论文标题
以太坊中蓬齐检测的异质特征增强
Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum
论文作者
论文摘要
区块链技术触发了新的工业和技术革命，但它也带来了新的挑战。最近，许多带有“区块链”袜子的新骗局继续出现，例如庞氏骗局，洗钱等，严重威胁着财务安全。区块链中的现有欺诈检测方法主要集中于手动特征和图形分析，这些功能和图形分析首先使用部分区块链数据构建均匀的事务图，然后使用图形分析检测异常，从而导致模式信息丢失。在本文中，我们主要关注庞氏骗局方案检测并提出HFAUG，这是一种通用的异质特征增强模块，可以捕获与帐户行为模式相关的异质信息，并可以与现有的庞氏骗子检测方法结合使用。 Hfaug在辅助异质相互作用图中学习了基于Metapath的行为特征，并将异质特征汇总到均质的ponzi检测方法中的相应帐户节点中的异质特征。全面的实验结果表明，我们的HFAUG可以帮助现有的庞氏骗检测方法对以太坊数据集进行了显着的性能提高，这表明异质信息对检测庞氏骗局的有效性。
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 feature 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 paper, 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.
