3.7 Conclusions
This chapter illustrated the importance of GP techniques for evolving intrusion detection systems. MEP outperforms LGP for three of the considered classes and LGP outperform MEP for two of the classes. MEP classification accuracy is grater than 95% for all considered classes and for three of them is greater than 99.75%. It is to be noted that for real time intrusion detection systems MEP and LGP would be the ideal candidates because of its simplified implementation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abraham, A., Evolutionary Computation in Intelligent Web Management, Evolutionary Computing in Data Mining, Ghosh A. and Jain L.C. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, Chapter 8, pp. 189–210, 2004.
J. P. Anderson. Computer Security Threat Monitoring and Surveillance. Technical report, James P Anderson Co., Fort Washington, Pennsylvania, April 1980.
Barbara D., Couto J., Jajodia S. and Wu N., ADAM: A Testbed for Exploring the Use of Data Mining in Intrusion Detection. SIGMOD Record, 30(4), pp. 15–24, 2001.
Brameier M. and Banzhaf W, Explicit control of diversity and effective variation distance in Linear Genetic Programming. In Proceedings of the fourth European Conference on Genetic Programming, Springer-Verlag Berlin, 2001.
Brieman L., Friedman J., Olshen R., and Stone C., Classification of Regression Trees. Wadsworth Inc., 1984.
Cohen W., Learning Trees and Rules with Set-Valued Features, American Association for Artificial Intelligence (AAAI), 1996.
Denning D., An Intrusion-Detection Model, IEEE Transactions on Software Engineering, Vol. SE-13, No. 2, pp. 222–232, 1987.
T. D. Garvey and T. F. Lunt. Model based intrusion detection, In Proceedings of the 14th National Computer Security Conference, pages 372–385, October 1991.
Grosan C., Abraham A. and Han S.Y., MEPIDS: Multi-Expression Programming for Intrusion Detection System, International Work-conference on the Interplay between Natural and Artificial Computation, (IWINAC’05), Spain, Lecture Notes in Computer Science, Springer Verlag, Germany, pp. 163–172, 2005.
R. Heady, G. Luger, A. Maccabe, and M. Servilla, The Architecture of a Network level Intrusion Detection System. Technical report, Department of Computer Science, University of New Mexico, August 1990.
K. Ilgun. USTAT: A Real-Time Intrusion Detection System for UNIX, Master Thesis, University of California, Santa Barbara, November 1992.
T. Joachims. Making Large-Scale SVM Learning Practical. LS8-Report, University of Dortmund, LS VIII-Report, 1998.
KDD Cup 1999 Intrusion detection data set: http://kdd.ics.uci.edu/databases/kddcup99/kddcup.data_10_percent.gz
S. Kumar and E. H. Spafford. An Application of Pattern Matching in Intrusion Detection. Technical Report CSD-TR-94-013, Purdue University, 1994.
S. Kumar. Classification and Detection of Computer Intrusions, PhD Thesis, Department of Computer Science, Purdue University, August 1995.
Lee W. and Stolfo S. and Mok K., A Data Mining Framework for Building Intrusion Detection Models. In Proceedings of the IEEE Symposium on Security and Privacy, 1999.
T.F. Lunt, A. Tamaru, F. Gilham et al, A Real Time Intrusion Detection Expert System (IDES), Final Technical Report, Project 6784, SRI International 1990
T. Lunt. Detecting intruders in computer systems. In Proceedings of the 1993 Conference on Auditing and Computer Technology, 1993.
MIT Lincoln Laboratory. http://www.ll.mit.edu/IST/ideval/
Oltean M. and Grosan C., A Comparison of Several Linear GP Techniques, Complex Systems, Vol. 14, No. 4, pp. 285–313, 2004.
Oltean M. and Grosan C., Evolving Evolutionary Algorithms using Multi Expression Programming. Proceedings of The 7 th European Conference on Artificial Life, Dortmund, Germany, pp. 651–658, 2003.
Peddabachigari S., Abraham A., Thomas J., Intrusion Detection Systems Using Decision Trees and Support Vector Machines, International Journal of Applied Science and Computations, Vol.11, No.3, pp.118–134, 2004.
P. A. Porras. STAT: A State Transition Analysis Tool for Intrusion Detection. Master’s Thesis, Computer Science Dept., University of California, Santa Barbara, 1992.
Provost, F. and T. Fawcett. Robust Classification for Imprecise Environments, Machine Learning 42, 203–231, 2001.
J. R. Quinlan. Induction of Decision Trees. Machine Learning, 1:81–106, 1986.
J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
C. Ryan C. J.J. Collins and M. O’Neill. Gramatical Evolution: Evolving programs for an arbitrary language, In Proceedings of the first European Workshop on Genetic Programming, Springer-Verlag, Berlin, 1998.
Summers R.C., Secure Computing: Threats and Safeguards. McGraw Hill, New York, 1997.
A. Sundaram. An Introduction to Intrusion Detection. ACM Cross Roads, Vol. 2, No. 4, April 1996.
H. S. Teng, K. Chen and S. C. Lu. Security Audit Trail Analysis Using Inductively Generated Predictive Rules. In Proceedings of the 11th National Conference on Artificial Intelligence Applications, pages 24–29, IEEE, IEEE Service Center, Piscataway, NJ, March 1990.
Vapnik V.N., The Nature of Statistical Learning Theory. Springer, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Abraham, A., Grosan, C. (2006). Evolving Intrusion Detection Systems. In: Nedjah, N., Mourelle, L.d.M., Abraham, A. (eds) Genetic Systems Programming. Studies in Computational Intelligence, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32498-4_3
Download citation
DOI: https://doi.org/10.1007/3-540-32498-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29849-6
Online ISBN: 978-3-540-32498-0
eBook Packages: EngineeringEngineering (R0)