TY - JOUR
T1 - How to Find a Bitcoin Mixer
T2 - A Dual Ensemble Model for Bitcoin Mixing Service Detection
AU - Xu, Chang
AU - Xiong, Ruting
AU - Shen, Xiaodong
AU - Zhu, Liehuang
AU - Zhang, Xiaoming
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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 article, we propose a novel detection scheme to identify the addresses belonging to Bitcoin mixing services. Specifically, we first construct the Bitcoin mixing data set, 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 subgraphs from the established Bitcoin address-transaction network. The subgraphs are then classified using a kernel-based graph classification method, which is embedded in the model. Comprehensive experiments on three data sets demonstrate the effectiveness of our scheme, and the proposed model has a detection accuracy of 99.84% for the Bitcoin mixing service.
AB - 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 article, we propose a novel detection scheme to identify the addresses belonging to Bitcoin mixing services. Specifically, we first construct the Bitcoin mixing data set, 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 subgraphs from the established Bitcoin address-transaction network. The subgraphs are then classified using a kernel-based graph classification method, which is embedded in the model. Comprehensive experiments on three data sets demonstrate the effectiveness of our scheme, and the proposed model has a detection accuracy of 99.84% for the Bitcoin mixing service.
KW - Bitcoin
KW - ensemble learning
KW - kernel-based graphical classification
KW - mixing service
UR - http://www.scopus.com/inward/record.url?scp=85162885610&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2023.3275158
DO - 10.1109/JIOT.2023.3275158
M3 - Article
AN - SCOPUS:85162885610
SN - 2327-4662
VL - 10
SP - 17220
EP - 17230
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 19
ER -