计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 170-178.doi: 10.11896/jsjkx.210600092
杜航原1, 李铎1, 王文剑1,2
DU Hang-yuan1, LI Duo1, WANG Wen-jian1,2
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。
中图分类号:
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