Efficient Algorithms for Intrusion Detection

  • Niranjan K. Boora
  • Chiranjib Bhattacharyya
  • K. Gopinath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3347)


Detecting user to root attacks is an important intrusion detection task. This paper uses a mix of spectrum kernels and probabilistic suffix trees as a possible solution for detecting such intrusions efficiently. Experimental results on two real world datasets show that the proposed approach outperforms the state of the art Fisher kernel based methods in terms of speed with no loss of accuracy.


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  1. 1.
    Anderson, J.P.: Computer security threat monitoring and surveillance. tech. rep., James P Anderson Co., Fort Washington, Pennsylvania (April 1980)Google Scholar
  2. 2.
    Kendall, K.: A database of computer attacks for the evalutation of intrusion detection. Master’s thesis, MIT (June 1999)Google Scholar
  3. 3.
    Axelsson, S.: Intrusion detection systems: A survey and taxonomy. tech. rep., Department of Computer Engineering, Chalmers University of Technology (2000)Google Scholar
  4. 4.
    Sundaram, A.: An introduction to intrusion detection. ACM Crossroads Student Magazine (1996)Google Scholar
  5. 5.
    Baras, J., Rabi, M.: Intrusion detection with support vector machines and generative models. tech. rep., Institute for Systems Research, University of Maryland (2002)Google Scholar
  6. 6.
    Jaakkola, T., Haussler, D.: Using the Fisher kernel methods to detect remote protein homologies. In: Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, pp. 149–158 (1999)Google Scholar
  7. 7.
    Leslie, C., Eskin, E., Stafford, W.: The spectrum kernel: A string kernel for SVM protein classification. In: Proceedings of the Pacific Symposium on Biocomputing, January 2002, pp. 564–575 (2002)Google Scholar
  8. 8.
    Ron, D., Singer, Y., Tishby, N.: The power of amnesia: learning probabilistic automata with variable memory length. Machine Learning 25(2-3), 117–149 (1996)zbMATHCrossRefGoogle Scholar
  9. 9.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  10. 10.
    Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. IEEE ASSP Magazine, 4–15 (January 1986)Google Scholar
  11. 11.
    Duggad, R., Desai, U.B.: A tutorial on hidden Markov models. tech. rep., Electrical Department, Indian Institute of Technology, Bombay (1996)Google Scholar
  12. 12.
    Burges, C.J.C.: A tutorial on support vector machine for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  13. 13.
    Pavlidis, P., Furey, T.S., Liberto, M., Haussler, D., Grundy, W.N.: Promoter region-based classification of genes. In: Proceedings of the Pacific Symposium on Biocomputing, January 2001, pp. 151–163 (2001)Google Scholar
  14. 14.
    Bejerano, G., Yona, G.: Variations on probabilistic suffix trees: statistical modeling and prediction of protein families. Bioinformatics 17(1), 23–43 (2001)CrossRefGoogle Scholar
  15. 15.
    UNM, Department of Computer Science, Computer immune systems homepage,
  16. 16.
    Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning (1999)Google Scholar
  17. 17.
    M. P. I. f. M. G. Algorithmics group, General hidden Markov model library (ghmm),

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Niranjan K. Boora
    • 1
  • Chiranjib Bhattacharyya
    • 2
  • K. Gopinath
    • 2
  1. 1.Dept. of Electrical Engineering 
  2. 2.Dept. of Computer Science & AutomationIndian Institute of ScienceBangaloreIndia

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