Traffic Classification Method Based On Data Stream Fingerprint

DOI

10.2991/icamcs-16.2016.150How to use a DOI?

Keywords

Traffic classification, Renyi cross entropy, Data stream fingerprint, Similarity

Abstract

Traffic classification is a method for categorizing the computer network traffic into a number of traffic class based on various features observed passively from the traffic. In recent years, duo to the rapid development of the Internet, as well as the rapid increase of different Internet application, the requirement to distinguish between the different applications is rising. Many traditional methods like port based, packets based and some alternate methods based on machine learning approaches have been used for the traffic classification. In this paper, a new traffic classification method was proposed to utilize the data stream fingerprint information generated by an application. The proposed new method is compared with other network traffic classification methods. The experimental results show that the classification accuracy of the new method meet the actual needs.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kefei Cheng
AU  - Guohui Wei
AU  - Xiangjun Ma
PY  - 2016/06
DA  - 2016/06
TI  - Traffic Classification Method Based On Data Stream Fingerprint
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 742
EP  - 746
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.150
DO  - 10.2991/icamcs-16.2016.150
ID  - Cheng2016/06
ER  -