Skip to main content
SpringerLink
Log in

A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection

  • Conference paper
Computational Science and Its Applications – ICCSA 2004 (ICCSA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3046))

Included in the following conference series:

Abstract

This paper presents preliminary works on an agent-based approach for distributed learning of decision trees. The distributed decision tree approach is applied to intrusion detection domain, the interest of which is recently increasing. In the approach, a network profile is built by applying a distributed data analysis method for the collection of data from distributed hosts. The method integrates inductive generalization and agent-based computing, so that classification rules are learned via tree induction from distributed data to be used as intrusion profiles. Agents, in a collaborative fashion, generate partial trees and communicate the temporary results among them in the form of indices to the data records. Experimental results are presented for military network domain data used for the network intrusion detection in KDD cup 1999. Several experimental results show that the performance of distributed version of decision tree is much better than that of non-distributed version with data collected manually from distributed hosts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zaidi, S.Z.H., Abidi, S.S.R., Manickam, S.: Distributed data mining from heterogeneous healthcare data repositories: towards an intelligent agent-based framework. In: Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS), pp. 339–342 (2002)

    Google Scholar 

  2. Abidi, S.S.R.: Applying Knowledge Discovery in Healthcare: An Info-Structure for Delivering Knowledge-Driven Strategic Services. In: Medical Informatics Europe 1999, pp. 453–456. IOS Press, Amsterdam (1999)

    Google Scholar 

  3. Krishnaswamy, S., Zaslavsky, A., Loke, S.W.: An architecture to support distributed data mining services in e-commerce environments. In: Proceedings of Second International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS), pp. 239–246 (2000)

    Google Scholar 

  4. Chan, P.K., Fan, W., Prodromidis, A.L., Stolfo, S.J.: Distributed data mining in credit card fraud detection, Intelligent Systems. IEEE [see also IEEE Expert] 14(6), 67–74 (1999)

    Google Scholar 

  5. Kargupta, H., Park, B.: Collective Data Mining: A New Perspective toward Distributed Data Analysis. In: Kargupta, H., Chan, P. (eds.) Advanced in Distributed and Parallel Knowledge Discovery, pp. 133–184. AAAI/MIT Press (2000)

    Google Scholar 

  6. Cheung, D.W., Ng, V.T., Fu, A.W., Fu, Y.: Efficient mining of association rules in distributed databases. Proceedings of the IEEE Transactions on Knowledge and Data Engineering 8(6), 911–922 (1996)

    Article  Google Scholar 

  7. Yamanish, K.: Distributed cooperative Bayesian learning strategies. In: Proceedings of COLT 1997 (ACM), pp. 250–262 (1997)

    Google Scholar 

  8. Grossman, R.L., Yunhong, G., Hanley, D., Xinwei, H., Levera, J., Mazzucco, M., Lillethun, D., Mambretti, J., Weinberger, J.: Mass Storage Systems and Technologies (MSST). In: Proceedings of the 20th IEEE/11th NASA Goddard Conference, pp. 62–66 (2003)

    Google Scholar 

  9. Wei, D., Agrawal, G.: Developing distributed data mining implementations for a grid Environment. In: Cluster Computing and the Grid 2nd IEEE/ACM International Symposium (CCGRID), pp. 410–411 (2002)

    Google Scholar 

  10. Cannataro, M., Talia, D., Trunfio, P.: Distributed data mining on the grid. Future Generation Computer Systems 18(8), 1101–1112 (2002)

    Article  MATH  Google Scholar 

  11. Cannataro, M.: Clusters and Grids for Distributed and Parallel Knowledge Discovery. In: Williams, R., Afsarmanesh, H., Bubak, M., Hertzberger, B. (eds.) HPCN-Europe 2000. LNCS, vol. 1823, pp. 708–715. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Zaidi, S.Z.H., Abidi, S.S.R., Manickam, S.: Distributed data mining from heterogeneous healthcare data repositories: towards an intelligent agent-based framework. In: Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS), pp. 339–342 (2002)

    Google Scholar 

  13. Klusch, M., Lodi, S., Moro, G.: Agent-Based Distributed Data Mining: The KDEC Scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS (LNAI), vol. 2586, pp. 104–122. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Quinlan, J.R., Rivest, R.L.: Inferring Decision Trees Using the Minimum Description Length Principle. Information and Computation 80(3) (1989)

    Google Scholar 

  15. See Web site at http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

Download references

Author information

Authors and Affiliations

  1. Sejong University, Seoul, 143-747, KOREA

    Sung Baik

  2. Datamat Systems Research, Inc, 1600 International Drive, McLean, VA, 22102, USA

    Jerzy Bala

Authors
  1. Sung Baik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerzy Bala
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  5. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  6. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Baik, S., Bala, J. (2004). A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24768-5_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22060-2

  • Online ISBN: 978-3-540-24768-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation