Multi-class Bitcoin mixing service identification based on graph classification

Abstract

Due to its anonymity and decentralization, Bitcoin has long been a haven for various illegal activities. Cyber-criminals generally legalize illicit funds by Bitcoin mixing services. Therefore, it is critical to investigate the mixing services in cryptocurrency anti-money laundering. Existing studies treat different mixing services as a class of suspicious Bitcoin entities. Furthermore, they are limited by relying on expert experience or needing to deal with large-scale networks. So far, multi-class mixing service identification has not been explored yet. It is challenging since mixing services share a similar procedure, presenting no sharp distinctions. However, mixing service identification facilitates the healthy development of Bitcoin, supports financial forensics for cryptocurrency regulation and legislation, and provides technical means for fine-grained blockchain supervision. This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification (BMSI-GC) model. First, BMSI-GC constructs 2-hop ego networks (2-egonets) of mixing services based on their historical transactions. Second, it applies graph2vec, a graph classification model mainly used to calculate the similarity between graphs, to automatically extract address features from the constructed 2-egonets. Finally, it trains a multilayer perceptron classifier to perform classification based on the extracted features. BMSI-GC is flexible without handling the full-size network and handcrafting address features. Moreover, the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification. Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification, achieving an average identification F1-score of 95.08%.