基于XGBoost方法的社交网络异常用户检测技术  

Research on abnormal user detection technology in social network based on XGBoost method

作　　者：袁丽欣 顾益军 赵大鹏 Yuan Lixin;Gu Yijun;Zhao Dapeng(School of Information Technology&Network Security Enforcement,People’s Public Security University of China,Beijing 102600,China)

机构地区：[1]中国人民公安大学信息技术与网络安全学院,北京102600

出　　处：《计算机应用研究》2020年第3期814-817,共4页Application Research of Computers

基　　金：国家重点研发计划资助项目(2017YFC0820100)。

摘　　要：针对传统社交网络异常用户检测算法应用于现实中非平衡数据集时存在召回率低、运行效率低等问题,对社交网络数据集提取用户内容、行为、属性、关系特征,应用梯度增强集成分类器XGBoost算法进行特征选择,建立分类模型,构造非平衡数据集并识别三类垃圾广告发送账号。实验结果表明,该方法与随机森林等传统分类方法相比,对平衡及非平衡数据集进行异常用户检测均实现召回率和F 1值的有效提升;同时其选取少量特征同样可达到较高检测水平,证明了该方法的有效性。Aiming at the problems of low recall rate and poor running efficiency caused by traditional abnormal accounts detecting algorithms in non-balanced social network datasets,the paper extracted user content,behavior,attributes,and relationship features from social network data sets,selected features using gradient-enhanced ensemble classifier XGBoost algorithm,established classification model,constructed unbalanced data sets and realized the identification of three types of spam accounts.Experimental results show that it improves the recall rate and the F 1 value in identification of three types of abnormal users effectively by XGBoost algorithm in binary classification and multiple classification tasks both in the balanced and unbalanced dataset in comparison with the traditional classification methods such as random forest.And with few features selected by XGBoost,the classification algorithm can get the same effect as with all features of samples,which proves the effectiveness of this method.

关 键 词：XGBoost 社交网络 异常用户检测 异常账号检测 垃圾广告发送者 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]