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Moosavi, Mohammad Reza, Fazaeli Javan, Mahsa, Sadreddini, Mohammad Hadi, Zolghadri Jahromi, Mansoor. (1393). Entropy Based Fuzzy Rule Weighting for Hierarchical Intrusion Detection. نشریه علمی دانشگاه سیستان و بلوچستان, 11(3), 77-94. doi: 10.22111/ijfs.2014.1571
Mohammad Reza Moosavi; Mahsa Fazaeli Javan; Mohammad Hadi Sadreddini; Mansoor Zolghadri Jahromi. "Entropy Based Fuzzy Rule Weighting for Hierarchical Intrusion Detection". نشریه علمی دانشگاه سیستان و بلوچستان, 11, 3, 1393, 77-94. doi: 10.22111/ijfs.2014.1571
Moosavi, Mohammad Reza, Fazaeli Javan, Mahsa, Sadreddini, Mohammad Hadi, Zolghadri Jahromi, Mansoor. (1393). 'Entropy Based Fuzzy Rule Weighting for Hierarchical Intrusion Detection', نشریه علمی دانشگاه سیستان و بلوچستان, 11(3), pp. 77-94. doi: 10.22111/ijfs.2014.1571
Moosavi, Mohammad Reza, Fazaeli Javan, Mahsa, Sadreddini, Mohammad Hadi, Zolghadri Jahromi, Mansoor. Entropy Based Fuzzy Rule Weighting for Hierarchical Intrusion Detection. نشریه علمی دانشگاه سیستان و بلوچستان, 1393; 11(3): 77-94. doi: 10.22111/ijfs.2014.1571

Entropy Based Fuzzy Rule Weighting for Hierarchical Intrusion Detection

Iranian Journal of Fuzzy Systems
مقاله 6، دوره 11، شماره 3، شهریور 2014، صفحه 77-94    اصل مقاله (483.13 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2014.1571
نویسندگان
Mohammad Reza Moosavi* ؛ Mahsa Fazaeli Javan؛ Mohammad Hadi Sadreddini؛ Mansoor Zolghadri Jahromi
Department of Computer Science and Eng. and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
چکیده
Predicting different behaviors in computer networks is the subject of many data mining researches. Providing a balanced Intrusion Detection System (IDS) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. Many of the proposed methods perform well in one of the two aspects, and concentrate on a subset of system requirements. There are many non-functional requirements for an applicable and practical IDS. The process should be online, incremental and adaptive to ever changing behaviors of normal users and attackers. Moreover providing comprehensive and interactive IDS could both, enhance the performance of the system and extend the knowledge of domain experts.
In this paper, we propose a fuzzy rule-based classification system using a hierarchical rule learning method. In each stage of the hierarchy, a set of rules with certain length of antecedent are investigated. A novel rule weighting method, based on the entropy measure, determines the appropriateness of each rule. The experimental results on KDD99 intrusion detection dataset show the effectiveness of the proposed method in tackling the tradeoff between accuracy and comprehensibility of fuzzy rule-based systems. Although the dimension of antecedents is not limited, the resultant rule-base contains a small number of complex rules, which are essential to reach the desired accuracy.
کلیدواژه‌ها
Intrusion detection؛ Hierarchical classification؛ Iterative fuzzy rule-based system؛ Rule weighting؛ Entropy measure؛ KDD99
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