BLINC MULTILEVEL TRAFFIC CLASSIFICATION IN THE DARK PDF

Migore Internet application traffic classification using fixed IP-port. From This Paper Topics from this paper. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. See our FAQ for additional information. A parameterizable methodology for Internet traffic flow profiling.

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Migore Internet application traffic classification using fixed IP-port. From This Paper Topics from this paper. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper.

See our FAQ for additional information. A parameterizable methodology for Internet traffic flow profiling. Is P2P dying or just hiding? Christian Dewes 2 Estimated H-index: Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows.

Toward the accurate identification of network applications Andrew W. Daniele Piccitto 1 Estimated H-index: Internet traffic classification using bayesian analysis techniques. This paper has highly influenced other papers. Terry Winograd 61 Estimated H-index: This paper has 1, citations. Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification. Pavel Piskac 1 Estimated H-index: Are you looking for We analyze these patterns at three levels thr increasing detail i the social, ii the functional and iii the application level.

Erik Hjelmvik 2 Estimated H-index: Supporting the visualization and classirication analysis of network events. Other Papers By First Author. Architecture of a network monitor. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. First, it operates in the darkhaving a no access to packet payload, b no knowledge clasification port numbers and c no additional information other than what current flow collectors provide.

Statistical Clustering of Internet Communication Patterns. Showing of extracted citations. Furthermore, our approach has two important features. Cited 3 Source Add To Collection. Claffy 1 Estimated H-index: Related Articles

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Traffic classification: Issues and challenges

Download BibTex We present a fundamentally different approach to classifying traffic flows according to the applications that generate them. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, it operates in the dark, having a no access to packet payload, b no knowledge of port numbers and c no additional information other than what current flow collectors provide.

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BLINC MULTILEVEL TRAFFIC CLASSIFICATION IN THE DARK PDF

Downloading and watching video content on mobile devices is currently a growing trend among users, that is causing a demand for higher bandwidth and better provisioning throughout the network infrastructure. At the same time, popular demand for privacy has led many online streaming services to adopt end-to-end encryption, leaving providers with only a handful of indicators for identifying QoE issues. In order to address these challenges, we propose a novel methodology for detecting video streaming QoE issues from encrypted traffic. We develop predictive models for detecting different levels of QoE degradation that is caused by three key influence factors, i. The information they collect is ostensibly used for customization and targeted advertising.

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BLINC: multilevel traffic classification in the dark

We present a fundamentally different approach to classifying traffic flows according to the applications that generate them. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, it operates in the dark, having a no access to packet payload, b no knowledge of port numbers and c no additional information other than what current flow collectors provide. These restrictions respect privacy, technological and practical constraints.

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