MixGuard：面向以太坊混币器洗钱检测与理解
MixGuard: Towards Detecting and Understanding Mixer Laundering on Ethereum
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中文总结 AI 辅助
本文首次系统研究以太坊混币器洗钱，构建公开数据集MixLaunder，并提出MixGuard方法，结合三视图表示学习与两阶段分组，实现高精度检测与案例分组。
中文摘要 AI 辅助
混币器通过隐藏存款与取款之间的关联来保护隐私，但也常被滥用以清洗非法资金。现有的反洗钱研究并未专门针对混币器洗钱，而混币器研究则侧重于去匿名化而非识别与洗钱相关的交易。公开报告零散分布，缺乏公开的案例级数据集以进行系统性测量和检测。为填补这一空白，本文首次对以太坊上的混币器洗钱进行了全面研究。我们首先构建了MixLaunder，这是首个公开的混币器洗钱案例级数据集。该数据集涵盖2020年至2025年间涉及Tornado Cash和Railgun的27个案例，并标注了9,300笔与洗钱相关的交易，包含案例身份及可观测的上游和下游资金流向，其中存款总额约为11亿美元。通过将这些交易与背景混币器使用情况进行比较，我们识别出五种常见策略，表明洗钱证据跨越互补的行为和资金流背景，而同案例活动在局部紧密但在爆发期间跨连接较弱。我们的分析进一步揭示了混币器侧风险筛查和代表性去匿名化启发式方法的覆盖缺口。在这些发现的指导下，我们开发了MixGuard，它结合三视图表示学习与两阶段分组，用于交易级检测和案例感知分组。在严格的案例级留出评估下，MixGuard优于代表性基线，实现了97.89%的检测精确率和98.73%的分组纯度，而其前十个分组平均覆盖每个案例交易的95.09%。
英文摘要
Mixers protect privacy by concealing deposit--withdrawal links, but are also abused to launder illicit funds. Existing anti-money laundering studies do not specifically target mixer laundering, while mixer research focuses on deanonymization rather than identifying laundering-related transactions. Public reports remain fragmented, leaving no public case-level dataset for systematic measurement and detection. To fill this void, this paper presents the first comprehensive study of mixer laundering on Ethereum. We first construct \textsc{MixLaunder}, the first public case-level dataset of mixer laundering. It covers 27 cases involving Tornado Cash and Railgun from 2020 to 2025 and labels 9,300 laundering-related transactions with case identities and observable upstream and downstream fund flows, including deposits totaling approximately \$1.1 billion. By comparing these transactions with background mixer usage, we identify five common strategies, showing that laundering evidence spans complementary behavioral and fund-flow contexts, while same-case activity is locally tight but weakly connected across bursts. Our analysis further reveals coverage gaps in mixer-side risk screening and representative deanonymization heuristics. Guided by these findings, we develop \textsc{MixGuard}, which combines tri-view representation learning with two-stage grouping for transaction-level detection and case-aware grouping. Under strict case-level holdout evaluation, \textsc{MixGuard} outperforms representative baselines, achieving 97.89\% detection precision and 98.73\% group purity, while its top ten groups cover 95.09\% of each case's transactions on average.
发表机构
- Monash University（蒙纳士大学）
- Imperial College London（帝国理工学院）
- Griffith University（格里菲斯大学）
- CSIRO（澳大利亚联邦科学与工业研究组织）
- The University of Manchester（曼彻斯特大学）
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