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Intrusion detection using a hybridization of evolutionary fuzzy systems and artificial immune systems

Saneei Abadeh, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/CEC.2007.4424932
  3. Publisher: 2007
  4. Abstract:
  5. This paper presents a novel hybrid approach for intrusion detection in computer networks. The proposed approach combines an evolutionary based fuzzy system with an artificial immune system to generate high quality fuzzy classification rules. The performance of final fuzzy classification system has been investigated using the KDD-Cup99 benchmark dataset. The results indicate that in comparison to several traditional techniques, such as C4.5, Naïve Bayes, k-NN and SVM, the proposed hybrid approach achieves better classification accuracies for most of the classes of the intrusion detection classification problem. Therefore, the resulted fuzzy classification rules can be used to produce a reliable intrusion detection system. © 2007 IEEE
  6. Keywords:
  7. Artificial immune systems ; Fuzzy classification ; Hybridization ; Classification (of information) ; Evolutionary algorithms ; Fuzzy rules ; Intrusion detection ; Fuzzy systems
  8. Source: 2007 IEEE Congress on Evolutionary Computation, CEC 2007; Singapour 25 September 2007 through 28 September 2007 ; 2007 , Pages 3547-3553 ; 1424413400 (ISBN); 9781424413409 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4424932