Detecting Mixing Services via Mining Bitcoin Transaction Network With Hybrid Motifs

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.

Automated summary

This article focuses on the detection of addresses belonging to mixing services in Bitcoin, proposing a feature-based network analysis framework to identify statistical properties of mixing services and introducing the concept of attributed temporal heterogeneous motifs (ATH motifs). By approaching the mixing detection task as a positive and unlabeled learning problem, a detection model is built with considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of the detection model and the importance of hybrid motifs including ATH motifs in mixing detection.