【Author】
Zhang, Yuhang; Wang, Jun; Zhao, Fei
【Source】2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020)
【Abstract】The emergency of anonymous encrypted digital currency based on blockchain brings the rapid growth of financial crimes simultaneously. However, under the condition of Know Your Customer rules, the traditional rule-based filtering and supervised pattern recognition methods are mainly built, which does not apply to the scenario of anonymous encrypted digital currency. In this paper, we attempt to tackle this problem by constructing user graph from transactions and dividing the whole user graph into tightly connected communities and clustering similar communities into groups. Experimental results on bitcoin transaction datasets show that the proposed approach has higher than 92% precision and higher than 73% recall for identifying gambling and mining pool communities.
【Keywords】community discovery; transaction community identification; outlier; Abnormal transaction node detection
【摘要】基于区块链的匿名加密数字货币的出现，同时也带来了金融犯罪的快速增长。然而，在“了解您的客户”规则条件下，主要构建传统的基于规则的过滤和监督模式识别方法，不适用于匿名加密的数字货币场景。在本文中，我们试图通过从事务中构建用户图，并将整个用户图划分为紧密连接的社区和将相似的社区聚类为组来解决这个问题。在比特币交易数据集上的实验结果表明，该方法识别赌博和挖矿池社区的准确率高于92%，召回率高于73%。
【关键词】社区发现;社区事务识别;离群值;异常事务节点检测
评论