HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering

【Author】 Song, Jialin; Gu, Yijun

【Source】APPLIED SCIENCES-BASEL

【影响因子】2.838

【Abstract】In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.

【Keywords】graph embedding; Bitcoin anti-money laundering; machine learning; heterogeneous graph neural networks; metapath encoder

【发表时间】2023 AUG

【收录时间】2023-08-25

【文献类型】实证数据

【主题类别】

区块链治理-技术治理-异常检测