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Auto-generation of Detection Rules with Tree Induction Algorithm

  • Minsoo Kim
  • Jae-Hyun Seo
  • Il-Ahn Cheong
  • Bong-Nam Noh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

A generation of rule for detecting an attack from enormous network data is very difficult, and this is commonly required an expert’s experiences. An auto-generation of detection rules cut down on maintenance or management expenses of intrusion detection systems, but the problem is accuracy for the time being. In this paper, we propose an automatic generation method of detection rules with a tree induction algorithm that is adequate to search special rules based on entropy theory. While we progress the experiment on rule generation and detection with extracted information from network session data, we found a problem in selecting measures. To solve the problem, we present a method of converting the continuous measures into categorical measures and a method of choosing a good measure according to the accuracy of the generated detection rules. As the result, the detection rules for each attack are automatically generated without any help of the experts. Also, the correctness of detection improves according to the selection of network measures.

Keywords

Continuous Measure False Alarm Rate Intrusion Detection Intrusion Detection System Network Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Minsoo Kim
    • 1
  • Jae-Hyun Seo
    • 1
  • Il-Ahn Cheong
    • 2
  • Bong-Nam Noh
    • 3
  1. 1.Dept. of Information SecurityMokpo Nat’l Univ.MokpoKorea
  2. 2.Electronics and Telecommunications Research InstituteDaejeonKorea
  3. 3.Div. of Electr-Comp. & Inform-Engin.Chonnam Nat’l Univ.GwangjuKorea

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