Method for detecting abnormal behaviour of users based on selective clustering ensemble
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 prop...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-03-01
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| Series: | IET Networks |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-net.2017.0127 |
| _version_ | 1819242198654255104 |
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| author | Juan Du Ping Xie Junlong Zhu Ruijuan Zheng Qingtao Wu Mingchuan Zhang |
| author_facet | Juan Du Ping Xie Junlong Zhu Ruijuan Zheng Qingtao Wu Mingchuan Zhang |
| author_sort | Juan Du |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T14:36:00Z |
| format | Article |
| id | doaj.art-10bbf59fe7d04dfd8f5916e65651e238 |
| institution | Directory Open Access Journal |
| issn | 2047-4954 2047-4962 |
| language | English |
| last_indexed | 2024-12-23T14:36:00Z |
| publishDate | 2018-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Networks |
| spelling | doaj.art-10bbf59fe7d04dfd8f5916e65651e2382022-12-21T17:43:20ZengWileyIET Networks2047-49542047-49622018-03-0172859010.1049/iet-net.2017.0127Method for detecting abnormal behaviour of users based on selective clustering ensembleJuan Du0Ping Xie1Junlong Zhu2Ruijuan Zheng3Qingtao Wu4Mingchuan Zhang5College of Information Engineering, Henan University of Science and Technology, Henan ShengPeople's Republic of ChinaCollege of Information Engineering, Henan University of Science and Technology, Henan ShengPeople's Republic of ChinaCollege of Information Engineering, Henan University of Science and Technology, Henan ShengPeople's Republic of ChinaCollege of Information Engineering, Henan University of Science and Technology, Henan ShengPeople's Republic of ChinaCollege of Information Engineering, Henan University of Science and Technology, Henan ShengPeople's Republic of ChinaCollege of Information Engineering, Henan University of Science and Technology, Henan ShengPeople's Republic of ChinaWith 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.https://doi.org/10.1049/iet-net.2017.0127user abnormal behaviour detectioncloud computingmobile cloud servicesselective clustering ensemble algorithmfractal dimensionsliding window |
| spellingShingle | Juan Du Ping Xie Junlong Zhu Ruijuan Zheng Qingtao Wu Mingchuan Zhang Method for detecting abnormal behaviour of users based on selective clustering ensemble IET Networks user abnormal behaviour detection cloud computing mobile cloud services selective clustering ensemble algorithm fractal dimension sliding window |
| title | Method for detecting abnormal behaviour of users based on selective clustering ensemble |
| title_full | Method for detecting abnormal behaviour of users based on selective clustering ensemble |
| title_fullStr | Method for detecting abnormal behaviour of users based on selective clustering ensemble |
| title_full_unstemmed | Method for detecting abnormal behaviour of users based on selective clustering ensemble |
| title_short | Method for detecting abnormal behaviour of users based on selective clustering ensemble |
| title_sort | method for detecting abnormal behaviour of users based on selective clustering ensemble |
| topic | user abnormal behaviour detection cloud computing mobile cloud services selective clustering ensemble algorithm fractal dimension sliding window |
| url | https://doi.org/10.1049/iet-net.2017.0127 |
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