基于交易历史特征的比特币地址分类研究

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI:10.1117/12.2667252

Lu Qin, Li Yi, Xiancheng Lin, Ziqiang Luo

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引用次数: 0

摘要

作为目前最流行的加密货币，比特币的交易数据很容易获得，因此比特币的去匿名化成为可能。本文构建了一个包含5类比特币地址的数据集，在相关工作的基础上更详细地分析提取了比特币地址的交易特征，并提出了四阶交易矩和样本分布两个新特征。新功能提高了比特币地址分类的性能。LightGBM模型的准确率为0.94,F1评分为0.91。这种方法可以识别未知类型的比特币地址，提高了相关机构调查比特币非法活动的能力。

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Research on Bitcoin address classification based on transaction history features

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.

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Third International Seminar on Artificial Intelligence, Networking, and Information Technology

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