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Bibliography Details

D. Antoniades, M. Polychronakis, S. Antonatos, E. Markatos, and S. Ubik, "Appmon: An Application for Accurate per Application Network Traffic Characterization", in BroadBand Europe 2006, Dec 2006.

Appmon: An Application for Accurate per Application Network Traffic Characterization
Authors:	D. Antoniades M. Polychronakis S. Antonatos E. Markatos S. Ubik
Published:	BroadBand Europe, 2006
URL:	http://www.ist-lobster.org/publications/papers/antoniades-appmon.pdf
Entry Dates:	2009-02-09
Abstract:	Accurate traffic classification is the keystone of numerous network activities. Our work capitalises on hand-classified network data, used as input to a supervised Bayes estimator. We illustrate the high level of accuracy achieved with a supervised Naive Bayes estimator; with the simplest estimator we are able to achieve better than 83% accuracy on both a per-byte and a per-packet basis
Results:	an open source application; a passive monitoring application for per application network traffic classification. Appmon uses deep packet inspection to accurately attribute traffic flows to the applications that generate them, and reports in real time the network traffic breakdown through a Web-based GUI;