南方电网技术2024，Vol.18Issue(9)：78-87,105,11.DOI:10.13648/j.cnki.issn1674-0629.2024.09.009
基于深度PCA与贝叶斯优化的区块链异常交易检测
Blockchain Abnormal Transaction Detection Based on Deep PCA and Bayesian Optimization
摘要
Abstract
Due to the complex trading scenarios and rich trading modes of blockchain,blockchain transactions are frequently threatened by illegal behaviors such as anonymous attacks,Ponzi schemes,phishing attacks,etc.These abnormal behaviors cause huge economic risks to the development of smart grid based on blockchain technology.Aiming at the problem of poor comprehensive performance of blockchain abnormal transaction detection,the characteristics of high data dimensions and imbalanced positive and negative samples of transaction data are analyzed.A blockchain abnormal transactions detection method based on deep principal component analysis(PCA)and Bayesian optimization is proposed.By designing the deep PCA model,linear and nonlinear dimensionality reduction of blockchain transaction data is achieved.The Bayesian optimization is employed to optimize the random forest hyper-parameters,and the optimized random forest classifier is utilized to effectively solve the problem of unbalanced positive and negative samples.And finally abnormal transactions are detected in the blockchain.The experiments based on Elliptic and power grid blockchain transaction datasets show that the proposed method improves the comprehensive performance of blockchain abnormal transaction detection.关键词
区块链/异常交易检测/主成分分析/贝叶斯优化/随机森林Key words
blockchain/abnormal transaction detection/principal component analysis/Bayesian optimization/random forest分类
计算机与自动化引用本文复制引用
王栋,李达,王合建..基于深度PCA与贝叶斯优化的区块链异常交易检测[J].南方电网技术,2024,18(9):78-87,105,11.基金项目
国家重点研发计划资助项目(2018YFB0805005) （2018YFB0805005）
国网数字科技控股有限公司科技项目(9200/2023-72001B).Supported by the National Key Research and Development Program of China(2018YFB0805005) （9200/2023-72001B）
the Science and Technology Project of State Grid Digital Technology Holding Co.,Ltd.(9200/2023-72001B). （9200/2023-72001B）
