Improving Network Intrusion Detection with Extended KDD Features

  • Edward Paul Guillén
  • Jhordany Rodríguez Parra
  • Rafael Vicente Paéz Mendez
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)

Abstract

In order to analyze results of anomaly detection methods for Network Intrusion Detection Systems, the DARPA KDD data set have been widely analyzed but their data are outdated for most kinds of attacks. A software called Spleen designed to get data from a tested network with the same structure of DARPA data set is introduced. The application is used to complete the data set with additional features according to an attack analysis. Finally, to show advantages of an extended data set, two genetic methods in the detection of non-content based attacks are tested.

Keywords

Adaptative algorithm Genetic algorithms Information security Intrusion detection Machine learning TCPIP 

Notes

Acknowledgments

This work was possible with the support of Military University Doctoral Support Program, and Javeriana University Doctoral Program.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Edward Paul Guillén
    • 1
  • Jhordany Rodríguez Parra
    • 1
  • Rafael Vicente Paéz Mendez
    • 2
  1. 1.Telecomunications Engineering DepartmentMilitary University “Nueva Granada”BogotáColombia
  2. 2.Systems Engineering DepartmentJaveriana UniversityBogotáColombia

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