Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A supervised clustering algorithm for computer intrusion detection

  • Short Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

We previously developed a clustering and classification algorithm—supervised (CCAS) to learn patterns of normal and intrusive activities and to classify observed system activities. Here we further enhance the robustness of CCAS to the presentation order of training data and the noises in training data. This robust CCAS adds data redistribution, a supervised hierarchical grouping of clusters and removal of outliers as the postprocessing steps.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Axelsson S (2000) Intrusion detection systems: a survey and taxonomy. Report, Dept of Computer Engineering, Chalmers University of Technology, Goteborg, Sweden

    Google Scholar 

  2. Ester M, Kriegel HP, Sander J, Wimmer M, Xu X (1998) Incremental clustering for mining in a data warehousing environment. Proc 24th VLDB conference, New York, USA

  3. Harsha SG, Choudhary A (1999) Parallel subspace clustering for very large data sets. Technical report CPDC-TR-9906-010, Northwestern University, Evanston, Illinois, USA

  4. Huang C, Bi Q, Stiles R, Harris R (1992) Fast full search equivalent encoding algorithms for image compression using vector quantization. IEEE Trans Image Process 1(3):413–416

    Article  Google Scholar 

  5. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall

  6. Li X, Ye N (2002) Grid- and dummy-cluster-based learning of normal and intrusive clusters for computer intrusion detection. Qual Reliabil Eng Int 18(3)

  7. Lim TS, Loh WY, Shih YS (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn J 40:203–228

    Article  Google Scholar 

  8. Zhang T (1997) Data clustering for very large datasets plus applications. Ph.D. Thesis, Department of Computer Science, University of Wisconsin–Madison, Madison, Wisconsin, USA

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, University of Michigan—Dearborn, Dearborn, MI, 48128, USA

    Xiangyang Li

  2. Department of Industrial Engineering, Arizona State University, Tempe, AZ, USA

    Nong Ye

Authors
  1. Xiangyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nong Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangyang Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, X., Ye, N. A supervised clustering algorithm for computer intrusion detection. Knowl Inf Syst 8, 498–509 (2005). https://doi.org/10.1007/s10115-005-0195-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-005-0195-8

Keywords

Search

Navigation