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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 1465810, 13 pages
Research Article

Online Incremental Learning for High Bandwidth Network Traffic Classification

Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia

Received 31 October 2015; Revised 27 January 2016; Accepted 31 January 2016

Academic Editor: Jun He

Copyright © 2016 H. R. Loo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incremental -means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental -means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incremental -means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second.