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Semantic Cluster based Classification for Data Leakage Detection for the Cloud Security
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 110 - Number 6 |
| Year of Publication: 2015 |
| Authors: C. Suresh Kumar, K. Iyakutty |
10.5120/19319-0874
|
C. Suresh Kumar, K. Iyakutty . Semantic Cluster based Classification for Data Leakage Detection for the Cloud Security. International Journal of Computer Applications. 110, 6 ( January 2015), 19-22. DOI=10.5120/19319-0874
Abstract
A novel approach for the data leak detection in the cloud environment is discussed in this paper. The paper uses the semantic based clustering for the anomaly detection to find the data leak. The Clustering is further used for the classification to add up for the semi supervised classification. After classification the threat patterns are stored in the database for further preventive actions in the data transmission. The necessary theory is discussed and the proposed approach is discussed with the results obtained.
References
- http://www. istf. jucc. edu. hk/newsletter/IT_03/IT-3_Cloud_Computing. pdf
- http://www. buyya. com/papers/AnekaMagazineArticle1. pdf
- https://cloudsecurityalliance. org/topthreats/csathreat s. v1. 0. pdf
- http://www. isaca. org/Groups/Professional-English/security-trend/GroupDocuments/DLP-WP-14Sept2010-Research. pdf
- http://www. istf. jucc. edu. hk/newsletter/General_01/Gen-2_Data_leakage. pdf
- Philip K. Chan, Matthew V. Mahoney, Muhammad H. Arshad," Learning Rules and Clusters for Anomaly Detection in Network Traffic", Managing Cyber Threats Massive Computing Volume 5, 2005, pp 81-99
- S. Forrest, S. Hofmeyr, and A. Somayaji. Computer immunology. Comm. ACM, 4(10):88-96, 1997.
- Varun Chandola, Arindam Banerjee, and Vipin Kumar ,Outlier Detection – A Survey, Technical Report TR07-17, University of Minnesota
- Xuan-Hui Wang; Zheng Chen; Hongjun Lu; Wei-Ying Ma, "CBC: Clustering Based Text Classification Requiring Minimal Labeled Data", Proceedings of the Third IEEE International Conference on Data Mining, ICDM '03.
- Kyriakopoulou, A. , Kalamboukis, T. : Using clustering to enhance text classification. In: 30th annual international ACM SIGIR conference on Research and development in information retrieval (2007)
- Raskutti, B. , Ferr, H. , Kowalczyk, A. : Using unlabeled data for text classification through addition of cluster parameters. In: 9th International Conference on Machine Learning (2002)
- Zeng, H. J. , Wang, X. H. , Chen, Z. , Lu, H. , Ma, W. Y. : CBC: Clustering based text classification requiring minimal labeled data. In: Third IEEE International Conference on Data Mining (2003)
- Hassan H. Malik,John R. Kender, "Classification by Pattern-Based Hierarchical Clustering", ECML/PKDD-08 Workshop 15 September 2008, Antwerp, Belgium
- Yoshida, T. ; Xijin Tang, "Text Classification Using Semi-supervised Clustering", International Conference on Business Intelligence and Financial Engineering, 2009.
- R. Bekkerman, R. El-Yaniv, and Y. Winter,"Distributional word clusters vs. words for text categorization. Journal of Machine Learning Research",3:1183-1208, 2003.
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