Dynamic Online Traffic Classification Using Data Stream Mining
Xu Tian, Qiong Sun, Xiaohong Huang, Yan Ma · 2008
Recently, traffic classification becomes more and more important for network management and measurement tasks. In this paper, we make a first step towards dynamic online traffic classification using data stream mining method. Two main contributions are as follows. Firstly, we propose a novel integrated dynamic online traffic classification framework, called DSTC (data stream based traffic classification). Secondly, a data stream mining algorithm, called VFDT (very fast decision tree) is implemented in DSTC, which can identify all kinds of traffic, e.g. encrypted traffic and peer-to-peer traffic, with several remarkable advantages: 1) It was designed to handle multiple, continuous, rapid, time-vary, and potential unbounded network traffic; 2) It provides real-time high accuracy traffic classification by using memory efficient method; 3) The underlying training model can adjust incrementally for newly emerging applications; 4) The training phase can go simultaneously with classification phase. The experiment results show that DSTC achieves extremely fast update speed and small memory cost with high accuracy of above 98%.