摘要
比特币由于其便捷性、匿名性、全球性、高流动性的特点,为犯罪分子使用其作为价值传递的媒介从事犯罪活动提供了理想的工具,产生大量利用比特币进行勒索、洗钱、非法毒品、武器交易等异常交易问题。传统基于有监督的异常地址识别方法由于交易信息单一,不能全面和准确地反映地址间的关系,异常地址识别率较低。该文提出了一种基于交易网络特征增强的比特币异常地址识别方法。该方法将比特币交易数据转化为复杂网络,并提出一种基于改进的PageRank的节点重要性特征构造方法,根据比特币交易特点,引入比特币交易额度和频率相关性得到新的PageRank值并加入特征集。通过对不同的机器学习方法进行比较以获得最佳的预测模型,提升检测模型的分类效果。与传统的检测方法相比,结合网络信息的模型具有更好的检测性能,其中极限梯度提升树(XGBoost)分类器效果最好,F1分数由原来的0.83提升至0.94,AUC值由原来的0.88提升至0.95。
Because of its convenience,anonymity,globality and high mobility,Bitcoin provides an ideal tool for criminals to use it as a medium of value transmission to engage in criminal activities,resulting in a large number of abnormal transactions such as extortion,money laundering,illegal drugs and weapons trading.The traditional method of anomaly address recognition based on supervision cannot fully and accurately reflect the relationship between addresses due to the single transaction information,so the recognition rate of anomaly address is low.Therefore,we propose a Bitcoin anomaly address recognition method based on transaction network feature enhancement.This method converts Bitcoin transaction data into a complex network and extracts network features,and proposes a node importance feature construction method based on improved PageRank.According to Bitcoin transaction features,the Bitcoin transaction quota and frequency correlation are introduced to obtain new PR values and add them to the feature collection.By comparing different machine learning methods,we can get the best prediction model and improve the classification effect of the detection model.Compared with the traditional detection methods,the model combined with network information has a better detection performance.Among them,the XGBoost classifier has the best performance.The F1 score increases from 0.83 to 0.94,and the AUC value increases from 0.88 to 0.95.
作者
张梦楠
吴礼发
ZHANG Meng-nan;WU Li-fa(School of Cyberspace Security,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
出处
《计算机技术与发展》
2023年第9期8-15,共8页
Computer Technology and Development
基金
国家重点研发计划项目(2019YFB2101704)。
关键词
比特币
异常地址识别
机器学习
特征提取
网络科学
Bitcoin
abnormal address recognition
machine learning
feature extraction
network science
