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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BLINC: multilevel traffic classification in the dark – Semantic Scholar

Claffy 1 Estimated H-index: Pavel Piskac 1 Estimated H-index: In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. Furthermore, our approach has two important features. Gang Xiong 4 Estimated H-index: Hall University of Waikato. This paper has highly influenced other papers. Terry Winograd 61 Estimated H-index: Erik Hjelmvik 2 Estimated H-index: Second, it can be tuned to balance the clqssification of the classification versus the number of successfully classified traffic flows.

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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.

KleinbergDoug J. Showing of extracted citations. Toward the accurate identification of network applications.

BLINC: multilevel traffic classification in the dark

Pieter Classificatuon 3 Estimated H-index: Toward the accurate identification of network applications Andrew W. Alberto Dainotti 20 Estimated H-index: Statistical Clustering of Internet Communication Patterns.

Architecture of a network monitor. A continuous time bayesian network approach for intrusion detection.

Citations Publications citing clasification paper. Network packet Tracing software. We analyze these patterns at three levels of increasing detail i the social, ii the multlievel and iii the application level. Sung-Ho Yoon 6 Estimated H-index: Journal of Network Management We present a fundamentally different approach to classifying traffic flows according to the applications that generate them. Thomas Karagiannis 1 Estimated H-index: Traffic Mining in IP Tunnels. Tygar Lecture Notes in Computer Science A parameterizable methodology for Internet traffic flow profiling.

File-sharing in the Internet: Daniele Piccitto 1 Estimated H-index: These restrictions respect privacy, technological and practical constraints. Citation Statistics 1, Citations 0 50 ’07 ’10 ’13 ‘ See our FAQ for additional information. Semantic Scholar estimates that this publication has 1, citations based on the available data.

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Using of time characteristics in data flow for traffic classification. This paper has 1, citations. This multilevel approach of looking at traffic flow is probably the most important contribution thd this paper.

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Rao Computer Networks Cited 3 Source Add To Collection. Topics Discussed in This Paper.

In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. Thomas Karagiannis 32 Estimated H-index: Other Papers By First Author. Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer rark Network packet Traffic classification. From This Paper Topics from this paper.