Research on Bitcoin address classification based on transaction history features

Lu Qin; Li Yi; Xiancheng Lin; Ziqiang Luo

doi:10.1117/12.2667252

22 February 2023 Research on Bitcoin address classification based on transaction history features

Lu Qin, Li Yi, Xiancheng Lin, Ziqiang Luo

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125870N (2023) https://doi.org/10.1117/12.2667252
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

As the most popular cryptocurrency now, Bitcoin's transaction data is easy to obtain, so de-anonymizing Bitcoin becomes possible. This paper constructs a data set of Bitcoin addresses including 5 categories, analyzes and extracts the transaction features of Bitcoin addresses in more detail based on related work, and proposes two new features of fourth-order transaction moments and sample distribution. New features improve the performance of Bitcoin address classification. The accuracy of the LightGBM model was 0.94 and the F1 score was 0.91. This method can identify unknown types of Bitcoin addresses, which improves the ability of relevant agencies to investigate Bitcoin illegal activities.

Citation Download Citation

Lu Qin, Li Yi, Xiancheng Lin, and Ziqiang Luo "Research on Bitcoin address classification based on transaction history features", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125870N (22 February 2023); https://doi.org/10.1117/12.2667252

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