未见活动的检测表现。研究结果针对比特币网络中的检测，并不能证明同一算法适用于其他加密货币或较新的混币器设计。

核心观点

  • 混币服务旨在掩盖加密货币交易中的资金流向。
  • 研究尝试识别混币交易及相关混币器、发送方和接收方地址。
  • 由于缺少现成标签，研究人员通过使用混币服务生成带标签的数据。
  • 研究人员将带标签活动中的统计模式整合为检测算法。
  • 文中没有报告评估指标，也未证明该方法可推广到比特币以外的网络。

标签

加密资产链上数据统计学风险管理
全文
# Mixing detection on Bitcoin transactions using statistical patterns


# Mixing detection on Bitcoin transactions using statistical patterns









Cryptocurrencies gained lots of attention mainly because of the anonymous way of online payment, which they suggested. Meanwhile, Bitcoin and other major cryptocurrencies have experienced severe deanonymization attacks. To address these attacks, Bitcoin contributors introduced services called mixers or tumblers. Mixing or laundry services aim to return anonymity back to the network. In this research, we tackle the problem of losing the footprint of money in Bitcoin and other cryptocurrencies networks caused by the usage of mixing services. We devise methods to track transactions and addresses of these services and the addresses of dirty and cleaned money. Because of the lack of labeled data, we had to transact with these services and prepare labeled data. Using this data, we found reliable patterns and developed an integrated algorithm to detect mixing transactions, mixing addresses, sender addresses, and receiver addresses in the Bitcoin network.

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。