Machine Learning Approach to Realtime Intrusion Detection System

  • Byung-Joo Kim
  • Il Kon Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


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.

Content Areas

machine learning data mining knowledge discovery industrial applications of AI 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Byung-Joo Kim
    • 1
  • Il Kon Kim
    • 2
  1. 1.Dept. of Network and Information EngineeringYoungsan UniversityKyoungnamKorea
  2. 2.Department of Computer ScienceKyungpook National UniversityKorea

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