Abstract
A novel approach for fast traffic classification in the high speed networks is proposed, which bases on the protocol behavior statistical features. The frame lengths, arrival times and direction of packets are collected from the real data flows. Comparing the features of the unknown flow with the protocol masks, we can judge which application protocol this flow belongs to. Distinct from other statistic methods, we use the “universal flow-based inter-arrival time” to overcome the influence of RTT variance so that a set of excellent protocol masks is site-independent and time-independent. Because there is no need for character string searching and complex algorithms, the proposed approach can be easily deployed in the hardware of high speed network equipments.
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Gu, R., Hong, M., Wang, H., Ji, Y. (2008). Fast Traffic Classification in High Speed Networks. In: Ma, Y., Choi, D., Ata, S. (eds) Challenges for Next Generation Network Operations and Service Management. APNOMS 2008. Lecture Notes in Computer Science, vol 5297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88623-5_44
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DOI: https://doi.org/10.1007/978-3-540-88623-5_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88622-8
Online ISBN: 978-3-540-88623-5
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