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;