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Australasian Joint Conference on Artificial Intelligence

AI 2005: AI 2005: Advances in Artificial Intelligence pp 153–163Cite as

Machine Learning Approach to Realtime Intrusion Detection System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

Abstract

Computer security has become a critical issue with the rapid development of business and other transaction systems over the internet. Recently applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the classification performance of classifier. Selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not proper method for realtime intrusion detection system. In this paper, we develop the realtime intrusion detection system which combining on-line feature extraction method with Least Squares Support Vector Machine classifier. Applying proposed system to KDD CUP 99 data, experimental results show that it have remarkable feature feature extraction and classification performance compared to existing off-line intrusion detection system.

This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (02-PJ1-PG6-HI03-0004).

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Author information

Authors and Affiliations

  1. Dept. of Network and Information Engineering, Youngsan University, 150, Junam-ri, Ungsang-eup, Yangsan-si, Kyoungnam, 626-847, Korea

    Byung-Joo Kim

  2. Department of Computer Science, Kyungpook National University, Korea

    Il Kon Kim

Authors
  1. Byung-Joo Kim
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  2. Il Kon Kim
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, BJ., Kim, I.K. (2005). Machine Learning Approach to Realtime Intrusion Detection System. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_18

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  • DOI: https://doi.org/10.1007/11589990_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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