World Scientific
  • Search
    Quick Search in Journals
    Access type:
    Quick Search anywhere
    Access type:
    Advanced Search
  • My Cart
Skip main navigation
Close Drawer MenuOpen Drawer Menu

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
No Access

GENETIC PROGRAMMING APPROACH FOR MULTI-CATEGORY PATTERN CLASSIFICATION APPLIED TO NETWORK INTRUSIONS DETECTION

    https://doi.org/10.1142/S1469026806001812Cited by:20
    Previous
    Next

    Abstract

    The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.

    References

    • D. Song, M. I. Heywood and A. N. Zincir-Heywood, IEEE Trans. Evol. Comput. 9(3), 225 (2005), DOI: 10.1109/TEVC.2004.841683. Crossref, Google Scholar
    • D. Song, M. I. Heywood and A. N. Zincir-Heywood, A linear genetic programming approach to intrusion detection, GECCO 2003, Lecture Notes in Computer Science 2724, eds. E. Cantú-Pazet al. (Springer-Verlag, Berlin, Heidelberg, 2003) pp. 2325–2336. Google Scholar
    • J. K. Kishoreet al., IEEE Trans. Evol. Comput. 4, 242 (2000), DOI: 10.1109/4235.873235. Crossref, Google Scholar
    • B. Pfahringer, SIGKDD Explorations, ACM SIGKDD 1(2), 65 (2000). Crossref, Google Scholar
    • I. Levin, SIGKDD Explorations, ACM SIGKDD 1(2), 67 (2000). Crossref, Google Scholar
    • M. Vladimir, V. Alexei and S. Ivan, SIGKDD Explorations, ACM SIGKDD 1(2), 76 (2000). Crossref, Google Scholar
    • E.   Eskin et al. , Applications of Data Mining in Computer Security , eds. D.   Barbara and S.   Jajodia ( Kluwer , 2002 ) . Google Scholar
    • G.   Kayacik , N.   Zincir-Heywood and M.   Heywood , On the capability of an SOM based intrusion detection system , Proc. Int. Joint Conf. Neural Networks ( 2003 ) , DOI: 10.1109/IJCNN.2003.1223682 . Google Scholar
    • J. R.   Koza , Genetic Programming II: Automatic Discovery of Reusable Programs ( MIT Press , 1994 ) . Google Scholar
    • M. Zhang and V. Ciesielski, Genetic programming for multiple class object detection, Proc. 12th Austral. Joint Conf. Artif. Intell.1747, Lecture Notes in Artificial Intelligence (Springer, Heidelberg, 1999) pp. 180–191. Google Scholar
    • M. Zhang and W. Smart, Multiclass object classification using genetic programming, Technical Report CS-TR-04/2, School of Mathematical and Computing Sciences,Victoria University (February 2004) . Google Scholar
    • T. Loveard and V. Ciesielski, Representing classification problems in genetic programming, Proc. Congr. Evol. Comput.2 (2001) pp. 1070–1077, http://goanna.cs.rmit.edu.au/toml/cec2001.ps DOI: 10.1109/CEC.2001.934310. Google Scholar
    • B.-C. Chien, J. Y. Lin and T.-P. Hong, Expert Syst. Appl. 23, 31 (2002), DOI: 10.1016/S0957-4174(02)00025-8. Crossref, Google Scholar
    • R. R. F. Mendeset al., Discovering fuzzy classification rules with genetic programming and co-evolution, Proc. 5th Eur. Conf. PKDD2168, Lecture Notes in Artificial Intelligence (2001) pp. 314–325. Google Scholar
    • D. P. Muni, N. R. Pal and J. Das, IEEE Transactions on Evol. Comput. 8(2), 183 (2004), DOI: 10.1109/TEVC.2004.825567. Crossref, Google Scholar
    • M.   Crosbie and G.   Spafford , Applying genetic programming techniques to intrusion detection , Proc. AAAI 1995 Fall Symp. ( 1995 ) . Google Scholar
    • B. Adolf, New Paradigms for intrusion detection using genetic programming, Technical Report (January 2004) . Google Scholar
    • M.   Cosbie and G.   Spafford , Applying genetic programming to intrusion detection , Proc. 18th NISSC Conf. ( 1998 ) . Google Scholar
    • R. A. Fisher, Ann. Eugenics 7, 179 (1936). Crossref, Google Scholar
    • KDD data set, 1999; http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, cited April 2003 . Google Scholar
    • C. Elkan, SIGKDD Explorations, ACM SIGKDD  (2000). Google Scholar
    • W. Lee and S. Stolfo, Inf. Syst. Security 3(4), 227 (2000), DOI: 10.1145/382912.382914. Crossref, Google Scholar
    • M. Sabhnani and G. Serpen, Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context, Proc. Int. Conf. Mach. Learn. Models, Technol. Appl. (MLMTA 2003) (2003) pp. 209–215. Google Scholar
    • A. Küçükyılmaz, Pattern Classification: A Survey and Comparison, Department of Computer Engineering, Bilkent University, 06800, Ankara, Turkey (April 2005) http://www.cs.bilkent.edu.tr/~guvenir/courses/cs550/Workshop/Ayse_Kucukyilmaz.pdf . Google Scholar
    • A. K. Jain, R. P. W. Duin and J. Mao, IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4 (2000), DOI: 10.1109/34.824819. Crossref, Google Scholar
    • R. Agarwal and M. V. Joshi, PNrule: A new framework for learning classifier models in data mining, Technical Report TR 00-015, Department of Computer Science, University of Minnesota (2000) . Google Scholar
    Remember to check out the Most Cited Articles!

    Check out these titles in artificial intelligence!