A Granular Classifier By Means of Context-based Similarity Clustering

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.