Abstract
Outlier detection is a popular technique that can be utilized for finding Intruders. Security is becoming a critical part of organizational information systems. Network Intrusion Detection System ( NIDS) is an important detection system that is used as a counter measure to preserve data integrity and system availability from attacks [2]. However, current researches find that it is extremely difficult to find out outliers directly from high dimensional datasets. In our work we used entropy method for reducing high dimensionality to lower dimensionality, where the processing time can be saved without compromising the efficiency. Here we proposed a framework for finding outliers from high dimensional dataset and also presented the results. We implemented our proposed method on standard dataset kddcup’99 and the results shown with the high accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hwang, T.S., Lee, T.-J., Lee, Y.-J.: A Three-tier IDS via Data Mining Approach. In: MineNet 2007, San Diego, California, USA, June 12 (2007)
Srinivasulu, P., Nagaraju, D., Ramesh Kumar, P., Nageswara Rao, K.: Classifying the Network Intrusion Attacks using Data Mining Classification Methods and their Performance Comparison. IJCSNS International Journal of Computer Science and Network Security 9(6) (June 2009)
McHugh, J.: Intrusion and Intrusion Detection. Technical Report CERT Coordination Center, Software Engineering Institute, Carnegie Mellon University (2001)
Bezroukov, N.: Intrusion Detection (general issues). Open Source Software Educational Society, Softpanorama (July 19, 2003), http://www.softpanorama.org/Security/intrusion_detection.shtml (October 30, 2003)
Stolfo, S.J., Fan, W., Lee, W., Prodromidis, A., Chan, P.K.: Costbased modeling for fraud and intrusion detection: Results from the jam project. Discex 2, 1130 (2000)
Lippmann, R.P., Fried, D.J., Graf, I., Haines, J.W., Kendall, K.R., McClung, D., Weber, D., Webster, S.E., Wyschogrod, D., Cunningham, R.K., Zissman, M.A.: Evaluating intrusion detection systems: The 1998 darpa off-line intrusion detection evaluation. Discex 2, 1012 (2000)
KDD Cup 1999. (Ocotber 2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Bronnimann, H., Chen, B., Dash, M., Haas, P., Qiao, Y., Scheuermann, P.: Efficient Data-Reduction Methods for On-Line Association Rule Discovery in thesis
Lee, C., Lee, G.G.: Information gain and divergence-based feature selection for machine learning-based text categorization. In: Information Processing & Management, vol. 42(1), pp. 155–165 (January 2006)
Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 223–228. AAAI Press, San Jose (1992)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers, vol. 29(2-3), pp. 131–163. ACM, New York (November/December 1997)
Susanne, G., Dethlefsen, B.C.: Learning Bayesian Networks with R. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, Austria, March 20–22 (2003)
Song, Y., Huang, J., Zhou, D., Zha, H., Lee Giles, C.: IKNN: Informative K-Nearest Neighbor Pattern Classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 248–264. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Devarakonda, N., Pamidi, S., Valli Kumari, V., Govardhan, A. (2011). Outliers Detection as Network Intrusion Detection System Using Multi Layered Framework. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. CCSIT 2011. Communications in Computer and Information Science, vol 131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17857-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-17857-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17856-6
Online ISBN: 978-3-642-17857-3
eBook Packages: Computer ScienceComputer Science (R0)