Method for detecting abnormal behaviour of users based on selective clustering ensemble. Issue 2 (1st March 2018)

• Record Type: Journal Article
• Authors: Du, Juan; Xie, Ping; Zhu, Junlong; Zheng, Ruijuan; Wu, Qingtao; Zhang, Mingchuan
• Is Part Of: IET networks. Volume 7:Issue 2(2018)
• Page Start: 85
• Page End: 90
• Publication Date: 2018-03-01
• Subjects: pattern clustering -- behavioural sciences computing -- cloud computing -- mobile computing; user abnormal behaviour detection -- cloud computing -- mobile cloud services -- selective clustering ensemble algorithm -- fractal dimension -- sliding window -- user behaviour acquisition -- incremental clustering stage -- Duun_index -- voting algorithm -- detection rate improvement -- accuracy rate improvement; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2017.0127
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : With the development of cloud computing in the mobile communications industry, the credibility between users and the cloud has become an important obstacle to the development of mobile cloud services. Therefore, the user's abnormal behaviour detection is particularly important. The authors propose a selective clustering ensemble algorithm based on fractal dimension. In their proposed algorithm, they firstly use the sliding window to dynamically obtain data to improve the accuracy of user behaviour acquisition. Secondly, they use the initial clustering stage and incremental clustering stage to produce clustered members. Thirdly, they also use the Duun_index to select the clustered members, then the selection of high‐quality clustered members with the voting algorithm to get the final result. Finally, they use the average difference of change to determine whether the current behaviour is abnormal based on the clustering results. The experimental results show that the proposed scheme has a better performance than the traditional clustering algorithm in clustering accuracy. Moreover, their algorithm can improve the detection rate and accuracy rate, respectively.