论文标题
在标签稀缺的情况下，在比特币区块链中检测洗钱的机器学习方法
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
论文作者
论文摘要
每年，罪犯从严重的重罪（例如恐怖主义，毒品走私或人口贩运）中获得数十亿美元的洗钱，损害了无数人和经济。特别是加密货币已发展为洗钱活动的避风港。机器学习可用于检测这些非法模式。但是，标签是如此稀缺，以至于传统的监督算法是不适用的。在这里，我们解决了洗钱检测，假设对标签的访问最少。首先，我们表明使用无监督的异常检测方法现有的最新解决方案不足以检测实际比特币交易数据集中的非法模式。然后，我们表明我们提出的主动学习解决方案能够通过仅使用5％的标签来匹配完全监督的基线的性能。该解决方案模仿了典型的现实生活，其中可以通过专家手动注释获得有限的标签。
Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5\% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.
