Skip to main content
SpringerLink
Log in

A Novel Intrusion Detection Model Based on Multi-layer Self-Organizing Maps and Principal Component Analysis

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Included in the following conference series:

Abstract

In this paper, the Self Organizing Maps (SOM) learning and classification algorithms are firstly modified. Then via the introduction of match-degree, reduction-rate and quantification error of reducing sample, a novel approach to intrusion detection based on Multi-layered modified SOM neural network and Principal Component Analysis (PCA) is proposed. In this model, PCA is applied to feature selection, and Multi-layered SOM is designed to subdivide the imprecise clustering in single-layered SOM layer by layer. Experimental results demonstrate that this model can provide a precise and efficient way for implementing the classifier in intrusion detection.

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 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Li, Z.J., Wu, Y., Wang, G.Y.: A new framework for intrusion detection based on rough set theory. In: Proceedings of SPIE, vol. 5433, pp. 122–130 (2004)

    Google Scholar 

  2. Lin, S.C., Shi, D.H.: A Study of Intrusion Detection System Based on Anomaly Detection in Windows Environment. Master thesis, Taiwan of China (2004)

    Google Scholar 

  3. Lichodzijewski, P.: Dynamic Intrusion Detection Using Self Organizing Maps. In: 14th Annual Canadian Information Technology Security Symposium (2002)

    Google Scholar 

  4. Eskin, E.A. (ed.): A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. Data Mining for Security Applications. Kluwer, Dordrecht (2002)

    Google Scholar 

  5. Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9) (1990)

    Google Scholar 

  6. Kayacik, H.G., Zincir Heywood, A.N., Heywood, M.I.: On Dataset Biases in a Learning System with Minimum a priori Information for Intrusion Detection. In: Proceedings of the IEEE CNSR, Fredericton, Canada, pp. 181–189 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Jie Bai, Yu Wu, Guoyin Wang & Wenbin Qiu

  2. School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada

    Simon X. Yang

Authors
  1. Jie Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoyin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simon X. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenbin Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bai, J., Wu, Y., Wang, G., Yang, S.X., Qiu, W. (2006). A Novel Intrusion Detection Model Based on Multi-layer Self-Organizing Maps and Principal Component Analysis. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_37

Download citation

  • DOI: https://doi.org/10.1007/11760191_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation