Network traffic classification using a random field model

Gang Shen; Zhaojie Niu; Liyuan Duan

doi:10.1117/12.2030954

19 July 2013 Network traffic classification using a random field model

Gang Shen, Zhaojie Niu, Liyuan Duan

Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 887835 (2013) https://doi.org/10.1117/12.2030954
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China

Abstract

The accurate identification of the different protocols used by various applications plays an important role in many network management and monitoring tasks. However, the development of emerging applications and the evolution of existing applications have made the early success of port number or payload signature based classification methods no longer repeatable. On the other hand, machine learning based approaches have achieved steady progress in classification accuracy, with the statistical features extracted from packets and flows. In this paper, by introducing a Markov random field to model the semantics of network application protocols, we investigate a new approach to classifying network traffic into application protocols. First the packets in a flow are aggregated into messages that contain the related semantics information. We assume that the simple message features like the length and the direction of a message are observable, while the semantics of messages are invisible in both training and test phases. Tested with traffic traces collected from heterogeneous sources, this approach was demonstrated to be able to deliver good accuracy and speed.

Citation Download Citation

Gang Shen, Zhaojie Niu, and Liyuan Duan "Network traffic classification using a random field model", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887835 (19 July 2013); https://doi.org/10.1117/12.2030954

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KEYWORDS

Classification systems

Machine learning

Data modeling

Feature extraction

Statistical modeling

Network security

Image classification

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