【Author】
Wu, Jiajing; Liu, Jieli; Chen, Weili; Huang, Huawei; Zheng, Zibin; Zhang, Yan
【Source】IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
【Abstract】As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, mixing services in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.
【Keywords】Bitcoin; Feature extraction; Task analysis; Training; Peer-to-peer computing; Ecosystems; Tools; Anti-money laundering (AML); bitcoin; mixing services; network mining; network motifs
【标题】利用混合图案挖掘比特币交易网络检测混合服务
【摘要】作为第一个去中心化的点对点(P2P)加密货币系统，允许人们使用假名地址进行交易，比特币近年来越来越受欢迎。然而，比特币的P2P和假名性质使得在这个平台上的交易很难被追踪，从而引发了比特币生态系统中各种非法活动的出现。特别是，为了提高交易的匿名性，比特币的混合服务被广泛用于洗钱，使追踪非法资金的过程变得更加复杂。在本文中，我们重点研究了属于混合服务的地址的检测，这是比特币反洗钱的一个重要任务。具体来说，我们提供了一个基于特征的网络分析框架，从网络层、账户层和交易层三个层次识别混合服务的统计属性。为了更好地描述不同地址类型的事务模式，我们提出了时间属性异构基序(ATH基序)的概念。此外，为了解决不完美标记的问题，我们将混合检测任务作为一个积极和未标记学习(PU学习)问题来处理，并利用考虑的特征建立一个检测模型。在真实比特币数据集上的实验证明了我们的检测模型的有效性，以及混合基元(包括ATH基元)在混合检测中的重要性。
【关键词】比特币;特征提取;任务分析;培训;点对点计算;生态系统;工具;反洗钱(AML);比特币;混合服务;网络挖掘;网络主题
【文献类型】Article; Early Access
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