刑事技术2026，Vol.51Issue(3)：260-265,6.DOI:10.16467/j.1008-3650.2025.0015
一种基于特征增强的图神经网络比特币非法交易检测方法
A Bitcoin Illegal Transaction Detection Method Based on Feature-Enhanced Graph Neural Networks
摘要
Abstract
Detection of Bitcoin illegal transactions is a significant challenge in blockchain technology,particularly when faced with complex transaction patterns and issues of class imbalance.This paper proposes a feature-enhanced graph neural network approach aimed at improving the accuracy of Bitcoin illegal transaction detection.First,the BERT model is employed to enhance transaction features,leveraging its powerful contextual modeling capabilities to extract more expressive features.Second,an innovative model architecture is designed,combining LSTM with a dual-channel ONGNNConv,where the latter passes its output to an attention mechanism for weighted aggregation,thereby enabling more effective capture of latent patterns in the transaction network.Finally,to address the class imbalance problem,a weighted binary cross-entropy loss is incorporated into the loss function to enhance the detection of illegal transactions.Experimental results demonstrate that the proposed method outperforms existing baseline models across multiple evaluation metrics,validating its effectiveness and robustness in Bitcoin illegal transaction detection.关键词
比特币非法交易/图神经网络/特征增强/BERT模型Key words
Bitcoin illegal transactions/graph neural network/feature enhancement/BERT model分类
社会科学引用本文复制引用
姜贤波,康艳荣,邢桂东,冯冉,鄢飞,吴昊,严圣东..一种基于特征增强的图神经网络比特币非法交易检测方法[J].刑事技术,2026,51(3):260-265,6.基金项目
中央级公益性科研院所基本科研业务费专项资金项目(2022JB033) （2022JB033）
公安部科技强警基础工作专项(2022JC15) （2022JC15）
