Advertisement

Advanced Clustering Based Intrusion Detection (ACID) Algorithm

  • Samarjeet Borah
  • Debaditya Chakravorty
  • Chandan Chawhan
  • Aritra Saha
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)

Abstract

Computer security or network security has become one of the biggest issues now-a-days. Intrusion Detection process detects malicious attacks which generally includes theft of information or data. Traditional IDS (Intrusion Detection System) detects only those attacks which are known to them. But they rarely detect unknown intrusions. Clustering based method may be helpful in detecting unknown attack patterns. In this paper an attempt has been made to propose a new intrusion detection method based on clustering. The algorithm is experimented with KDD99 dataset and is found to produce satisfactory results.

Keywords

Security Network Intrusion Detection Clustering IDS Training Data Set Knowledge Base 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sabahi, F., Movaghar, A.: Intrusion Detection: A Survey. In: The Proceedings of 3rd International Conference on Systems and Networks Communications, ICSNC 2008, IEEE, Los Alamitos (2008)Google Scholar
  2. 2.
    Berkhin, P.: Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA (2002)Google Scholar
  3. 3.
    Terry Brugger, S.: Data Mining Methods for Network Intrusion Detection. University of California, Davis (2004)Google Scholar
  4. 4.
    Prerau, M.J., Eskin, E.: Unsupervised anomaly detection using an optimized K-nearest neighbors algorithm. Master”s thesis, http://www.music.columbia.edu/~mike/publications/thesis.pdf
  5. 5.
    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 (2003)Google Scholar
  6. 6.
    Bloedorn, E., Christiansen, A.D., Hill, W., Skorupka, C., Talbot, L.M., Tivel, J.: Data mining for network intrusion detection: How to get started (August 2001), http://citeseer.nj.nec.com/523955.html
  7. 7.
    Lee, W., Stolfo, S.J.: Data Mining Approaches for Intrusion Detection. In: Proceedings of the 1998 USENIX Security Symposium (1998)Google Scholar
  8. 8.
    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
  9. 9.
    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 MultiConference of Engineers and Computer Scientists 2009, IMECS 2009, Hong Kong, March 18-20, vol. I (2009)Google Scholar
  10. 10.
    Zhong, S., Khoshgoftaar, T.M., Seliya, N.: Clustering-based network intrusion detection (2007)Google Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification (2001)Google Scholar
  12. 12.
    Zanero, S., Savaresi, S.M.: Unsupervised learning techniques for a intrusion detection system (2004)Google Scholar
  13. 13.
    Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A Comparative study of anomaly detection schemes in network intrusion detection (2003)Google Scholar
  14. 14.
    Zhang, R., et al.: BIRCH: An efficient data clustering method for very large databases (1996)Google Scholar
  15. 15.
    Lee, S.: Data mining approaches for intrusion detection (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Samarjeet Borah
    • 1
  • Debaditya Chakravorty
    • 1
  • Chandan Chawhan
    • 1
  • Aritra Saha
    • 1
  1. 1.Department of Computer Science & EngineeringSikkim Manipal Institute of TechnologyIndia

Personalised recommendations