HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering-Reference-Cited by-同舟云学术

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

Published:2023-07-29 Issue:15 Volume:13 Page:8766
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Song Jialin¹,Gu Yijun¹

Affiliation:

1. School of Information Network Security, People’s Public Security University of China, Beijing 100032, China

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.

Funder

China People's Public Security University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/15/8766/pdf

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