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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Skip to search form Skip to main content. A parameterizable methodology for Internet traffic flow profiling.

Tygar Lecture Notes in Computer Science Traffic Mining in IP Tunnels. A continuous time bayesian network approach for intrusion detection. Are you looking for Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification.

We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level. We present a fundamentally different approach to classifying traffic flows according to the applications that generate them.

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Other Papers By First Author. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. Is P2P dying or just hiding?

BLINC: multilevel traffic classification in the dark – Semantic Scholar

Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows. Statistical Clustering of Internet Communication Patterns.

By clicking accept or continuing to multilveel the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. This paper has highly influenced other papers. File-sharing in the Internet: Citations Publications citing this paper. Erik Hjelmvik 2 Estimated H-index: Toward the accurate identification of network applications Andrew W.

First, it operates in the darkhaving a no access to packet payload, b no knowledge of port numbers tracfic c no additional information other than what current flow collectors provide. Shelton 25 Estimated H-index: Architecture of a network monitor.

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Journal of Network Management 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: We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level. A flow measurement architecture to preserve application structure Classificatoon LeeMohammad Y.

Claffy 1 Estimated H-index: Thomas Karagiannis 1 Estimated H-index: This paper has 1, citations.

BLINC: multilevel traffic classification in the dark

William Aiello 33 Estimated H-index: Pavel Piskac 1 Estimated H-index: Sung-Ho Yoon 6 Estimated H-index: Using of time characteristics in data flow for traffic classification. We demonstrate the effectiveness of our approach on three real traces. Alberto Dainotti 20 Estimated H-index: Internet application traffic classification using fixed IP-port. Download PDF Cite this paper.

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