计算机工程与应用2024，Vol.60Issue(5)：271-280,10.DOI:10.3778/j.issn.1002-8331.2211-0395
高判别精度的区块链交易合法性检测方法
Blockchain Transaction Legitimacy Discrimination with High Recognition Accuracy
摘要
Abstract
Legitimacy discrimination of transactions on the blockchain is of great importance for the regulation of crypto-currencies.In order to effectively take into account the information of the transaction itself and the topological informa-tion in the discriminative process,and to improve the discrimination accuracy,this paper proposes a multi-perspective legitimacy detection method that incorporates the trustworthy deep forest.Firstly,a trustworthy deep forest(TForest)based on generating trustworthy features is designed.It gives sufficient discrimination to subsamples by feature reordering and combines variable sliding windows to extract differentiable subsamples in a balanced and confusion-free manner.The discrimination accuracy of the deep forest is improved on the basis of significantly reducing the dimensionality of generated features.Then,an ensemble strategy is designed.It uses a two-stage layer-by-layer optimization approach to effectively fuse three types of base discriminators,namely trustworthy deep forest,Transformer graph network and ResNet.The strategy is based on the difference of base models for positive and negative samples recognition ability,and utilizes two kinds of information,finally,a high-accuracy multi-perspective analysis model T2Rnet is constituted.The experimental results on the Elliptic dataset show that the F1-score of the model achieves 83.11% ,which is 31.6% higher than the baseline graph convolution method.The model has reliable transaction legitimacy discrimination performance.关键词
区块链/合法性检测/可信深度森林/神经网络/双阶段集成Key words
blockchain/legitimacy discrimination/trustworthy deep forest/neural network/two-stage ensemble strategy分类
信息技术与安全科学引用本文复制引用
蔡元海,宋甫元,黎凯,陈彦宇,付章杰..高判别精度的区块链交易合法性检测方法[J].计算机工程与应用,2024,60(5):271-280,10.基金项目
国家重点研发计划(2021YFB2700900). （2021YFB2700900）
