BLINC MULTILEVEL TRAFFIC CLASSIFICATION IN THE DARK PDF

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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Internet application traffic classification using fixed IP-port. Using of time characteristics in data flow for traffic classification. Rao Computer Networks Gang Xiong 4 Estimated H-index: This paper has 1, citations. A parameterizable methodology for Internet traffic flow profiling.

BLINC: multilevel traffic classification in the dark

A continuous time bayesian network approach for intrusion detection. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. Citation Statistics 1, Citations 0 50 ’07 ’10 ’13 ‘ Pavel Piskac 1 Estimated H-index: Moore 24 Estimated H-index: 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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Network packet Tracing software. Shelton 25 Estimated H-index: Andrea Baiocchi 17 Estimated H-index: Internet traffic classification using bayesian analysis techniques. Are you looking for See our FAQ for additional information. These restrictions respect privacy, technological and practical constraints.

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Sung-Ho Yoon 6 Estimated H-index: Analysis of communities of interest in data networks. Journal of Network Management A flow measurement architecture to preserve application structure Myungjin LeeMohammad Y.

Download PDF Cite this paper. Toward the accurate identification of network applications Andrew W.

Tygar Lecture Notes in Computer Science Topics Discussed in Classificatlon Paper. Erik Hjelmvik 2 Estimated H-index: Daniele Piccitto 1 Estimated H-index: Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification.

William Aiello 33 Estimated H-index: Citations Publications citing this paper.

Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows. KleinbergDoug J.

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We present a fundamentally different approach to classifying traffic flows according to the applications that generate them. Ttraffic, our approach has two important features.

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