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Computer intrusion detection using an iterative fuzzy rule learning approach

Saniee Abadeh, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/FUZZY.2007.4295375
  3. Publisher: 2007
  4. Abstract:
  5. The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system. © 2007 IEEE
  6. Keywords:
  7. Classification (of information) ; Computer crime ; Computer networks ; Computer systems ; Evolutionary algorithms ; Fuzzy logic ; Fuzzy rules ; Fuzzy sets ; Fuzzy systems ; Iterative methods ; Laws and legislation ; Process engineering ; Sensors ; Signal detection ; Computer intrusion ; Fuzzy classification rules ; Fuzzy classification systems ; Fuzzy classifiers ; Fuzzy rule base ; Fuzzy rule learning ; High-dimensional classification ; International conferences ; Intrusion detection system ; Rule learning ; System designs ; Intrusion detection
  8. Source: 2007 IEEE International Conference on Fuzzy Systems, FUZZY, London, 23 July 2007 through 26 July 2007 ; 2007 ; 10987584 (ISSN) ; 1424412102 (ISBN); 9781424412105 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4295375