The Effect of Binary Matching Rules in Negative Selection View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2003-06-18

AUTHORS

Fabio González , Dipankar Dasgupta , Jonatan Gómez

ABSTRACT

Negative selection algorithm is one of the most widely used techniques in the field of artificial immune systems. It is primarily used to detect changes in data/behavior patterns by generating detectors in the complementary space (from given normal samples). The negative selection algorithm generally uses binary matching rules to generate detectors. The purpose of the paper is to show that the low-level representation of binary matching rules is unable to capture the structure of some problem spaces. The paper compares some of the binary matching rules reported in the literature and study how they behave in a simple two-dimensional real-valued space. In particular, we study the detection accuracy and the areas covered by sets of detectors generated using the negative selection algorithm. More... »

PAGES

195-206

References to SciGraph publications

  • 2001-09-27. CDIS: Towards a Computer Immune System for Detecting Network Intrusions in RECENT ADVANCES IN INTRUSION DETECTION
  • Book

    TITLE

    Genetic and Evolutionary Computation — GECCO 2003

    ISBN

    978-3-540-40602-0
    978-3-540-45105-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-45105-6_25

    DOI

    http://dx.doi.org/10.1007/3-540-45105-6_25

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1014094239


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