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논문 기본 정보

자료유형
학술저널
저자정보
Wei Huang (Tianjin University of Technology) Jinsong Wang (Tianjin University of Technology) Jiping Liao (Tianjin University of Technology)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.11 No.5
발행연도
2016.9
수록면
1,383 - 1,394 (12page)

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초록· 키워드

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In this study, we propose a granular classifier (GC) with the aid of a context-based similarity clustering (CSC) method and applied it for network intrusion detection. The proposed CSC supporting the design of information granules is exploited here to determine the so-called contexts. Unlike the conventional similar clustering method, here the CSC built clusters by taking into consideration of both input data and output data. The design of granular classifier is realized based on the if-then rules, which consists two parts: namely premise part and conclusion part. The premise part is developed by using the CSC, while the conclusion part is realized with the aid of supported vector machines. In contrast to typical rule-based classifier, the underlying principle exploited here is to consider a robust classification with the adequate use of output data. In particular, rule-based classifiers or supported vector machines can be regarded as a special case of the proposed granular classifier. Numeric studies show the superiority of the proposed approach.

목차

Abstract
1. Introduction
2. Granular Classifier: an idea
3. Architecture of Granular Classifier
4. Design Procedure of Granular Classifiers
5. Experimental Studies
6. Concluding Remarks
References

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