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A New Method for Filtering IDS False Positives with Semi-supervised Classification

  • Minghua Zhang
  • Haibin Mei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

Constructing alert classifiers is an efficient way to filter IDS false positives. Classifiers built with supervised classification technique require large amounts of labeled training alerts which are difficult and expensive to prepare. This paper proposes to use semi-supervised learning technique to build alert classification model to reduce the number of needed labeled training alerts. Experiments conducted on the DARPA 1999 dataset have demonstrated that the semi-supervised alert classification model can improve the classification performance dramatically, especially when the labeled alert training dataset is small. As a result, the feasibility of deploying alert classifier for filtering false positives is enhanced.

Keywords

intrusion detection system false positive semi-supervised learning EM algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Minghua Zhang
    • 1
  • Haibin Mei
    • 1
  1. 1.Information CollegeShanghai Ocean UniversityShanghaiChina

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