  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 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.
