摘要
在电商网络中,异常用户往往表现出与正常用户截然不同的行为特征,检测异常用户并分析其行为模式对维护电商平台秩序具有十分重要的现实意义。通过分析异常用户的行为模式,将电商网络抽象为异质信息网络并转化为用户-设备二分图,然后在此基础上提出了一种面向电商网络的异常用户检测方法——自监督异常检测模型(Self-Supervised Anomaly Detection Model,S-SADM)。该方法具有自监督学习机制,采用自编码器编码获取用户节点表示,通过优化联合目标函数来完成反向传播,同时采用支持向量数据描述对用户节点表示进行异常检测。经过网络的自动迭代优化,不仅使用户节点表示具有监督信息,还获得了较稳定的检测结果。最后,在真实网络数据集和半合成网络数据集中对S-SADM进行实验,结果表明了该方法的有效性和优越性。
In the e-commerce network, abnormal users often show different behavioral characteristics from normal users.Detecting abnormal users and analyzing their behavior patterns is of great practical significance to maintaining the order of e-commerce platforms.By analyzing the behavior patterns of abnormal users, we abstract the e-commerce network into the heterogeneous information network, and convert it into a user-device bipartite graph.On this basis, we propose a method for detecting abnormal users oriented to e-commerce network--self-supervised anomaly detection model(S-SADM).The model has a self-supervised learning mechanism.It uses an autoencoder to encode the user-device bipartite graph to obtain user node representations.By optimizing the joint objective function, the model completes backpropagation, and uses support vector data descriptions to perform anomaly detection on user node representations.After the automatic iterative optimization of the network, the user node representation has supervised information, and we obtain relatively stable detection results.Finally, S-SADM is validated on 3 real network datasets and a semi-synthetic network dataset, and the experimental results demonstrate the effectiveness and superiority of the method.
作者
杜航原
李铎
王文剑
DU Hang-yuan;LI Duo;WANG Wen-jian(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University),Ministry of Education,Taiyuan 030006,China)
出处
《计算机科学》
CSCD
北大核心
2022年第7期170-178,共9页
Computer Science
基金
国家自然科学基金(61902227,62076154,U1805263)
中央引导地方科技创新项目(YDZX20201400001224)
山西省自然科学基金(201901D211192)
山西省高校科技创新项目(2019L0039)。
关键词
异常检测
电商网络
异质信息网络
自监督学习
自编码器
支持向量数据描述
Anomaly detection
E-commerce network
Heterogeneous information network
Self-supervised learning
Autoenco-der
Support vector data description
