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A Linear Genetic Programming Approach to Intrusion Detection

  • Dong Song
  • Malcolm I. Heywood
  • A. Nur Zincir-Heywood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2724)

Abstract

Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem as defined by the KDD-99 benchmark dataset. By careful adjustment of the relationship between subset layers, over fitting by individuals to specific subsets is avoided. Moreover, efficient training on a dataset of 500,000 patterns is demonstrated. Unlike the current approaches to this benchmark, the learning algorithm is also responsible for deriving useful temporal features. Following evolution, decoding of a GP individual demonstrates that the solution is unique and comparative to hand coded solutions found by experts.

Keywords

Genetic Programming Intrusion Detection Intrusion Detection System Subset Selection Attack Type 
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 2003

Authors and Affiliations

  • Dong Song
    • 1
  • Malcolm I. Heywood
    • 1
  • A. Nur Zincir-Heywood
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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