基于时序交易图注意力神经网络的以太坊恶意账户检测

Detection of Malicious Ethereum Account Based on Time Series Transaction and Graph Attention Neural Network

随着区块链的迅速发展,利用以太坊从事传销、诈骗、洗钱等犯罪行为逐年增加,因此对于以太坊账户的检测成为了破解新型犯罪的一种有效方法。文章提出将交易时间信息融入到以太坊地址账户特征的模型,从而检测以太坊账户是否为恶意账户。模型对传统的注意力网络进行改进,通过融合时序交易时间图注意力的神经网络实现了地址账户特征的最终表达。实验结果表明,该模型优于传统的机器学习分类算法和图神经网络分类算法。

With the rapid development of blockchain,using ethereum to engage in pyramid selling,fraud,and money laundering crimes has increased year by year.Therefore,the detection of ethereum accounts has become an effective method to crack new types of crimes.The information was integrated into the characteristics of the ethereum address and account as a model to detect whether the account was a malicious one.The model in this paper improves the the neural network of graph attention mechanism and the time-series transaction information to realize the final expression of the address account characteristics.It is verified by experiments that the purposed model is superior to the graph neural network classification algorithm established by the traditional classification method.