Algorithm for Clustering with Intrusion Detection Using Modified and Hashed K – Means Algorithms

  • M. Varaprasad Rao
  • A. Damodaram
  • N. Ch. Bhatra Charyulu
Part of the Advances in Intelligent Systems and Computing book series (volume 167)


The k-Means clustering algorithm partition a dataset into meaningful patterns. Intrusion Detection System detects malicious attacks which generally include theft information. It can be found from the studies that clustering based intrusion detection methods may be helpful in detecting unknown attack patterns compared to traditional intrusion detection systems. This paper presents modified k-Means by applying preprocessing and normalization steps. As a result the effectiveness is improved and it overcomes the shortcomings of k-Means. This approach is proposed to work on network intrusion data and the algorithm is experimented with KDD99 dataset and found satisfactory results.


Intrusion Detection System K-Means clustering Algorithm AIM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An introduction to Cluster analysis. John Wiley, New York (1990) ISBN 0-471-85233-3CrossRefGoogle Scholar
  2. 2.
    Velmurugan, T., Santhanam, T.: Computational Complexity between K-Means and KMedoids Clustering Algorithms for Normal and Uniform Distributions of Data Points. Journal of Computer Science 6(3), 363–368 (2010)CrossRefGoogle Scholar
  3. 3.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers. An Imprint of Elsevier (2006) ISBN 81-312-0535-5Google Scholar
  4. 4.
    Dunham, M.H.: Data Mining- Introductory and Advanced Concepts. In: Pearson Education 2006. Proceedings of the World Congress on Engineering, vol. 1 (2009)Google Scholar
  5. 5.
    McQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. Univ. of California Press, Berkeley (1967)Google Scholar
  6. 6.
    Merz, C., Murphy, P.: UCI Repository of Machine Learning Databases,
  7. 7.
    Tan, P.-N., Steinback, M., Kumar, V.: Introduction to Data Mining. Pearson Education (2007)Google Scholar
  8. 8.
    Patel, V.R., Mehta, R.G.: Clustering Algorithms: A Comprehensive Survey. In: International Conference on Electronics, Information and Communication Systems Engineering, Jodhpur (2011)Google Scholar
  9. 9.
    Oyelade, O.J., Oladipupo, O.O., Obagbuwa, I.C.: Application of kMeans Clustering algorithm for prediction of Students’ Academic Performance. International Journal of Computer Science and Information Security 7 (2010)Google Scholar
  10. 10.
    Sumitra Devi, K.A., Vijayalakshmi, M.N, Vasantha, R., Abraham, A.: Accomplishment of Circuit Partitioning using VHDL and Clustering Pertaining to VLSI designGoogle Scholar
  11. 11.
    Tilton, J.C., Marchisio, G., Koperski, K.: NASA’s Intelligent Systems Program, NASA Headquarter Code RGoogle Scholar
  12. 12.
    Ng, R.T., Han, J.: CLARANS:A Method for Clustering Objects for Spatial Data Mining. IEEE Transaction on Knowledge and Data Engineering 14(5), 1003–1016 (2002)CrossRefGoogle Scholar
  13. 13.
    Seidman, C.: Data Mining with Microsoft SQL Server 2000, Technical Reference, ISBN:0-7356-1271-4,
  14. 14.
    Noh, S.-K., Kim, Y.-M., Kim, D.K., Noh, B.-N.: Network Anomaly Detection Based on Clustering of Sequence Patterns. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3981, pp. 349–358. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Sahay, S.: Study and Implementation of CHEMELEON algorithm for gene clusteringGoogle Scholar
  16. 16.
    Erman, J., Arlitt, M., Mahanti, A.: Traffic Classification Using Clustering Algorithms. In: SIGCOMM 2006 Workshops Pisa, Italy, September 11-15 (2006)Google Scholar
  17. 17.
    Santhisree, K., Damodaram, A.: OPTICS on Sequential Data: Experiments and Test Results. International Journal of Computer Applications 5, 1–4 (2010)CrossRefGoogle Scholar
  18. 18.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. Department of Computer Science, University of Wisconsin, Madison, WI 53706Google Scholar
  19. 19.
    Maheshwari, P., Srivastava, N.: WaveCluster for Remote Sensing Image Retrieval. International Journal on Computer Science and Engineering 3(2) (2011)Google Scholar
  20. 20.
    Scanlan, J., Hartnett, J., Williams, R.: DynamicWEB: Profile Correlation Using COBWEB. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1059–1063. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Patel, V.R., Mehta, R.G.: Modified k-Means Clustering Algorithm. In: Das, V.V., Thankachan, N. (eds.) CIIT 2011. CCIS, vol. 250, pp. 209–213. Springer, Heidelberg (2011)Google Scholar
  22. 22.
    Borah, S., Chetry, S.P.K., Singh, P.K.: Hashed-K-Means: A Proposed Intrusion Detection Algorithm. In: Das, V.V. (ed.) CIIT 2011. CCIS, vol. 250, pp. 855–860. Springer, Heidelberg (2011)Google Scholar
  23. 23.
    Sabahi, F., Movaghar, A.: Intrusion Detection: A Survey. In: The Proceedings of 3rd International Conference on Systems and Networks Communications, ICSNC 2008, vol. 1. IEEE (2008) ISBN: 978-0-7695-3371-1Google Scholar
  24. 24.
    Borah, S., Ghose, M.K.: Automatic Initialization of Means (AIM): A Proposed Extension to the K-means Algorithm. International Journal of Information Technology & Knowledge Management 3(2), 247–250 (2010) ISSN: 0973-4414Google Scholar
  25. 25.
    Guan, Y., Ghorbani, A., Belacel, N.: Y-means: A Clustering Method for Intrusion Detection. In: Proceedings of Canadian Conference on Electrical and Computer Engineering, Montreal, Quebec, Canada, May 4-7, pp. 1083–1086 (2003)Google Scholar
  26. 26.
    Portnoy, L., Eskin, E., Stolfo, S.: Intrusion Detection with Unlabeled Data Using Clustering. In: Proceedings of the ACM CSS Workshop on Data Mining Applied to Security (DMSA 2001), Philadelphia, PA, November 5-8 (2001)Google Scholar
  27. 27.
    Yan, K.Q., Wang, S.C., Liu, C.W.: A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks. In: Proceedings of the International Multi-Conference of Engineers and Computer Scientists 2009, IMECS 2009, Hong Kong, March 18-20, vol. I (2009)Google Scholar
  28. 28.

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • M. Varaprasad Rao
    • 1
  • A. Damodaram
    • 2
  • N. Ch. Bhatra Charyulu
    • 3
  1. 1.Department of Computer ScienceMIPGSHyderabadIndia
  2. 2.Department of Computer ScienceJNTUHyderabadIndia
  3. 3.Department of StatisticsOsmania UniversityHyderabadIndia

Personalised recommendations