While transactions with cryptocurrencies such as Ethereum are becoming more prevalent, fraud and other criminal transactions are not uncommon. Graph analysis algorithms and machine learning techniques detect suspicious transactions that lead to phishing in large transaction networks. Many graph neural network (GNN) models have been proposed to apply deep learning techniques to graph structures. Although there is research on phishing detection using GNN models in the Ethereum transaction network, models that address the scale of the number of vertices and edges and the imbalance of labels have not yet been studied. In this paper, we compared the model performance of GNN models on the actual Ethereum transaction network dataset and phishing reported label data to exhaustively compare and verify which GNN models and hyperparameters produce the best accuracy. Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges. We showed that heterogeneous models had better model performance than homogeneous models. In particular, the RGCN model achieved the best performance in the overall metrics.
翻译:虽然电子交易网络等有密码的交易日益普遍,但欺诈和其他犯罪交易并不罕见。图表分析算法和机器学习技术发现可疑交易,导致大型交易网络的网状钓鱼。许多图形神经网络模型(GNN)建议对图形结构应用深层学习技术。虽然在Etheum交易网络中,正在研究使用GNN模型进行网目探测,但尚未研究处理顶点和边缘数量以及标签不平衡的模型。在本文件中,我们比较了GNN模型在实际的Etheum交易网络数据集上的模型性能和所报告标签数据的模拟性能,以便详尽地比较和核实GNNN模型和超参数产生的最佳准确性。具体地说,我们评估了具有代表性的、单一式GNNN模型的模型性能,这些模型考虑到单型节点和边缘,以及支持不同类型节点和边缘的混合性GNNN模型。我们发现,混合模型的模型性能优于同型模型。特别是RGCN模型在总体指标中取得了最佳的性能。