Due to the increasing abuse of fraudulent activities that result in significant financial and reputational harm, Ethereum smart contracts face a significant problem in detecting fraud. Existing monitoring methods typically rely on lease code analysis or physically extracted features, which suffer from scalability and adaptability limitations. In this study, we use graph representation learning to observe purchase trends and find fraudulent deals. We can achieve powerful categorisation performance by using innovative machine learning versions and transforming Ethereum invoice data into graph structures. Our method addresses label imbalance through SMOTE-ENN techniques and evaluates models like Multi-Layer Perceptron ( MLP ) and Graph Convolutional Networks ( GCN). Experimental results show that the MLP type surpasses the GCN in this environment, with domain-specific assessments closely aligned with real-world assessments. This study provides a scalable and efficient way to improve Ethereum's ecosystem's confidence and security.

翻译：由于欺诈活动日益猖獗，导致严重的财务与声誉损害，以太坊智能合约在欺诈检测方面面临严峻挑战。现有监测方法通常依赖于租赁代码分析或人工提取特征，存在可扩展性与适应性不足的问题。本研究采用图表示学习技术来识别交易模式并发现欺诈行为。通过创新的机器学习模型将以太坊交易数据转化为图结构，我们实现了强大的分类性能。该方法采用SMOTE-ENN技术处理标签不平衡问题，并评估了多层感知器（MLP）与图卷积网络（GCN）等模型。实验结果表明，在此场景下MLP模型性能优于GCN，领域特定评估结果与现实场景评估高度吻合。本研究为提升以太坊生态系统的可信度与安全性提供了一种可扩展且高效的解决方案。