Abstract

Support vector machine (SVM) is one of the effective classifiers in the field of network intrusion detection; however, some important information related to classification might be lost in the reprocessing. In this paper, we propose a granular classifier based on entropy clustering method and support vector machine to overcome this limitation. The overall design of classifier is realized with the aid of if-then rules that consists of a premise part and conclusion part. The premise part realized by the entropy clustering method is used here to address the problem of a possible curse of dimensionality, while the conclusion part realized by support vector machines is utilized to build local models. In contrast to the conventional SVM, the proposed entropy clustering-based granular classifiers (ECGC) can be regarded as an entropy-based support function machine. Moreover, an opposition-based genetic algorithm is proposed to optimize the design parameters of the granular classifiers. Experimental results show the effectiveness of the ECGC when compared with some classical models reported in the literatures.

中文翻译：

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基于熵聚类的粒度分类器用于网络入侵检测

摘要

支持向量机（SVM）是网络入侵检测领域的有效分类器之一。但是，一些与分类有关的重要信息可能会在重新处理中丢失。在本文中，我们提出了一种基于熵聚类和支持向量机的粒度分类器来克服这一限制。分类器的总体设计是通过由前提部分和结论部分组成的if-then规则来实现的。此处，通过熵聚类方法实现的前提部分用于解决可能的维数诅咒问题，而通过支持向量机实现的结论部分用于构建局部模型。与传统的SVM相比，所提出的基于熵聚类的粒度分类器（ECGC）可以看作是基于熵的支持函数机。此外，提出了一种基于对立的遗传算法来优化颗粒分类器的设计参数。实验结果表明，与文献报道的一些经典模型相比，ECGC的有效性。