Induction of Fuzzy Classification Systems Using Evolutionary ACO-Based Algorithms

In this paper we have proposed an evolutionary algorithm to induct fuzzy classification rules. The algorithm uses an ant colony optimization based local searcher to improve the quality of final fuzzy classification system. The proposed algorithm is performed on intrusion detection as a high-dimensio...

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Bibliographic Details
Published inAMS 2007 : proceedings [of the] first Asia International Conference on Modelling & Simulation : 27-30 March, 2007, Prince of Songkla University, Phuket, Thailand pp. 346 - 351
Main Authors Abadehm, M.S., Habibi, J., Soroush, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2007
Subjects
Ant colony optimization
Computer networks
Computer science
Evolutionary computation
Fuzzy sets
Fuzzy systems
Genetic algorithms
Intrusion detection
Knowledge based systems
Reliability engineering
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ISBN9780769528458
0769528457
DOI10.1109/AMS.2007.53

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Summary:In this paper we have proposed an evolutionary algorithm to induct fuzzy classification rules. The algorithm uses an ant colony optimization based local searcher to improve the quality of final fuzzy classification system. The proposed algorithm is performed on intrusion detection as a high-dimensional classification problem. Results show that the implemented evolutionary ACO-Based algorithm is capable of producing a reliable fuzzy rule based classifier for intrusion detection
ISBN:9780769528458
0769528457
DOI:10.1109/AMS.2007.53