X hits on this document

PDF document

Modeling Intrusion Detection Systems Using Linear Genetic Programming Approach - page 10 / 10





10 / 10

We note, however, that the difference in accuracy figures tend to be very small and may not be statistically significant, especially in view of the fact that the 5 classes of patterns differ in their sizes tremendously. More definitive conclusions can only be made after analyzing more comprehensive sets of network traffic data.


  • 1.

    Denning D. (1987) “An Intrusion-Detection Model,” IEEE Transactions on Software Engineering, Vol. SE-13, No. 2, pp.222-232.

  • 2.

    Kumar S., Spafford E. H. (1994) “An Application of Pattern Matching in Intrusion Detection,” Technical Report CSD-TR-94-013. Purdue University.

  • 3.

    Cannady J. (1998) “Applying Neural Networks for Misuse Detection,” Proceedings of 21st National Information Systems Security Conference, pp.368-381.

  • 4.

    Ryan J., Lin M-J., Miikkulainen R. (1998) “Intrusion Detection with Neural Networks,” Advances in Neural Information Processing Systems, Vol. 10, Cambridge, MA: MIT Press.

  • 5.

    Mukkamala S., Janoski G., Sung A. H. (2002) “Intrusion Detection Using Neural Networks and Support Vector Machines,” Proceedings of IEEE International Joint Conference on Neural Networks, pp.1702-1707.

  • 6.

    Stolfo J., Wei F., Lee W., Prodromidis A., and Chan P. K. (1999) “Cost-based Modeling and Evaluation for Data Mining with Application to Fraud and Intrusion Detection,” Results from the JAM Project by Salvatore.

  • 7.

    Mukkamala S., Sung A. H. (2002) “Identifying Key Features for Intrusion Detection Using Neural Networks,” Proceedings of ICCC International Conference on Computer Communications, pp. 1132-1138.

  • 8.


  • 9.

    Banzhaf. W., Nordin. P., Keller. E. R., Francone F. D. (1998) “Genetic Programming : An Introduction on The Automatic Evolution of Computer Programs and its Applications,Morgan Kaufmann Publishers, Inc.

  • 10.

    AIMLearning Technology, http://www.aimlearning.com.

  • 11.

    Brameier. M., Banzhaf. W. (2001) “A comparison of linear genetic programming and neural networks in medical data mining, Evolutionary Computation,” IEEE Transactions on, Volume: 5(1), pp. 17-26.

  • 12.

    Riedmiller M., and Braun H. (1993) “A direct adaptive method for faster back propagation learning: The RPROP algorithm”, Proceedings of the IEEE International Conference on Neural Networks.

  • 13.

    Joachims T. (1998) “Making Large-Scale SVM Learning Practical,” LS8-Report, University of Dortmund, LS VIII-Report.

  • 14.

    Joachims T. (2000) “SVMlight is an Implementation of Support Vector Machines (SVMs) in C,” http://ais.gmd.de/~thorsten/svm_light. University of Dortmund. Collaborative Research Center on Complexity Reduction in Multivariate Data (SFB475).

  • 15.

    Vladimir V. N. (1995) “The Nature of Statistical Learning Theory,” Springer.

  • 16.

    Kendall K. (1998) “A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems”, Master's Thesis, Massachusetts Institute of Technology.

  • 17.

    Webster S. E. (1998) “The Development and Analysis of Intrusion Detection Algorithms,”

    • M.

      S. Thesis, Massachusetts Institute of Technology.

Document info
Document views38
Page views38
Page last viewedSat Jan 21 00:47:59 UTC 2017