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Intrusion Detection Based on Data Mining

  • Jian Yin
  • Fang Mei
  • Gang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Many traditional algorithms use single metric generated by multi-events to detect intrusion by comparison with a certain threshold. In this paper we present a metric vector-based algorithm to detect intrusion while introducing the sample distance for both discrete and continuous data in order to improve the algorithm on heterogeneous dataset. Experiments on MIT lab Data show that the proposed algorithm is effective and efficient.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Yin
    • 1
  • Fang Mei
    • 1
  • Gang Zhang
    • 1
  1. 1.Department of Computer Science, Sun Yat-Sen University, Guangzhou 510275China

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