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Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems

  • S. Elhag
  • A. Fernández
  • S. Alshomrani
  • F. Herrera
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 779)

Abstract

The so-called Evolutionary Fuzzy Systems consists of the application of evolutionary algorithms in the design process of fuzzy systems. Thanks to this hybridization, excellent abilities are provided to fuzzy systems in different work scenarios of data mining, such as standard classification, regression problems and association rule mining. The main reason of their success is the adaptation of their inner characteristics to any context. Among different areas of application, Evolutionary Fuzzy Systems have recently excelled in the area of Intrusion Detection Systems, yielding both accurate and interpretable models. To fully understand the nature and goodness of these type of models, we will introduce a full taxonomy on Evolutionary Fuzzy Systems. Then, we will overview a number of proposals from this research area that have been developed to address Intrusion Detection Systems. Finally, we will present a case study highlighting the good behaviour of Evolutionary Fuzzy Systems in this particular context.

Keywords

Computational intelligence Evolutionary fuzzy systems Intrusion detection systems Multi-objective evolutionary fuzzy systems Fuzzy rule based systems 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • S. Elhag
    • 1
  • A. Fernández
    • 2
  • S. Alshomrani
    • 3
  • F. Herrera
    • 2
  1. 1.Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  3. 3.Faculty of Computing and Information TechnologyUniversity of JeddahJeddahSaudi Arabia

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