Universiti Teknologi Malaysia Institutional Repository

Online incremental learning for high bandwidth network traffic classification

Loo, H. R. and Joseph, S. B. and Marsono, M. N. (2016) Online incremental learning for high bandwidth network traffic classification. Applied Computational Intelligence And Soft Computing, 2016 . pp. 1-13. ISSN 1687-9724

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Official URL: http://dx.doi.org/10.1155/2016/1465810


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.

Item Type:Article
Uncontrolled Keywords:desicion tree, Euclidean distance, network traffic
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication
Divisions:Electrical Engineering
ID Code:70105
Deposited By: Narimah Nawil
Deposited On:22 Nov 2017 00:45
Last Modified:22 Nov 2017 00:45

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