刑事技术2025，Vol.50Issue(5)：463-468,6.DOI:10.16467/j.1008-3650.2024.0062
一种基于多特征融合的比特币非法交易检测方法
A Method for Detecting Illegal Bitcoin Transactions Based on Multi-feature Fusion
摘要
Abstract
As digital currencies,notably,Bitcoin,gain widespread adoption,the detection of illicit transactions poses a pressing challenge that requires prompt resolution.This research introduces a novel approach for detecting illicit Bitcoin transactions that integrates diverse features to enhance both detection efficiency and accuracy.Firstly,a comprehensive feature set is constructed by amalgamating conventional data features with those uniquely derived from LSTM,RandomWalk,and PageRank algorithms,enabling the capture of intricate patterns within transaction data.Secondly,to address the class imbalance inherent in Bitcoin transaction datasets,FocalLoss is adopted as the loss function,strengthening the model's ability to discern minority classes(i.e.,illicit transactions).Finally,the model is validated on the Elliptic dataset using a multilayer perceptron(MLP)architecture with a single hidden layer,and its performance is compared with current mainstream Bitcoin illegal transaction detection models(GAT,GCN).Experimental results demonstrate that the proposed method achieves significant improvements in crucial metrics such as F1 score and recall rate compared to traditional methods,validating the effectiveness of the multi-feature fusion strategy and the utilization of FocalLoss in tackling the challenge of illegal Bitcoin transaction detection.关键词
特征融合/比特币/非法交易检测/不平衡数据集/深度学习Key words
feature fusion/Bitcoin/illegal transaction detection/imbalanced dataset/deep learning分类
社会科学引用本文复制引用
姜贤波,邢桂东,康艳荣,范玮,庄辰,严圣东..一种基于多特征融合的比特币非法交易检测方法[J].刑事技术,2025,50(5):463-468,6.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）
