NDLI: Cascaded classifier for improving traffic classification accuracy

Content Provider	IET Digital Library
Author	Lu, Gang Guo, Ronghua
Abstract	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.
Starting Page	1751
Ending Page	1758
Page Count	8
ISSN	17518628
Volume Number	11
e-ISSN	17518636
Issue Number	Issue 11, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/11/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2017.0091
Journal	IET Communications
Publisher Date	2017-04-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cascaded Classification Algorithm Cascaded Classifier Computer Communication Flow Level Statistics Improving Traffic Classification Accuracy Information Network Internet Internet Service Providers Knowledge Engineering Technique Learning in AI Machine Learning ML Technique Multiclass Subclassifier Multiclass Traffic Classifier Other Computer Network Statistical Analysis Statistics Telecommunication Traffic
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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