SLIC: Self-Learning Intelligent Classifier for network traffic

SLIC: Self-Learning Intelligent Classifier for network traffic
SLIC: Self-Learning Intelligent Classifier for network traffic
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Computer Networks
Publication Date:
04 September 2015
Dinil Mon Divakaran, Le Su, Yung Siang Liau, Vrizlynn L. L. Thing, SLIC: Self-Learning Intelligent Classifier for network traffic, Computer Networks, Volume 91, 14 November 2015, Pages 283-297, ISSN 1389-1286,
Internet traffic classification plays an important role in the field of network security and management. Past research works utilize flow-level statistical features for accurate and efficient classification, such as the nearest-neighbor based supervised classifier. However, classification accuracy of supervised approaches is significantly affected if the size of the training set is small. More importantly, the model built using a static training set will not be able to adapt to the non-static nature of Internet traffic. With the drastic evolution of the Internet, network traffic cannot be assumed to be static. In this paper, we develop the concept of ‘self-learning’ to deal with these two challenges. We propose, design and develop a new classifier called Self-Learning Intelligent Classifier (SLIC). SLIC starts with a small number of training instances, self-learns and rebuilds the classification model dynamically, with the aim of achieving high accuracy in classifying non-static traffic flows. We carry out performance evaluations using two real-world traffic traces, and demonstrate the effectiveness of SLIC. The results show that SLIC achieves significant improvement in accuracy compared to the state-of-the-art approach.
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