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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Sung-Ho Yoon myltilevel Estimated H-index: Toward the accurate identification of network applications. Transport layer Traffic flow Computer network Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification.
Cited 3 Source Add To Collection. Rao Computer Networks Hall University of Waikato. Internet traffic classification using bayesian analysis techniques. This paper has 1, citations. We present a fundamentally different approach to classifying traffic flows according to the applications that generate them.
Network packet Tracing software.
Using of time characteristics in data flow for traffic classification. Claffy 1 Estimated H-index: We demonstrate the effectiveness of our approach on three real traces.
Erik Hjelmvik 2 Estimated H-index: Citation Statistics 1, Citations 0 50 ’07 ’10 ’13 ‘ We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the application level. Alberto Dainotti 20 Estimated H-index: Moore 24 Estimated H-index: Statistical Clustering of Internet Communication Patterns.
William Aiello 33 Estimated H-index: In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. Internet application traffic classification using fixed IP-port.
Other Papers By First Author. This paper has highly influenced other papers. Gang Xiong 4 Estimated H-index: Download PDF Cite this paper. Citations Classfiication citing this paper. Daniele Piccitto 1 Estimated H-index: Andrea Baiocchi 17 Estimated H-index: These restrictions respect privacy, technological and practical constraints.
Topics Discussed in This Paper. Toward the accurate identification of network applications Andrew W. Showing of extracted citations. Thomas Karagiannis 1 Estimated H-index: Analysis of communities of interest in data networks.
BLINC: multilevel traffic classification in the dark
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BLINC: multilevel traffic classification in the dark – Semantic Scholar
Tygar Lecture Notes in Computer Science Christian Dewes 2 Estimated H-index: Thomas Karagiannis 32 Estimated H-index: A continuous time bayesian network approach for intrusion detection. A parameterizable methodology for Internet traffic flow profiling. Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows.
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.
Pieter Burghouwt 3 Estimated H-index: Traffic Mining in IP Tunnels. Shelton 25 Estimated H-index: This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. Is P2P dying or just hiding? Pavel Piskac 1 Estimated H-index: