NDLI: Traffic identification using flexible neural trees

Content Provider	IEEE Xplore Digital Library
Author	Lizhi Peng Hongli Zhang Bo Yang Yuehui Chen Qassrawi, M.T. Gang Lu
Copyright Year	2010
Description	Author affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150002, P. R. China (Lizhi Peng; Hongli Zhang; Qassrawi, M.T.; Gang Lu) \|\| School of Information Science and Engineering, University of Jinan, Jinan, 250022, P. R. China (Bo Yang; Yuehui Chen)
Abstract	Traditional traffic classification techniques like port-based and payload-based techniques are becoming ineffective owning to more and more Internet applications using dynamic port number and encryption techniques. Therefore, in the past few years, many researches have addressed machine learning-based techniques. Most researches of machine learning-based traffic identification use traffic samples collected on key nodes of networks for their learning. These samples do not have accurate application information i. e. the ground truth which is crucial for machine learning algorithms. In this paper, we first designed a distributed host based traffic collecting platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then we built a data set using DHTCP, and applied Flexible Neural Trees (FNT) — a special kind of artificial neural network which has been successfully applied in many areas, for traffic identification. Web and P2P traffics were studied in our work. Although the proposed technique is at an early stage of development, experimental results show that it is a promising solution of Internet traffic identification.
Starting Page	1
Ending Page	5
File Size	216132
Page Count	5
File Format	PDF
ISBN	9781424459872
ISSN	1548615X
e-ISBN	9781424459865
DOI	10.1109/IWQoS.2010.5542729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Telecommunication traffic Traffic control Machine learning Support vector machines Support vector machine classification Machine learning algorithms Internet Cryptography Protocols Artificial neural networks Traffic classification Flexible Neural Tree
Content Type	Text
Resource Type	Article

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