As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem.

翻译：随着以太坊智能合约攻击事件日益增多，其对金融体系与信用环境已产生严重影响。当前基于代码解析或人工特征提取的反欺诈检测技术虽具备一定泛化与适应能力，但仍存在局限性。为此，本文提出采用图表示学习技术识别交易模式并区分恶意交易合约，即将以太坊交易数据表征为图结构，进而运用先进机器学习技术获取可靠且精准的检测结果。针对样本不平衡问题，我们采用SMOTE-ENN方法进行处理，并对多种模型进行测试，其中MLP的表现优于GCN，但具体效果仍需通过实际场景验证。本研究为以太坊生态系统的可信性与安全性开拓了更多可能性。