BLINC MULTILEVEL TRAFFIC CLASSIFICATION IN THE DARK PDF

This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. Furthermore, our approach has two important features. BLINC. Multilevel Traffic Classification in the Dark. Thomas Karagiannis1. Konstantina Papagiannaki2. Michalis Faloutsos1. 1UC Riverside. We present a fundamentally different approach to classifying traffic flows according to the applications that generate them. In contrast to previous methods, our.

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A flow measurement architecture to preserve application structure Myungjin LeeMohammad Y. Shelton 25 Estimated H-index: Citation Statistics 1, Citations 0 50 ’07 ’10 ’13 ‘ Toward the accurate identification of network applications Andrew W.

Hall University of Waikato. KleinbergDoug J.

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A continuous time bayesian network approach for intrusion detection. This paper has 1, citations. Semantic Scholar estimates that this publication has 1, citations based on the available data.

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An analysis of Internet chat systems. Architecture of a network monitor. Is P2P dying or just hiding?

Moore 24 Estimated Blijc Claffy 1 Estimated H-index: Sung-Ho Yoon 6 Estimated H-index: Are you looking for Cited 3 Source Add To Collection. Citations Publications citing this paper. Erik Hjelmvik 2 Estimated H-index: Rao Computer Networks In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer.

BLINC: multilevel traffic classification in the dark – Semantic Scholar

Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification.

By clicking classificatioon or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. First, it operates in the darkhaving a no access to packet payload, b no knowledge of port numbers and c no additional information other than what current flow collectors provide. We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level.

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Terry Winograd 61 Estimated H-index: Second, it can be tuned to multiilevel the accuracy of the classification versus the number of successfully classified traffic flows. Thomas Karagiannis 32 Estimated H-index: Journal of Network Management Tygar Lecture Notes in Computer Science Traffic Mining in IP Tunnels.

BLINC: multilevel traffic classification in the dark

We demonstrate the effectiveness of our approach on three real traces. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. Daniele Piccitto 1 Estimated H-index: From This Paper Trafdic from this paper.