Skip to main content

Distributed Intrusion Detection Based on Outlier Mining

  • Conference paper

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 181))

Abstract

With the rapid development of Internet and network technologies, intrusion detection system (IDS) is expected to be more intelligent. Generally, IDS in current use can rarely meet actual requirements in performance, accuracy and distributed characteristics. In this paper, we present a distributed network intrusion detection system, in which an improved outlier mining method on clustering is introduced. Experimental results prove that both traditional attacks like SYN flooding, and distributed attacks such as DDoS, can be detected effectively with visible accuracy rate and reliability.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   299.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   379.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Stefan, A.: Intrusion Detection System: A Survey and Taxonomy (February 2006), http://www.mnlab.cs.depaul.edu/seminar/spr2003/IDSSurvey.pdf

  2. Heckerman, D.: Bayesian Networks for Data Mining 1, 79–119 (1997)

    Google Scholar 

  3. Vaseghi, S.V.: State Duration Modeling in Hidden Markov Models 41, 32–41 (1995)

    Google Scholar 

  4. Vapnik, V.N.: Statistical Learning Theory (1998)

    Google Scholar 

  5. Dorothy Denning, E.: An intrusion-detection model. IEEE Transactions on Software Engineering 13, 222–232 (1987)

    Article  Google Scholar 

  6. Berges, C.J.C.: A tutorial on Support Vector Machines for Pattern recognition 2, 1–47 (1998)

    Google Scholar 

  7. Lippmann, R., Fried, D., Graf, I., Haines, J., Kendall, K., McClung, D.: Evaluating intrusion detection systems: the 1998 darpa off-line intrusion detection evaluation, 12–26 (2000)

    Google Scholar 

  8. Eskin, E., Arnold, A., Prerau, M.J.: A Geometric framework for unsupervised anomaly detection: Detecting intrusion in unlabeled data (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Da, W., Ting, H.S. (2013). Distributed Intrusion Detection Based on Outlier Mining. In: Yang, G. (eds) Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering. Advances in Intelligent Systems and Computing, vol 181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31698-2_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31698-2_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31697-5

  • Online ISBN: 978-3-642-31698-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics