Robust Data Clustering Algorithms for Network Intrusion Detection

International Journal of Computer & Organization Trends  (IJCOT)          
© 2012 by IJCOT Journal
Volume-2 Issue-5                          
Year of Publication : 2012
Authors :  Gunja Ambica , Mrs.N.Rajeswari 


Gunja Ambica , Mrs.N.Rajeswari "Robust Data Clustering Algorithms for Network Intrusion Detection" . International Journal of Computer & organization Trends (IJCOT), V2(5):6-11 Sep - Oct 2012, ISSN 2249-2593, Published by Seventh Sense Research Group.


IDS (Intrusion Detection system) is an active and driving defense technology. Intrusion detection is to detect attacks against a computer system. This project mainly focuses on intrusion detection based on data mining. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. One of the primary challenges to intrusion detection are the problem of misjudgment, misdetection and lack of real time response to the attack. In the recent years, as the second line of defense after firewall This project presents an approach to detect intrusion based on data mining frame work. In this framework, intrusion detection is achieved using clustering techniques. Firstly, a method to reduce the noise in the data set using improved kmeans. This system use K-means,FCM and Improved K-means data mining algorithms are used to improves the performance of intrusion detection since the traffic is large and the types of attack are various. By the more accurate method of finding k clustering center, an anomaly detection model was presented to get better detection effect. This project used KDD CUP 1999 data set to test the performance of the model. The results show the system has a higher detection rate and a lower false alarm rate, it achieves expectant aim.


[1] Data Clustering Using K-Mean Algorithm for Network Intrusion Detection, Satinder Pal Singh, Lovely Professional University, Jalandhar MAY-2010.
[2] C. Wang and J. C. Knight. Towards survivable intrusion detection. In Proceedings of the 3rd Information Survivability Workshop (ISW-2000), Boston, USA, October 2000.
[3] Tom Mitchell. Machine Learning. Mc Graw Hill, 1997.
[4] K. Aas and L. Eikvil, Text Categorisation: A Survey, aas99text.html, 1999.
[5] Carl Endorf, Gene Schultz, and Jim Mellander. Intrusion Detection and Prevention. McGraw- Hill Osborne Media, first edition, 2003.
[6] W. Lee and S. J. Stolfo. A framework for constructing features and models for intrusion detection systems. In Proceedings of ACM Transactions on Information and System Security (TISSEC), volume 3(4), pages 227–261.
[7] Wang, Q. and V. Megalooikonomou. " A clustering algorithm for intrusion detection. in SPIE Conference on Data Mining " , Intrusion Detection, Infonnation Assurance, and Data Networks Security. 2005. Orlando, Florida, USA.
[8] Jiawei Han Micheline Kamber, " Data Mining Concepts and Techniques " , Second Edition.
[9] Masakazu Seno, George Karypis, " Finding Frequent Patterns Using Length-Decreasing Support Constraints", Data Mining and Knowledge Discovery, pp.197-228, 2005.
[10] W. Lee, S. Stolfo, and K. Mok, Mining audit data to build intrusion detection models, Proc. 4th International Conf. on Knowledge Discovery and Data Mining (KDD ’98), New York City, NY, 1998, 66-72.
[11] H. Toivonen, Sampling large databases for association rules, Proc. 22nd international conf. on very large data bases (VLDB’96), Mumbai, India, 1996, 134-145.
[12] S. Lee and D. Cheung, Maintenance of discovered association rules: When to update?, Proc. 1997 ACMSIGMOD workshop on research issues on data mining and knowledge discovery (DMKD’97), Tucson, AZ, 1997