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Towards the Development of OMNIVORE: An Evolving Intelligent Intrusion Detection System

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Applications and Innovations in Intelligent Systems XV (SGAI 2007)

Abstract

The vast majority of existing Intrusion Detection Systems (IDS) incorporates static knowledge bases, which contain information corresponding to specific attack patterns. Although such knowledge bases can gradually expand, to be able to detect new attacks, this requires the maintenance of an expert. This paper describes a potential application of computationally evolving intelligent behaviour in conjunction with network intrusion detection. Our aim is to develop a standalone Network Intrusion Detection System (NIDS), capable of working in offline and online mode by evolving its structure and parameters in order to prevent both known and novel intrusions.

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References

  1. Angelov, P.P., Evolving Rule-Based Models:A Tool for Design of Flexible Adaptive Systems, Physica-Verlag, Heidelberg, New York, 2002.

    Google Scholar 

  2. H. Debar, et al., "Towards a Taxonomy of Intrusion Detection Systems", Technical Report RZ 3030, IBM Research Division, Zurich Research Laboratory, 1998.

    Google Scholar 

  3. J. Dickerson, et al., “Fuzzy intrusion detection,” Proceedings of the NAFIPS, Vancouver, British Columbia, 2001, Vol. 3, pp. 1506-1510.

    Google Scholar 

  4. W. Lee, S.J. Stolfo, “A framework for constructing features and models for intrusion detection systems”, ACM Transactions on Information and System Security, ACM, 2000, pp. 227-261.

    Google Scholar 

  5. A.H. Sung, et al., “The Feature Selection and Intrusion Detection Problems”, 9th Asian Computing Science Conference, ASIAN’04, Springer Verlag, Germany, Lecture Notes in Computer Science, 2004, Vol. 3321, pp. 468-482.

    Article  Google Scholar 

  6. S.L. Chiu, “Fuzzy Model Identification based on Cluster Estimation”, Journal of Intelligent and Fuzzy Systems, 1994, pp. 267-278.

    Google Scholar 

  7. R.R. Yager, D. Filev, “Learning of fuzzy rules by mountain clustering”, Proceedings of SPIE Conference on Application of Fuzzy Logic Technology, 1993, pp. 246-254.

    Google Scholar 

  8. M. Yang, K. Wu, “A modified mountain clustering algorithm”, Journal of Pattern Analysis and Applications, Springer, 2005, pp. 125-138.

    Google Scholar 

  9. Intrusion Detection Evaluation, MIT Lincoln Lab, <http://www.ll.mit.edu/IST/ideval/index.html>.

    Google Scholar 

  10. KDD Cup ‘99, Cup datasets, <http://kdd.ics.uci.edu//databases/kddcup99/kddcup99.html≫

    Google Scholar 

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© 2008 Springer-Verlag London Limited

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Lekkas, S., Mikhailov, D.L. (2008). Towards the Development of OMNIVORE: An Evolving Intelligent Intrusion Detection System. In: Ellis, R., Allen, T., Petridis, M. (eds) Applications and Innovations in Intelligent Systems XV. SGAI 2007. Springer, London. https://doi.org/10.1007/978-1-84800-086-5_22

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  • DOI: https://doi.org/10.1007/978-1-84800-086-5_22

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-085-8

  • Online ISBN: 978-1-84800-086-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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