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Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection

  • Chuanhuan Yin
  • Shengfeng Tian
  • Houkuan Huang
  • Jun He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3612)

Abstract

The DARPA/MIT Lincoln Laboratory off-line intrusion detection evaluation data set is the most widely used public benchmark for testing intrusion detection systems. But the presence of simulation artifacts attributes would cause many attacks in this dataset to be easily detected. In order to eliminate their influence on intrusion detection, we simply omit these attributes in the processes of both training and testing. We also present a GP-based rule learning approach for detecting attacks on network. GP is used to evolve new rules from the initial learned rules through genetic operations. Our results show that GP-based rule learning approach outperforms the original rule learning algorithm, detecting 84 of 148 attacks at 100 false alarms despite the absence of several simulation artifacts attributes.

Keywords

False Alarm Intrusion Detection Network Traffic Anomaly Detection Intrusion Detection System 
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

  • Chuanhuan Yin
    • 1
  • Shengfeng Tian
    • 1
  • Houkuan Huang
    • 1
  • Jun He
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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