基于位置信息的移动终端用户异常检测

Anomaly Detection Method of Mobile Terminal User Based on Location Information

针对当前轨迹异常检测中轨迹演化和检测结果类型单一的问题,结合用户历史行为模式、群体结构信息和近邻用户行为,提出一种基于位置信息的移动终端用户异常检测方法。该方法将位置数据转换为时空共现区,进一步挖掘用户行为模式,提取用户群体结构信息。在此基础上,根据历史行为模式异常、伴随行为模式异常、时空共现区行为模式异常、时空共现区流量模式异常和异常用户群体属性5种异常特征,采用随机森林方法构建多分类异常检测模型,识别移动终端用户个体异常、群体异常、时空异常和事件异常现象。在真实数据集上的实验结果表明,所提方法可以有效识别移动终端用户的轨迹演化行为,检测多种类型的异常现象,与同类方法相比具有较高的召回率和较低的误差率。

Aiming at the problem of trajectory evolution and single-type of detection result in trajectory anomaly detection technology,an anomaly detection method was proposed for mobile terminal user based on location information,which comprehensively utilizes the user historical behavior pattern,group structure information,and behavior of close users.The method converts the location data into the spatio-temporal co-occurrence area(STCOA),and further excavates the user behavior pattern and extractes the user group structure information.On this basis,a multi-class anomaly detection model was constructed by random forest method according to five abnormal characteristics of historical beha- vior pattern anomaly,accompanying behavior pattern anomaly,STCOA behavior pattern anomaly,STCOA flow pattern anomaly and group attribute of abnormal users.This model can identify individual anomaly,group anomaly,spatio-temporal anomaly and event anomaly of mobile terminal users.Experiments on real data sets show that the proposed me- thod can effectively identify the trajectory evolution behavior and detect various types of anomalies of mobile terminal users .Compared with the similar methods,this method has higher recall rate and lower error rate.