An effective network traffic classification method with unknown flow detection

Zhang, Jun, Chen, Chao, Xiang, Yang, Zhou, Wanlei, and Vasilakos, Athanasios V. (2013) An effective network traffic classification method with unknown flow detection. IEEE Transactions on Network and Service Management, 10 (2). pp. 133-147.

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Traffic classification technique is an essential tool for network and system security in the complex environments such as cloud computing based environment. The state-of-the-art traffic classification methods aim to take the advantages of flow statistical features and machine learning techniques, however the classification performance is severely affected by limited supervised information and unknown applications. To achieve effective network traffic classification, we propose a new method to tackle the problem of unknown applications in the crucial situation of a small supervised training set. The proposed method possesses the superior capability of detecting unknown flows generated by unknown applications and utilizing the correlation information among real-world network traffic to boost the classification performance. A theoretical analysis is provided to confirm performance benefit of the proposed method. Moreover, the comprehensive performance evaluation conducted on two real-world network traffic datasets shows that the proposed scheme outperforms the existing methods in the critical network environment.

Item ID: 64413
Item Type: Article (Research - C1)
ISSN: 1932-4537
Copyright Information: © 2013 IEEE.
Date Deposited: 28 Jul 2022 00:07
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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