Internet traffic classification using SVM with flexible feature space

Abstract

SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.

Keywords

• support vector machine
• flexible feature space
• traffic classification