An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming

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@Article{Mabu:2011:ieeeSMC,
  author =       "Shingo Mabu and Ci Chen and Nannan Lu and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "An Intrusion-Detection Model Based on Fuzzy
                 Class-Association-Rule Mining Using Genetic Network
                 Programming",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part C: Applications and Reviews",
  year =         "2011",
  month =        jan,
  volume =       "41",
  number =       "1",
  pages =        "130--139",
  abstract =     "As the Internet services spread all over the world,
                 many kinds and a large number of security threats are
                 increasing. Therefore, intrusion detection systems,
                 which can effectively detect intrusion accesses, have
                 attracted attention. This paper describes a novel fuzzy
                 class-association-rule mining method based on genetic
                 network programming (GNP) for detecting network
                 intrusions. GNP is an evolutionary optimisation
                 technique, which uses directed graph structures instead
                 of strings in genetic algorithm or trees in genetic
                 programming, which leads to enhancing the
                 representation ability with compact programs derived
                 from the reusability of nodes in a graph structure. By
                 combining fuzzy set theory with GNP, the proposed
                 method can deal with the mixed database that contains
                 both discrete and continuous attributes and also
                 extract many important class-association rules that
                 contribute to enhancing detection ability. Therefore,
                 the proposed method can be flexibly applied to both
                 misuse and anomaly detection in
                 network-intrusion-detection problems. Experimental
                 results with KDD99Cup and DARPA98 databases from MIT
                 Lincoln Laboratory show that the proposed method
                 provides competitively high detection rates compared
                 with other machine-learning techniques and GNP with
                 crisp data mining.",
  keywords =     "genetic algorithms, genetic programming, directed
                 graph structures, fuzzy class-association-rule mining,
                 fuzzy set theory, genetic network programming,
                 intrusion-detection model, data mining, directed
                 graphs, fuzzy set theory, security of data",
  DOI =          "doi:10.1109/TSMCC.2010.2050685",
  ISSN =         "1094-6977",
  notes =        "Also known as \cite{5499108}",
}

Genetic Programming entries for Shingo Mabu Ci Chen Nannan Lu Kaoru Shimada Kotaro Hirasawa

Citations