On Permutation Masks in Hamming Negative Selection

  • Thomas Stibor
  • Jonathan Timmis
  • Claudia Eckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)


Permutation masks were proposed for reducing the number of holes in Hamming negative selection when applying the r-contiguous or r-chunk matching rule. Here, we show that (randomly determined) permutation masks re-arrange the semantic representation of the underlying data and therefore shatter self-regions. As a consequence, detectors do not cover areas around self regions, instead they cover randomly distributed elements across the space. In addition, we observe that the resulting holes occur in regions where actually no self regions should occur.


Negative Selection Anomaly Detection Network Intrusion Detection Generalization Region Grey Shade Area 
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 2006

Authors and Affiliations

  • Thomas Stibor
    • 1
  • Jonathan Timmis
    • 2
  • Claudia Eckert
    • 1
  1. 1.Department of Computer ScienceDarmstadt University of Technology 
  2. 2.Departments of Electronics and Computer ScienceUniversity of YorkHeslington

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