URL: http://www.pamconf.org/2005/PDF/34310324.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:

• Genome Campus Datasets;
• traffic categories: BULK;DATABASE;INTERACTIVE;MAIL;SEVICES;WWW;P2P;ATTACK;GAMES;MULTIMEDIA;
• demonstrate an accuracy of better than 66% of flows and better than 83% for packets and bytes;