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Generation of Efficient Fuzzy Classification Rules for Intrusion Detection  

Kim, Sung-Eun ((주)퓨처시스템 정보통신연구소)
Khil, A-Ra (숭실대학교 컴퓨터학부)
Kim, Myung-Won (숭실대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.6, 2007 , pp. 519-529 More about this Journal
Abstract
In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.
Keywords
intrusion detection; fuzzy classification rule; evolutionary algorithm; partition evolutionary method; supervised clustering;
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Times Cited By KSCI : 1  (Citation Analysis)
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