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, \emph{mixing services} in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundry to complicate trailing illicit fund. In this paper, 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.

中文翻译：

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通过使用混合模型挖掘比特币交易网络来检测混合服务

作为第一个允许人们使用匿名地址进行交易的去中心化点对点 (P2P) 加密货币系统，比特币近年来变得越来越流行。但是，比特币的 P2P 和匿名性使得该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，比特币中的 \emph {mixing services} 最初旨在增强交易匿名性，已被广泛用于洗钱，使跟踪的非法资金复杂化。在本文中，我们专注于检测属于混合服务的地址，这是比特币反洗钱的一项重要任务。具体来说，我们提供了一个基于特征的网络分析框架，从三个层面识别混合服务的统计属性，即网络级、账户级和交易级。为了更好地表征不同类型地址的交易模式，我们提出了属性时间异构主题（ATH 主题）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正和未标记学习（PU 学习）问题来解决，并通过利用所考虑的特征来构建检测模型。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模体（包括 ATH 模体）在混合检测中的重要性。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的学习（PU 学习）问题来处理，并通过利用所考虑的特征来构建检测模型。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模体（包括 ATH 模体）在混合检测中的重要性。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的学习（PU 学习）问题来处理，并通过利用所考虑的特征来构建检测模型。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模体（包括 ATH 模体）在混合检测中的重要性。