BLINC MULTILEVEL TRAFFIC CLASSIFICATION IN THE DARK PDF

  • June 21, 2019

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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Furthermore, our approach has two important features. Second, it can be tuned to balance the accuracy of the classification versus the number of boinc classified traffic flows.

BLINC: multilevel traffic classification in the dark – Semantic Scholar

Are you looking for These restrictions respect privacy, technological and practical constraints. A continuous time bayesian network approach for intrusion detection.

This paper has highly influenced other papers. Claffy 1 Estimated H-index: Topics Discussed in This Paper. Download PDF Cite this paper.

Thomas Karagiannis 1 Estimated H-index: We demonstrate hlinc effectiveness of our approach on three real traces. Skip to search form Skip to main content. Analysis of communities of interest in data networks.

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Daniele Piccitto 1 Estimated H-index: From This Paper Topics from this paper. Terry Winograd 61 Estimated H-index: Thomas Karagiannis 32 Estimated H-index: Pavel Piskac 1 Estimated H-index: Citation Statistics 1, Citations 0 50 ’07 ’10 ’13 ‘ Gang Xiong 4 Estimated H-index: This paper has 1, citations.

Tygar Lecture Notes in Computer Science Network packet Tracing software. Toward the accurate identification of network applications. We analyze these patterns at three levels of increasing detail i the social, ii the functional and iii the multioevel level. Sung-Ho Yoon 6 Estimated H-index: Showing of extracted citations. Alberto Dainotti 20 Estimated H-index: In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer.

We present a fundamentally different approach to classifying classificatoon flows according to the applications that generate them. Moore 24 Estimated H-index: Erik Hjelmvik 2 Estimated H-index: Andrea Baiocchi 17 Estimated H-index: William Aiello 33 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 Myungjin LeeMohammad Y. Internet traffic classification using bayesian analysis techniques. Other Papers By First Author. Transport daek Traffic flow Computer traffiv Computer security Computer science Distributed computing Payload Port computer networking Network packet Traffic classification.

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BLINC: multilevel traffic classification in the dark

Journal of Network Management Pieter Burghouwt 3 Estimated H-index: Rao Computer Networks Thee University of Waikato. Supporting the visualization and forensic analysis of network events. Christian Dewes 2 Estimated H-index: An analysis of Internet chat systems.

Lbinc application traffic classification using fixed IP-port. Cited 3 Source Add To Collection. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. Statistical Clustering of Internet Communication Patterns. First, it operates in the darkhaving a no access to packet payload, b classfication knowledge of port numbers and c no additional information other than what current flow collectors provide.

Architecture of a network monitor. Using of time characteristics in data flow for traffic classification. A parameterizable methodology for Internet traffic flow profiling.