基于CoinJoin实现的混币交易检测方法——以Wasabi平台为例

doi:10.11959/j.issn.2096-109x.2023089

摘要/Abstract

摘要：

混币技术为加强用户交易隐私而设计，但对“加密货币”监管使用的地址聚类规则造成严重干扰，因而常被黑客用作洗钱工具实现资金逃逸，这引发了金融监管部门对混币交易检测的关注。目前混币交易检测工作大多停留在单纯的数据分析与统计阶段，对混币交易的内部细节缺乏清晰的认知，并且没有可信的验证数据，因而检测方法的效果缺少可信度。CoinJoin是去中心化混币的代表性思想，Wasabi是基于此思想实现的商用方案中比较流行的一种。结合CoinJoin概念及其限制交易匿名集大小和混币金额的特点，提出了CoinJoin混币交易的通用检测方法：CoinJoin混币交易为多输入多输出交易，具有交易输出项个数大于输入中UTXO个数和输出项金额中存在大量重复值的特点。将CoinJoin的通用检测方法与相关研究中提到的Wasabi混币交易的部分特征相结合，得到了对于Wasabi的基础检测方法并完成检测。从Wasabi平台服务接口处获得可信验证数据集，对该数据集分析并完成两项工作：一是对 Wasabi 基础检测方法中的规则参数进行调准；二是提出交易输出项金额中重复值的最高频次与输入中 UTXO 个数比值的新指标，该指标可用来衡量用户参与混币金额的自由度。在这两项工作的基础上，得到用于Wasabi的改进检测方法。实验表明：Wasabi基础检测方法的召回率为94.2%，准确率为67.2%；经过可信验证数据集的分析反馈，改进检测方法的召回率达到 100%且准确率在 99%以上。根据通用检测方法对整个CoinJoin类型的混币交易的总市场规模进行预测评估，得到结论：当今市场中CoinJoin混币交易占所有比特币交易的数量上限为1.9‰，金额上限为3.7‰。

关键词: CoinJoin, Wasabi, 混币交易检测, 地址聚类

Abstract:

Designed to enhance the privacy of user transactions, mixed coin technology has created disruptions to the address clustering rules typically used for cryptocurrency regulation.Consequently, hackers have begun utilizing mixed coin technology as a tool for money laundering and fund evasion, which has raised concerns among financial regulators regarding the detection of mixed coin transactions.Currently, most detection methods for mixed coin transactions rely on data analysis and statistics, lacking a comprehensive understanding of the internal workings of these transactions.This lack of knowledge may undermine the credibility and effectiveness of detection methods due to the absence of reliable verification data.CoinJoin, a decentralized mixed coin concept, represents one approach, and commercial implementations like Wasabi have gained popularity.Drawing from the characteristics of CoinJoin and its restriction on the size of anonymous transaction sets and mixed coin amounts, a general detection method for CoinJoin mixed coin transactions was devised.Such transactions typically involved multiple inputs and outputs, with more output items than UTXOs in the input, and a high occurrence of duplicate values among the output amounts.A basic detection method for Wasabi was developed by combining the generic detection method for CoinJoin with specific features of Wasabi, as identified in related studies, to complete the detection process.A trusted validation dataset was acquired from the Wasabi platform service interface, and this dataset was analyzed to achieve two objectives.First, the alignment of rule parameters in the Wasabi base detection method was accomplished.Second, a new metric was proposed, measuring the ratio of the highest frequency of duplicate values in the output amount of transactions to the number of UTXOs in the input.This metric assessed the level of user participation in mixed coin transactions, providing a measure of user freedom.Using these two approaches, significant progress is made in the detection of mixed coin transactions.The experiments show that the recall rate of Wasabi’s basic detection method is 94.2% and the accuracy rate is 67.2%.After the analytical feedback from the credible validation dataset, the recall rate of the improved detection method reaches 100% and the accuracy rate is above 99%.The total market size of the entire CoinJoin type of mixed coin transactions was evaluated and predicted based on a common test methodology.It is concluded that the number of CoinJoin mixed coin transactions in today’s mixed coin market represents 1.9 per 1 000 of all Bitcoin transactions and 3.7 per 1 000 of the transaction value at most.

Key words: CoinJoin, Wasabi, mixed cointransaction detection, address clustering

中图分类号:

TP309

李虎, 陈云芳, 张伟. 基于CoinJoin实现的混币交易检测方法——以Wasabi平台为例[J]. 网络与信息安全学报, 2023, 9(6): 140-153.

Hu LI, Yunfang CHEN, Wei ZHANG. Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example[J]. Chinese Journal of Network and Information Security, 2023, 9(6): 140-153.

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训练集组成部分	测试集组成部分	测试集交易个数	检测集交易个数	正确检测个数	漏检个数	误检个数	检测准确率	检测召回率
1、2、3、4、5、6、7	8、9、10	380	382	380	0	2	99.5%	100%
1、3、4、6、7、8、9	2、5、10	378	381	378	0	3	99.2%	100%
1、2、3、5、7、8、10	4、6、9	378	380	378	0	2	99.5%	100%
1、2、5、6、7、9、10	3、4、8	380	381	380	0	1	99.7%	100%
2、3、4、6、8、9、10	1、5、7	379	383	379	0	4	99.0%	100%