Title: Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models Show Chinese title

Title: 超越静态数据集：一种行为驱动的实体特定模拟，以克服数据稀缺性并训练有效的加密货币反洗钱模型

Abstract: For different factors/reasons, ranging from inherent characteristics and features providing decentralization, enhanced privacy, ease of transactions, etc., to implied external hardships in enforcing regulations, contradictions in data sharing policies, etc., cryptocurrencies have been severely abused for carrying out numerous malicious and illicit activities including money laundering, darknet transactions, scams, terrorism financing, arm trades. However, money laundering is a key crime to be mitigated to also suspend the movement of funds from other illicit activities. Billions of dollars are annually being laundered. It is getting extremely difficult to identify money laundering in crypto transactions owing to many layering strategies available today, and rapidly evolving tactics, and patterns the launderers use to obfuscate the illicit funds. Many detection methods have been proposed ranging from naive approaches involving complete manual investigation to machine learning models. However, there are very limited datasets available for effectively training machine learning models. Also, the existing datasets are static and class-imbalanced, posing challenges for scalability and suitability to specific scenarios, due to lack of customization to varying requirements. This has been a persistent challenge in literature. In this paper, we propose behavior embedded entity-specific money laundering-like transaction simulation that helps in generating various transaction types and models the transactions embedding the behavior of several entities observed in this space. The paper discusses the design and architecture of the simulator, a custom dataset we generated using the simulator, and the performance of models trained on this synthetic data in detecting real addresses involved in money laundering. Show Chinese abstract

Abstract: 由于各种因素/原因，从提供去中心化、增强隐私、交易便捷等固有特性，到执行法规的隐含外部困难、数据共享政策的矛盾等，加密货币已被严重滥用，用于进行包括洗钱、暗网交易、诈骗、恐怖主义融资、军火交易等多种恶意和非法活动。 然而，洗钱是需要减轻的关键犯罪，以暂停其他非法活动的资金流动。 每年有数十亿美元被洗钱。 由于目前存在许多分层策略，以及洗钱者使用的快速演变的战术和模式，使得在加密货币交易中识别洗钱变得极其困难。 已经提出了许多检测方法，从涉及完全人工调查的简单方法到机器学习模型。 然而，可用于有效训练机器学习模型的数据集非常有限。 此外，现有的数据集是静态的且类别不平衡，由于缺乏对不同需求的定制，给可扩展性和特定场景的适用性带来了挑战。 这在文献中一直是一个持续的挑战。 在本文中，我们提出了一种行为嵌入的实体特定洗钱类似交易模拟，有助于生成各种交易类型，并建模嵌入该领域中观察到的多个实体行为的交易。 本文讨论了模拟器的设计和架构、我们使用模拟器生成的自定义数据集，以及在该合成数据上训练的模型在检测涉及洗钱的真实地址方面的性能。