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Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

Abadeh, M. S ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1016/j.eswa.2010.12.006
  3. Publisher: 2011
  4. Abstract:
  5. The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with intrusion detection as a high-dimensional classification problem. Experiments were performed with DARPA data sets which have information on computer networks, during normal and intrusive behaviors. The paper presents some results and compares the performance of different generated fuzzy rule sets in detecting intrusion in a computer network according to three different types of genetic fuzzy systems
  6. Keywords:
  7. Abnormal behavior ; Approximate reasoning ; Combinatorial problem ; Data sets ; Design and analysis ; Fuzzy rule extraction ; Fuzzy rule set ; Genetic fuzzy systems ; High-dimensional ; Intrusion Detection Systems ; Learning ; Learning capabilities ; Michigan ; Pittsburgh ; Real-world application ; Rule learning ; Combinatorial mathematics ; Fuzzy rules ; Fuzzy sets ; Fuzzy systems ; Genetic algorithms ; Internet ; Learning algorithms ; Pattern recognition ; Intrusion detection
  8. Source: Expert Systems with Applications ; Volume 38, Issue 6 , June , 2011 , Pages 7067-7075 ; 09574174 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0957417410013692