基于位置信息的移动终端用户异常检测

doi:10.11896/j.issn.1002-137X.2019.03.027

摘要/Abstract

摘要： 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。

关键词: 轨迹演化, 位置数据, 移动终端, 异常分类, 异常特征

Abstract: 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.

Key words: Abnormal classification, Abnormal feature, Location data, Mobile terminal, Trajectory evolution

中图分类号:

TP309

李志,马春来,马涛,单洪. 基于位置信息的移动终端用户异常检测[J]. 计算机科学, 2019, 46(3): 180-187. https://doi.org/10.11896/j.issn.1002-137X.2019.03.027

LI Zhi, MA Chun-lai, MA Tao, SHAN Hong. Anomaly Detection Method of Mobile Terminal User Based on Location Information[J]. Computer Science, 2019, 46(3): 180-187. https://doi.org/10.11896/j.issn.1002-137X.2019.03.027

参考文献

[1]SHIKHA A,JITENDRA A.Survey on Anomaly Detection
using Data Mining Techniques[J].Procedia Computer Science,2015,60(1):708-713.
[2]LIU T B,LIU S P.Fraud detection model & application for credit card acquiring business based on data mining technology[C]∥4th International Conference on Electrical & Electronics Engineering and Computer Science.Atlantis Press,2016.
[3]CHRISTIANA I,VASOS V,CHARALAMPOS S.An Intrusion Detection System for Wireless Sensor Networks[C]∥International Conference on Telecommunications.2017.
[4]ZHOU J,YANG Y,DING S,et al.A Fault Detection and
Health Monitoring Scheme for Ship Propulsion Systems using SVM Technique[J].IEEE Access,2018,PP(99):1.
[5]WANG T,HICHEM S.Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram[J].IEEE Transa-ctions on Information Forensics & Security,2014,9(6):988-998.
[6]LI A,MIAO Z J,CEN Y G,et al.Anomaly detection using
sparse reconstruction in crowded scenes[J].Multimedia Tools &Applications,2017,76(24):26249-26271.
[7]ANIND D,JEFFREY H,EYALDE L,et al.Location-Based
Services[J].IEEE Pervasive Computing,2017,9(1):11-12.
[8]MAO J L,JIN C Q,ZHANG Z G,et al.Anomaly detection for trajectory big data:Advancements and framework[J].Journal of Software,2017,28(1):17-34.(in Chinese)
毛嘉莉,金澈清,章志刚,等.轨迹大数据异常检测:研究进展及系统框架[J].软件学报,2017,28(1):17-34.
[9]ZHENG Y U,LIU Y C,YUAN J,et al.Urban computing with taxicabs[C]∥International Conference on Ubiquitous Computing.ACM,2011.
[10]ZHENG Y.Urban computing and large data[J].Communication of China Computer Federation,2013,9(8):8-18.(in Chinese)
郑宇.城市计算与大数据[J].中国计算机学会通讯,2013,9(8):8-18.
[11]LIU H P,JIN C Q,ZHOU A Y.Popular Route Planning with Travel Cost Estimation[C]∥Proceedings,Part II,of the 21st International Conference on Database Systems for Advanced Applications-Volume 9643.2016.
[12]DUAN X Y,JIN C Q,WANG X L,et al.Real-Time Personali-
zed Taxi-Sharing[M].Springer International Publishing,2016.
[13]PANG L,CHAWLA S,LIU W,et al.On detection of emerging anomalous traffic patterns using GPS data[J].Data & Know-ledge Engineering,2013,87(9):357-373.
[14]REN M Q,SONG R X,WANG M,et al.Detection of Students’ Abnormal Behavior in the Intelligent Campus[J].Natural Science Journal of Harbin Normal University,2017,33(3):20-24.(in Chinese)
任孟其,宋汝鑫,王萌,等.面向智慧校园的学生异常行为检测[J].哈尔滨师范大学自然科学学报,2017,33(3):20-24.
[15]DONGHER S,MINGHUNG S,DAVIDC Y,et al.Personal
mobility pattern mining and anomaly detection in the GPS era[J].American Cartographer,2016,43(1):55-67.
[16]YANG S,ZHOU W B.Anomaly Detection on Collective Moving Patterns:Manifold Learning Based Analysis of Traffic Streams[C]∥IEEE Third International Conference on Privacy,Security,Risk and Trust.2012.
[17]PAN B,ZHENG Y,WILKIE D,et al.Crowd sensing of traffic anomalies based on human mobility and social media[C]∥ACM Sigspatial International Conference on Advances in Geographic Information Systems.2013.
[18]PANG L,CHAWLA S,LIU W,et al.On detection of emerging anomalous traffic patterns using GPS data[J].Data & Know-ledge Engineering,2013,87(9):357-373.
[19]EUNJOON C,SETHA M,JURE L.Friendship and mobility:user movement in location-based social networks[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Diego,CA,USA,2011.
[20]ZHAO G Z,QI J P,YU Y W,et al.Online check-in exception detection in mobile social networking[J].CAAI Transactions on Intelligent Systems,2017,12(5):752-759.(in Chinese)
赵冠哲,齐建鹏,于彦伟,等.移动社交网络异常签到在线检测算法[J].智能系统学报,2017,12(5):752-759.
[21]YU Y W,CAO L,ELKEA R,et al.Detecting moving object outliers in massive-scale trajectory streams[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.2014:422-431.
[22]CRANDALL D J,BACKSTROM L,COSLEY D,et al.Inferring social ties from geographic coincidences[J].Proceedings of the National Academy of Sciences of the United States of America,2010,107(52):22436-22441.
[23]TAN R,GU J Z,CHEN P,et al.Link Prediction Using Protected Location History[C]∥Fifth International Conference on Computational and Information Sciences.IEEE,2013.
[24]RODRIGUEZ A,LAIO A.Machine learning.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492.
[25]LIM K,CHAN J,LECKIE C,et al.Detecting Location-centric Communities using Social-Spatial Links with Temporal Constraints[C]∥European Conference on Information Retrieval.2015.
[26]CHLO B,VINCENZO N,SALVATORE S,et al.Social and place-focused communities in location-based online social networks[J].European Physical Journal B,2013,86(6):1-10.
[27]VISHNU J,KINSHUK B,ANSHU K,et al.Discovering Local Social Groups using Mobility Data[J].International Journal of Computer Applications,2015,120:15-19.
[28]MA C L,SHAN H,MA T,et al.An Improved Random Forests Algorithm with Application to Social Ties Inferring of LBS Users[J].Journal of Chinese Computer Systems,2016,37(12):2708-2712.(in Chinese)
马春来,单洪,马涛,等.随机森林改进算法在LBS用户社会关系推断中的应用[J].小型微型计算机系统,2016,37(12):2708-2712.
[29]CHEN J,WAN Y.Research on label propagation algorithm
based on modularity maximization in the social network[J].Journal on Communications,2017,38(2):25-33.(in Chinese)
陈晶,万云.社交网络中基于模块度最大化的标签传播算法的研究[J].通信学报,2017,38(2):25-33.
[30]WANG Z,ZHANG D Q,ZHOU X S,et al.Discovering and Profiling Overlapping Communities in Location-Based Social Networks[J].IEEE Transactions on Systems Man & Cybernetics Systems,2014,44(4):499-509.
[31]GUO J,SONG P.Community Discovery with Location-Interaction Disparity in Mobile Social Networks[J].ZTE Communications,2015,13(2):53-61.
[32]MA C L,SHAN H,MA T,et al.Random Forests Based Method for Inferring Social Ties of LBS Users[J].Computer Science,2016,43(12):218-222.(in Chinese)
马春来,单洪,马涛,等.一种基于随机森林的LBS用户社会关系判断方法[J].计算机科学,2016,43(12):218-222.
[33]ZHENG Y,ZHANG H C,YU Y.Detecting collective anomalies from multiple spatio-temporal datasets across different domains[C]∥Sigspatial International Conference on Advances in Geographic Information Systems.Bellevue,WA,USA:ACM,2015.

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