TY - CPAPER
AU - Pawel Foremski
AU - C. Callegari
AU - M. Pagano
AU - Piotr Gaj
AB - The Internet transports data generated by programs whch cause various phenomena in IP flows. By means of machine learning techniques, we can automatically discern between flows generated by different traffic sources and gain a more informed view of the Internet. In this paper, we optimize Waterfall, a promising architecture for cascade traffic classification. We present a new heuristic approach to optimal design of cascade classifiers. On the example of Waterfall, we show how to determine the order of modules in a cascade so that the classification speed in maximized, while keeping the number of errors and unlabeled flows at minimum. We validate our method experimentally on 4 real traffic datasets, showing significant improvements over random cascades.
BT - Computer Networks
LA - eng
N1 - 22nd International Conference, CN 2015 Brunów, Poland, June 16-19, 2015
N2 - The Internet transports data generated by programs whch cause various phenomena in IP flows. By means of machine learning techniques, we can automatically discern between flows generated by different traffic sources and gain a more informed view of the Internet. In this paper, we optimize Waterfall, a promising architecture for cascade traffic classification. We present a new heuristic approach to optimal design of cascade classifiers. On the example of Waterfall, we show how to determine the order of modules in a cascade so that the classification speed in maximized, while keeping the number of errors and unlabeled flows at minimum. We validate our method experimentally on 4 real traffic datasets, showing significant improvements over random cascades.
PB - Springer International Publishing Switzerland
PY - 2015
EP - 1–10
T2 - Computer Networks
TI - Waterfall traffic identification: optimizing classification cascades
ER -