基于交易特征对以太网多类型非法账户的分析与预测

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-06-06 Cooperative journals: 《计算机应用研究》

周健 闫石 张杰 黄世华

Abstract： The increasingly frequent illegal transactions hinder the secure transactions of Ethereum, and the anonymity of electronic currency makes it difficult to track and analyze illegal transactions. This paper used the transaction data of the Ethereum platform as the data source, the marked illegal account and unmarked normal account data set as the training set, and the characteristic attributes of the transaction data as the construction basis. Account for the overall forecast. The process uses the T-SNE algorithm to realize the dimensionality reduction and visualization of transaction features, adopts multiple cross-validation, and introduces the SHAP Value factor to judge the positive and negative attributes of the feature. The prediction effect accuracy rate of the established model reaches 94.29%. The evaluation metric for the area (AUC) value reached 0.9846. The proposed scheme can more accurately predict the illegal behavior on the Ethereum trading platform, and will effectively improve the blockchain-based trading environment.

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