Open Access Open Access  Restricted Access Subscription or Fee Access

Efficient Clustering Based Anomaly Detection in Cooperative Information System through Histograms

Anoop ., A. Jayachandran


Unsupervised Learning-based anomaly detection has proven to be an effective black-box technique for detecting unknown attacks. However, the effectiveness of this technique crucially depends upon both the quality and the completeness of the training data. Anomaly detection mechanism are deployed through the given system in order to identify any misbehaving exist in the provided Training Sets. Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. In this paper, an iterative procedure of clustering method based on multivariate outlier detection using Dynamic histogram was proposed. The proposed Approach is a density-based clustering algorithm that is suitable for anomaly detection where the anomalous are identify based on their Access Rates using Ranking mechanisms. At each iteration, multivariate histogram mean used to check the discrimination between the anomalous clusters and the inliers. Ranks are calculated for the calculated clusters that can be used to detect the outliers. This paper employed this procedure for clustering 275 patient of a hospital with their personal information as a source for processing.


Learning, Anomalous, Clustering, Unsupervised Learning, Ranking

Full Text:



A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity- part I: System description,” IEEE Trans. on Comm., vol. 51, pp. 1927-1938, Nov. 2003.

D. N. Lam, A. Liu, and C. Martin, Graph-Based Data Warehousing Using the Core-Facets Model, 11th Industrial Conference on Data Mining, New York, NY, 2011.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Computing Surveys, vol. 41, 2009

J. Gao, F. Liang, W. Fan, C. Wang, Y. Sun, and J. Han, On Community Outliers and their Efficient Detection in Information Networks, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, D.C.: ACM, 2010, pp. 813-822. [5] M. Janani, A. Hedayat, T. E. Hunter, and A. Nosratinia, “Coded cooperation in wireless communications: space-time transmission and iterative decoding,” IEEE Trans. on Sig. Proc., pp. 362-371, Feb. 2006.

Scarfone, K., Mell, P.: Guide to intrusion detection and prevention systems (idps).Technical Report 800-94, NIST, US Dept. of Commerce (2007)..

H. J. Escalante and O. Fuentes. Kernel Methods for Anomaly Detection and Noise Elimination. InPro-ceedings of the International Conference on Computing (CORE 2006), pages 69–80, MexicoCity, Mexico, 2006., 2006.

F. Oggier and B. Hassibi, “A coding strategy for wireless networks with no channel information,” in Proc. of Allerton Conf., 2006.

Agrawal, R., Gehrke, J., Gunopulos, D. & Raghav an, P . (1998), Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of SIGMOD 1998, pp. 94{105.

J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Trans. on Inf. Theory, pp. 3062-3080, Dec. 2004.

Denning, D. (1987), `An intrusion detection model'. In IEEE Tr ansactions on Software Engineering

Eskin, E., Arnold, A., Prerau, M., Portnoy , L. & Stolfo, S. (2002), A geometric framework for un-supervised anomaly detection: Detecting intru-sions in unlabeled data. In Applications of Data Mining in Computer Security.

D. Gunduz and E. Erkip, “Opportunistic cooperation by dynamic re- source allocation,” IEEE Trans. on Wireless Comm., pp. 1446-1454, Apr. 2007.

Wang, W., Y ang, J. & Muntz, R. R. (1997), Sting: A statistical information grid approach to spatial data mining. In Proceedings of the 23rd Inter-national Conference on Very Large Data Bases, Morgan Kaufmann, pp. 186{195..

Zanero, S. & Sav aresi, S. (2004), Unsupervised learn-ing techniques for an intrusion detection system. In Proceedings of the ACM Symposium on Ap-plied Computing, ACM SAC 2004.

M. Mahoney and P. Chan. Detecting novel attacks by identifying anomalous network packet headers. Techni-cal Report CS-2001-2, Florida Institute of Technology, 2001


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.