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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From This Paper Topics from this paper. KleinbergDoug J.
Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows. Analysis of communities of interest classificaation data networks.
BLINC: multilevel traffic classification in the dark
Sung-Ho Yoon 6 Estimated H-index: Alberto B,inc 20 Estimated H-index: Architecture of a network monitor. Download PDF Cite this paper. We present a fundamentally different approach to classifying traffic flows according to the applications that generate them.
These restrictions respect privacy, technological and practical constraints. Erik Hjelmvik 2 Estimated H-index: Network packet Tracing software. We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level.
BLINC: multilevel traffic classification in the dark – Semantic Scholar
Shelton 25 Estimated H-index: Tygar Lecture Notes in Computer Science We demonstrate the effectiveness of our approach on three real traces. In contrast to previous methods, our approach is based on observing and identifying classificaiton of host behavior at the transport layer. This paper has highly influenced other papers. Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification.
Skip to search tge Skip to main content. Semantic Scholar estimates that this publication has 1, citations based on the available data. Traffic Mining in IP Tunnels. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper.
We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level. Cited 3 Source Add To Collection. Journal of Network Management Other Papers By First Author.
Internet traffic classification using bayesian analysis techniques. Pieter Burghouwt 3 Estimated H-index: A flow measurement architecture to preserve application structure Myungjin LeeMohammad Y. Is P2P dying or just hiding?
Citations Publications citing this paper. Toward the accurate identification of network applications.
See our FAQ for additional information. 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. Using of time characteristics in data flow for traffic classification.
Gang Xiong 4 Estimated H-index: File-sharing in the Internet: Hall University of Waikato. Citation Statistics 1, Citations 0 50 ’07 ’10 ’13 ‘ A parameterizable methodology for Internet traffic flow profiling.
Moore 24 Estimated H-index: A continuous time bayesian network approach for intrusion detection. Supporting the visualization and forensic analysis of network events. Topics Discussed in This Paper.