计算机与现代化Issue(10)：103-109,7.DOI:10.3969/j.issn.1006-2475.2025.10.016
面向隐蔽化挖矿行为识别的特征选择方法
Feature Selection Method for Recognizing Covert Mining Behavior
摘要
Abstract
As the Chinese government continues to regulate cryptocurrency mining activities,miners are increasingly concealing their operations through encryption,proxies,and other methods.Existing mining behavior monitoring techniques have lower ac-curacy when dealing with covert mining,making effective detection difficult.To address this problem,this paper proposes a co-vert mining behavior identification method based on RF-Voting.First,we collected and compiled a dataset of covert mining traf-fic and defined three types of covert mining behaviors.In the feature selection module for covert mining,the RF(Random For-est)feature selector interacts with the Voting classifier to select features,effectively identifying important ones.In the behavior matching module,we propose an enhanced Voting classifier with performance-aware selection and adaptive weight assignment.Performance-aware selection allows for screening high-performance base classifiers,while adaptive weight assignment dynami-cally adjusts the weights of the classifiers.By combining these two methods,we effectively improve the classification performance and stability of the model.Experimental results show that,compared to traditional mining detection methods,the accuracy of this method was increased by up to 6.18 percentage points,and the F1 score was increased by up to 9.35 percentage points,dem-onstrating that the RF-Voting method provides a more accurate and effective solution for monitoring covert mining behavior.关键词
隐蔽化挖矿/挖矿行为/挖矿流量/流量分类/机器学习Key words
convert mining/mining behavior/mining traffic/traffic classification/machine learning分类
计算机与自动化引用本文复制引用
何志涌,贺泽宇,张伟,柳国平..面向隐蔽化挖矿行为识别的特征选择方法[J].计算机与现代化,2025,(10):103-109,7.基金项目
国家重点研发计划项目(2022YFC3320903) （2022YFC3320903）
"北京未来区块链与隐私计算高精尖中心"和"国家经济安全预警工程北京实验室"资助项目 （）
