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Building Lightweight Intrusion Detection System Based on Random Forest

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

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

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Abstract

This paper proposes a new approach to build lightweight Intrusion Detection System (IDS) based on Random Forest (RF). RF is a special kind of ensemble learning techniques and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). In addition, RF produces a measure of importance of feature variables. Our approach is able not only to show high detection rates but also to figure out stable output of important features simultaneously. The results of experiments on KDD 1999 intrusion detection dataset indicate the feasibility of our approach.

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Author information

Authors and Affiliations

  1. Network Security Lab., Computer Engineering Department, Hankuk Aviation University, 200-1, Hwajeon-dong, Deogyang-gu, Goyang-city, Gyeonggi-do, 412-791, Korea

    Dong Seong Kim, Sang Min Lee & Jong Sou Park

Authors
  1. Dong Seong Kim
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  2. Sang Min Lee
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  3. Jong Sou Park
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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

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© 2006 Springer-Verlag Berlin Heidelberg

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Kim, D.S., Lee, S.M., Park, J.S. (2006). Building Lightweight Intrusion Detection System Based on Random Forest. 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_33

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  • DOI: https://doi.org/10.1007/11760191_33

  • 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)

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