同种类的
计算机科学
分析
块链
图形
异常检测
数据库事务
数字加密货币
数据挖掘
理论计算机科学
数据库
计算机安全
数学
组合数学
作者
Chengxiang Jin,Jie Jin,Jiajun Zhou,Jiajing Wu,Qi Xuan
出处
期刊:IEEE Transactions on Circuits and Systems Ii-express Briefs
[Institute of Electrical and Electronics Engineers]
日期:2022-05-25
卷期号:69 (9): 3919-3923
被引量:20
标识
DOI:10.1109/tcsii.2022.3177898
摘要
While blockchain technology triggers new industrial and technological revolutions, it also brings new challenges. Recently, a large number of new scams with a “blockchain” sock-puppet continue to emerge, such as Ponzi schemes, money laundering, etc., seriously threatening financial security. Existing fraud detection methods in blockchain mainly concentrate on manual features and graph analytics, which first construct a homogeneous transaction graph using partial blockchain data and then use graph analytics to detect anomaly, resulting in a loss of pattern information. In this brief, we mainly focus on Ponzi scheme detection and propose HFAug , a generic Heterogeneous Feature Augmentation module that can capture the heterogeneous information associated with account behavior patterns and can be combined with existing Ponzi detection methods. HFAug learns the metapath-based behavior characteristics in an auxiliary heterogeneous interaction graph, and aggregates the heterogeneous features to corresponding account nodes in the homogeneous one where the Ponzi detection methods are performed. Comprehensive experimental results demonstrate that our HFAug can help existing Ponzi detection methods achieve significant performance improvement on Ethereum datasets, suggesting the effectiveness of heterogeneous information on detecting Ponzi schemes.
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