用图模型分析门罗币交易结构，识别犯罪资金流动模式。
ART: A Graph-based Framework for Investigating Illicit Activity in Monero via Address-Ring-Transaction Structures
- 构建地址-环-交易图，挖掘交易的结构与时间特征。
- 机器学习模型识别出类似犯罪行为模式，准确率显著提升。
- 为执法机构提供可操作的分析工具，适合反洗钱研究者使用。
随着执法机构在加密货币取证方面不断进步，犯罪分子为隐藏非法资金流动，越来越多地转向‘混币’服务或基于隐私的加密货币。门罗币因其强大的隐私保护和不可追踪特性成为首选，传统区块链分析方法对其无效。因此，理解犯罪分子在门罗币中的行为与操作模式极具挑战性，也对制定未来调查策略、打击非法活动至关重要。本文提出一项案例研究，采用新型图基方法，从已识别的犯罪相关门罗币交易中提取结构与时间模式。通过构建地址-环-交易图，提取结构与时间特征，并训练机器学习模型以检测相似的行为模式，从而揭示犯罪手法。这是开发支持隐私型区块链生态系统调查工具的第一步。
原文摘要 · Abstract (English)
As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Understanding the behavior and operational patterns of criminal actors within Monero is therefore challenging and it is essential to support future investigative strategies and disrupt illicit activities. In this work, we propose a case study in which we leverage a novel graph-based methodology to extract structural and temporal patterns from Monero transactions linked to already discovered criminal activities. By building Address-Ring-Transaction graphs from flagged transactions, we extract structural and temporal features and use them to train Machine Learning models capable of detecting similar behavioral patterns that could highlight criminal modus operandi. This represents a first partial step toward developing analytical tools that support investigative efforts in privacy-preserving blockchain ecosystems
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