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