论文标题
以太坊中蓬齐检测的时间感知的Metapath功能增强
Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum
论文作者
论文摘要
随着Web 3.0强调权力下放的发展，区块链技术在其革命中引起了许多挑战，尤其是在加密货币领域。最近，许多犯罪行为不断出现在区块链上，例如庞氏骗局和网络钓鱼骗局，这极大地危害了分散的财务。基于图形的基于图的异常行为检测方法通常着重于构建均质交易图，而无需区分节点和边缘的异质性，从而导致部分损失交易模式信息。尽管现有的异质建模方法可以通过Metapaths描绘更丰富的信息，但提取的Metapath通常忽略了实体之间的时间依赖性，并且不能反映实际行为。在本文中，我们将时间感知的Metapath功能增强功能（TMFAUG）作为插件播放模块，以捕获以太坊上庞氏骗局方案检测过程中基于Metapath的真实交易模式。所提出的模块可以与现有的基于图的庞氏骗子检测方法自适应结合。广泛的实验结果表明，我们的TMFAUG可以帮助现有的庞氏骗检测方法在以太坊数据集上实现了显着的性能提高，这表明异质时间信息在庞氏骗局方案检测中的有效性。
With the development of Web 3.0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency. Recently, a large number of criminal behaviors continuously emerge on blockchain, such as Ponzi schemes and phishing scams, which severely endanger decentralized finance. Existing graph-based abnormal behavior detection methods on blockchain usually focus on constructing homogeneous transaction graphs without distinguishing the heterogeneity of nodes and edges, resulting in partial loss of transaction pattern information. Although existing heterogeneous modeling methods can depict richer information through metapaths, the extracted metapaths generally neglect temporal dependencies between entities and do not reflect real behavior. In this paper, we introduce Time-aware Metapath Feature Augmentation (TMFAug) as a plug-and-play module to capture the real metapath-based transaction patterns during Ponzi scheme detection on Ethereum. The proposed module can be adaptively combined with existing graph-based Ponzi detection methods. Extensive experimental results show that our TMFAug can help existing Ponzi detection methods achieve significant performance improvements on the Ethereum dataset, indicating the effectiveness of heterogeneous temporal information for Ponzi scheme detection.
