The detection of scams within Ethereum smart contracts is a critical challenge due to their increasing exploitation for fraudulent activities, leading to significant financial and reputational damages. Existing detection methods often rely on contract code analysis or manually extracted features, which suffer from scalability and adaptability limitations. In this study, we introduce an innovative method that leverages graph representation learning to examine transaction patterns and identify fraudulent contracts. By transforming Ethereum transaction data into graph structures and employing advanced machine learning models, we achieve robust classification performance. Our method addresses label imbalance through SMOTE-ENN techniques and evaluates models like Multi-Layer Perceptron (MLP) and Graph Convolutional Networks (GCN). Experimental results indicate that the MLP model surpasses the GCN in this context, with real-world evaluations aligning closely with domain-specific analyses. This study provides a scalable and effective solution for enhancing trust and security in the Ethereum ecosystem.

翻译：以太坊智能合约中的诈骗检测是一项关键挑战，因其日益被用于欺诈活动，导致重大的财务和声誉损害。现有检测方法通常依赖于合约代码分析或手动提取特征，存在可扩展性和适应性局限。本研究提出一种创新方法，利用图表示学习分析交易模式并识别欺诈合约。通过将以太坊交易数据转换为图结构并采用先进的机器学习模型，我们实现了稳健的分类性能。该方法通过SMOTE-ENN技术处理标签不平衡问题，并评估了多层感知机（MLP）和图卷积网络（GCN）等模型。实验结果表明，在此场景下MLP模型优于GCN模型，实际评估结果与领域特定分析高度吻合。本研究为增强以太坊生态系统的信任与安全性提供了可扩展且有效的解决方案。