Skip to main content
Springer Nature Link
Log in

Using Boosting Learning Method for Intrusion Detection

  • Conference paper
Advanced Data Mining and Applications (ADMA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3584))

Included in the following conference series:

  • 2376 Accesses

Abstract

It is an important research topic to improve detection rate and reduce false positive rate of detection model in the field of intrusion detection. This paper adopts an improved boosting method to enhance generalization performance of intrusion detection model based on rule learning algorithm, and presents a boosting intrusion detection rule learning algorithm (BIDRLA). The experiment results on the standard intrusion detection dataset validate the effectiveness of BIDRLA.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Illgun, K., Kemmerer, R., Philips, A.: State Transition Analysis: A Rule-based Intrusion Detection Approach. IEEE Transaction on Software Engineering 2, 181–199 (1995)

    Article  Google Scholar 

  2. Karlton, S., Mohammed, Z.: ADMIT: Anomaly-based Data Mining for Intrusions. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 386–395. ACM Press, Edmonton Alberta Canada (2002)

    Google Scholar 

  3. Wenke, L., Stolfo, S.J., Mok, K.W.: A Data Mining Framework for Building Intrusion Detection Models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120–132. IEEE Press, Oakland (1999)

    Google Scholar 

  4. William, W.C.: Efficient Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  5. Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 2, 197–227 (1990)

    Google Scholar 

  6. Freund, Y., Robert, E.S.: A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences 1, 119–139 (1997)

    Article  Google Scholar 

  7. Quinlan, J.R.: Bagging, Boosting and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park, pp. 725–730 (1996)

    Google Scholar 

  8. Schapire, R.E., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91. ACM Press, Madison Wisconsin United States (1998)

    Chapter  Google Scholar 

  9. KDD CUP 1999 (1999), http://kdd.ics.uci.edu/database/kddcup99/kddcup99.html

Download references

Author information

Authors and Affiliations

  1. Information Security Research Center, Harbin Engineering University, Harbin, 150001, China

    Wu Yang, Xiao-Chun Yun & Yong-Tian Yang

  2. Computer Network and Information Security Technique Research Center, Harbin Institute of Technology, Harbin, 150001, China

    Xiao-Chun Yun

Authors
  1. Wu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Chun Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong-Tian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

    Xue Li

  2. The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430072, Wuhan, China

    Shuliang Wang

  3. School of ITEE, The Univ of Queensland, St. Lucia, 4072, QLD, Australia

    Zhao Yang Dong

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, W., Yun, XC., Yang, YT. (2005). Using Boosting Learning Method for Intrusion Detection. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_75

Download citation

  • DOI: https://doi.org/10.1007/11527503_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27894-8

  • Online ISBN: 978-3-540-31877-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation