Abstract
Bitcoin is the first decentralized peer-to-peer cryptocurrency that has gained popularity by providing users with transaction anonymity. With the development of Bitcoin and the higher privacy requirements of users, mixing services have emerged to enhance Bitcoin anonymity by obfuscating the flow of funds. However, they are also widely used for illegal activities due to its strong anonymity, especially for money laundering. Therefore, detecting mixing services has great significance for Bitcoin anti-money laundering. In this paper, we propose a novel detection scheme to identify the addresses belonging to Bitcoin mixing services. Specifically, we first construct the Bitcoin mixing dataset, which summarizes a total of 26 features to describe the transaction behavior of addresses. Next, we design a new classification model, called the Dual Ensemble Classification Model. The model combines the advantages of multiple models based on different algorithms and obtains better classification performance. In order to detect more complex mixing patterns, we also extract transaction sub-graphs from the established Bitcoin address-transaction network. The sub-graphs are then classified using a kernel-based graph classification method, which is embedded in the model. Comprehensive experiments on three datasets demonstrate the effectiveness of our scheme, and the proposed model has a detection accuracy of 99.84% for the Bitcoin mixing service.
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