用图学习识别以太坊恶意合约，提升安全检测准确率
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain
- 将交易数据建模为图结构，利用机器学习挖掘欺诈模式
- 在样本不均衡下使用SMOTE-ENN处理，MLP表现优于GCN
- 适合区块链安全研究者与智能合约开发者参考
随着以太坊智能合约攻击事件频发，金融安全与系统可信度受到严重威胁。现有反欺诈检测技术如代码解析或人工特征提取仍存在局限性，尽管具备一定泛化能力。为此，本文提出基于图表示学习的方法，将以太坊交易数据转化为图结构，借助先进机器学习技术识别恶意交易合约。针对样本不平衡问题，采用SMOTE-ENN进行处理，并对比多种模型性能；结果显示，MLP表现优于GCN，具体效果仍需实际场景验证。本研究为以太坊生态的安全与信任建设提供了新思路。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。