Zhang, Jun, Chen, Chao, Xiang, Yang, and Zhou, Wanlei (2012) Classification of correlated Internet traffic flows. In: Proceedings of the 11th International Conference on Trust, Security and Privacy in Computing and Communications. pp. 490-496. From: TrustCom 2012: IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 25-27 June 2012, Liverpool, UK.

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Abstract

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.

Item ID:	64409
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4673-2172-3
Copyright Information:	(C) IEEE
Date Deposited:	10 Aug 2021 02:36
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080303 Computer System Security @ 100%
SEO Codes:	89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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