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.
Highlights
In the past decades, lots of techniques such as artificial intelligence and mathematical methods have been applied for many applications [1,2,3,4,5]
The symbols used in these experiments are listed as follows: Classification rate for training data (TR) denotes the performance index of training data, 3.1 Machine learning data Some machine learning data are used to evaluate the performance of the proposed entropy clustering-based granular classifiers (ECGC)
Datasets are partitioned into two parts: 80% of data is considered as training data, while the rest 20% of data is regarded as testing data
Summary
Lots of techniques such as artificial intelligence and mathematical methods have been applied for many applications [1,2,3,4,5]. With the effectiveness in high-dimensional spaces, support vector machine (SVM) becomes one of the most important classification models when solving the problem of classification. Many researchers have utilized the SVM for solving the classification problem in the field of network intrusion detection. Chitrakar and Huang [6] have presented the selection of candidate support vectors in incremental SVM for network intrusion detection. Shams et al [7] have used trust aware SVM when dealing with the network intrusion detection problems. Aburomman and Reaz [8] have proposed a novel-weighted SVM multiclass classifier for the intrusion detection system. Yaseen et al [9] have constructed multi-level hybrid SVM by means of K-means for network intrusion detection. Vijayanand et al [10] have developed genetic-algorithmbased feature selection in the design of SVM for solving
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