Cascaded classifier for improving traffic classification accuracy
Gang Lü, Ronghua Guo · IET Communications · 2017
Machine learning (ML) techniques have been widely applied in recent traffic classification. However, the flow‐level statistics are prone to improve the accuracies for some applications; however, to reduce the accuracies for others. To address the problem, the authors propose a cascaded traffic classifier that is composed of both several binary sub‐classifiers and a multiclass sub‐classifier. The authors first present theorems that show how to make an optimal cascade of sub‐classifiers, and then design a cascaded classification algorithm for improving the accuracy of flow‐level traffic classification. In addition, to improve the classification speed, the authors propose a parallel scheme for the cascaded classifier. The authors evaluate their approaches on the traces captured from entirely different networks. Compared with the previous multiclass traffic classifiers built in one‐time training process, the cascaded classifier is superior in terms of the overall accuracy and the accuracy for each application.