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Analytical Study of Different Classification Technique for KDD Cup Data'99

Riti Lath, Manish Shrivastava Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
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  1. Riti Lath and Manish Shrivastava. Article: Analytical Study of Different Classification Technique for KDD Cup Data’99. International Journal of Applied Information Systems 3(6):5-9, July 2012. BibTeX

    	author = "Riti Lath and Manish Shrivastava",
    	title = "Article: Analytical Study of Different Classification Technique for KDD Cup Data’99",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 6,
    	pages = "5-9",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"


This paper is a concise analysis of classification of 10% of kdd cup'99 datasets based on intrusion detection. Analysis of data is performed using different techniques i. e. k-mean which is based on clustering, and k-nearest neighbor, support vector machine are classification techniques. Firstly the flat results are analyzed then preprocessed data is used. For preprocessing statistical normalization has been used. For analysis only two groups are considered that are normal and abnormal, no further division of abnormal category has been done. Matlab is used as a tool. As a result classification technique proves good in classifying data, abnormal data separately and normal and abnormal data collectively, for classification potentiality.


  1. Wei Wang, Xiangliang Zhang, Sylvain Gombault, and Svein J. Knapskog, Attribute Normalization in Network Intrusion Detection, IEEE, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks. 978-0-7695-3908-9/09, pp-448-453.
  2. Tapas Kanungo, David M. Mount Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. , An Efficient k-Means Clustering Algorithm: Analysis and Implementation, IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 7, July 2002, pp-881-892.
  3. Peter Scherer, Martin Vicher, Pavla Drazdilova, Jan Martinovic, JirDvorsky,and Vaclav Snasel, "Using SVM and Clustering Algorithms in IDS Systems", V. Snasel, J. Pokorny, K. Richta (Eds. ), Dateso 2011, pp. 108-119, ISBN 978-80-248-2391-1.
  4. P´adraig Cunningham ,University College Dublin and Sarah Jane Delany,k-Nearest Neighbors Classifiers Dublin Institute of Technology Technical Report UCD-CSI-2007-4 March 27, 2007
  5. Wencang Zhao, Guangrong Ji, Rui Nian, and Chen Feng, SVM Classification Method Based Marginal Points of Representative Sample Sets, International Journal of Information Technology Vol. 11 No. 9, 2005
  6. Steve R Gunn, Support Vector Machines For Classification and Regression, technical report, university of Southampton, may, 1998.
  7. . JoaquĂ­n PĂ©rez Ortega, Ma. Del RocĂ­o Boone Rojas, MarĂ­a J. Somodevilla GarcĂ­a, "Research issues on K-means Algorithm: An Experimental Trial Using Matlab", jperez@cenidet. edu. mx,{rboone,mariasg}@cs. buap. mx
  8. Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set". proceedings of the 2009 IEEE symposium on computional intelligence in security and defence applications.
  9. Adetunmbi A. Olusola. , Adeola S. Oladele. and Daramola O. Abosede, "Analysis of KDD '99 Intrusion Detection Dataset for Selection of Relevance Features", Proceedings of the World Congress on Engineering and Computer Science 2010 Vol I WCECS 2010, October 20-22, 2010, San Francisco, USA
  10. XindongWu ,Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh ,Qiang Yang, Hiroshi Motoda ,Geoffrey J. McLachlan ,Angus Ng, Bing Liu ,Philip S. Yu, Zhi-Hua Zhou,Michael Steinbach,David J. Hand, Dan Steinberg, Top 10 algorithms in data mining, SURVEY PAPER, Knowl Inf Syst (2008) 14:1–37 DOI 10. 1007/s10115-007-0114-2, © Springer-Verlag London Limited 2007.


classification technique, clustering, normalization, SVM