Balanced Iterative Reducing and Clustering Using Hierarchies with Principal Component Analysis (PBirch) for Intrusion Detection over Big Data in Mobile Cloud Environment
With the development of big data, mobile cloud computing, cyber security issues have become more and more critical. Thus, enabling an intrusion detection method over big data in mobile cloud environment is of paramount importance. In our previous research, we proposed an approach named Mini Batch Kmeans with Principal Component Analysis (PMBKM) for big data which can effectively solve the clustering problem for intrusion detection of big data, but it needs to preset the number of clusters. The best clustering number is selected by comparing the clustering results of different clustering values multiple times. To address the above issue, we propose a new clustering method named Balanced Iterative Reducing and Clustering Using Hierarchies with Principal Component Analysis (PBirch) in this paper. Compared to PMBKM, the experimental results show that PBirch can obtain a good clustering result without presetting clustering values, and the clustering result can be further improved by optimizing the relevant parameters. The clustering time of PBirch decreases linearly with the increasing of the cluster numbers. Thus, the larger the number of clusters, the smaller the PBirch time cost. All in all, our proposed method can be widely used for big data in mobile cloud environment.
KeywordsMobile cloud environment Big data Intrusion detection Birch PCA
This work is supported by The Natural Science Foundation of Fujian Province (Grant No. 2018J05106), Quanzhou Science and Technology Project (No. 2015Z115), the Scientific Research Foundation of Huaqiao University (No. 14BS316). The Education Scientific Research Project for Middle-age and Young Teachers of Fujian Province (JZ160084). China Scholarship Council awards to Kai Peng for one year’s research abroad at The University of British Columbia, Vancouver, Canada. The authors also wants to thank Jianping Liu, Zhiqiang Xu and etc. for sharing a lot of valuable information on his blog.
- 1.Anderson, J.P.: Computer security threat monitoring and surveillance. Technical Report, vol. 17. James P. Anderson Company, Pennsylvania (1980)Google Scholar
- 5.Peng, K., Lin, R.H., Huang, B.B., Zou, H., Yang, F.C.: Link importance evaluation of data center network based on maximum flow. J. Internet Technol. 18(1), 23–31 (2017)Google Scholar
- 10.Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: 7th USENIX. USENIX Security Symposium, pp. 79–93 (1998)Google Scholar
- 13.Halko, N., Martinsson, P.G., Tropp, J.A.: Finding Structure with Randomness: Stochastic Algorithms for Constructing Approximate Matrix Decompositions. http://resolver.caltech.edu/CaltechAUTHORS:20111012-111324407
- 16.Zhang, T., Ramakrishnan, R., Livny, M.: An efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD 1996), pp. 103–114. ACM, New York (1996)Google Scholar
- 20.Scikit-learn. http://scikit-learn.org/stable/index.html