摘要
针对电子商务中用户身份易被窃取冒用这一问题,设计研究了利用鼠标输入行为特征进行身份识别的方法,通过采集网上购物过程中用户的鼠标行为数据,使用聚类算法进行鼠标行为模式的固化,通过比较鼠标行为特征向量间的距离进行用户行为合法性判断。方法应用在电子商务系统,误检率与漏检率均在可接受范围内,可作为电子商务中用户身份认证的一种新的辅助手段。
The phenomenon of identity theft in e- commerce frequently happens, and credible problem has aroused wide public concern. In order to solve this problem, this paper discusses the method of identity authentication and anomaly detection by using the feature of mouse behavior. Mouse behavior data is collected during shopping and clustering algorithm is used to build the normal mouse behavior pattern. The distance between feature vectors is compared with the defined threshold to differentiate legal and illegal users. This method can be used as a new auxiliary method in user identity authentication in e-commerce, with low FAR and FRR.
出处
《电脑知识与技术》
2016年第1Z期241-242,245,共3页
Computer Knowledge and Technology
关键词
电子商务
鼠标行为
异常检测
身份认证
特征向量
e-commerce
mouse behavior
anomaly detection
identity authentication
feature vector
