Internet traffic classification using machine learning : a token-based approach
Wang, Yu, Xiang, Yang and Yu, Shunzheng 2011, Internet traffic classification using machine learning : a token-based approach, in CSE 2011 : Proceedings of the 14th IEEE International Conference on Computational Science and Engineering, IEEE, [Dalian, China], pp. 285-289.
Attached Files
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
Name
Description
MIMEType
Size
Downloads
Title
Internet traffic classification using machine learning : a token-based approach
Due to the increasing unreliability of traditional port-based methods, Internet traffic classification has attracted a lot of research efforts in recent years. Quite a lot of previous papers have focused on using statistical characteristics as discriminators and applying machine learning techniques to classify the traffic flows. In this paper, we propose a novel machine learning based approach where the features are extracted from packet payload instead of flow statistics. Specifically, every flow is represented by a feature vector, in which each item indicates the occurrence of a particular token, i.e.; a common substring, in the payload. We have applied various machine learning algorithms to evaluate the idea and used different feature selection schemes to identify the critical tokens. Experimental result based on a real-world traffic data set shows that the approach can achieve high accuracy with low overhead.