On permutation masks in hamming negative selection

Thomas Stibor*, Jonathan Timmis, Claudia Eckert

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

7 Citations (SciVal)


Permutation masks were proposed for reducing the number of holes in Hamming negative selection when applying the r-contignous 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.

Original languageEnglish
Title of host publicationArtificial Immune Systems - 5th International Conference, ICARIS 2006. Proceedings
PublisherSpringer Nature
Number of pages14
ISBN (Print)3540377492, 9783540377498
Publication statusPublished - 2006
Event5th International Conference on Artificial Immune Systems, ICARIS 2006 - Oeiras, Portugal
Duration: 04 Sept 200606 Sept 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Artificial Immune Systems, ICARIS 2006
Period04 Sept 200606 Sept 2006


  • negative selection
  • anomaly detection
  • network intrusion detection
  • generalization region
  • grey shade area

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