Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Internet traffic classification using energy time-frequency distributions
AU - Marnerides, Angelos
AU - Pezaros, Dimitrios
AU - Kim, Hyun-chul
AU - Hutchison, David
PY - 2013/6/9
Y1 - 2013/6/9
N2 - We present a fundamentally new approach to classify application flows based on the mapping of aggregate transport-layer volume information onto the Time-Frequency (TF) plane. We initially show that the volume persona (i.e. counts of packets and bytes) of traffic flows at the transport layer exhibits highly non-stationary characteristics, hence rendering many typical classification methods inapplicable. By virtue of this constraint, we present a novel application classification method based on the Cohen energy TF distributions for such highly non-stationary signals. We have used the Rényi information to measure the distinct complexity of any given application signal, and to subsequently construct a robust training model for every application protocol within our scheme. The effectiveness of our approach is demonstrated using real backbone and edge link network traces captured in US and Japan. Our results show that for the majority of applications, aggregate volume-based classification can reach up to 96% accuracy, while considering significantly less features in comparison with existing approaches.
AB - We present a fundamentally new approach to classify application flows based on the mapping of aggregate transport-layer volume information onto the Time-Frequency (TF) plane. We initially show that the volume persona (i.e. counts of packets and bytes) of traffic flows at the transport layer exhibits highly non-stationary characteristics, hence rendering many typical classification methods inapplicable. By virtue of this constraint, we present a novel application classification method based on the Cohen energy TF distributions for such highly non-stationary signals. We have used the Rényi information to measure the distinct complexity of any given application signal, and to subsequently construct a robust training model for every application protocol within our scheme. The effectiveness of our approach is demonstrated using real backbone and edge link network traces captured in US and Japan. Our results show that for the majority of applications, aggregate volume-based classification can reach up to 96% accuracy, while considering significantly less features in comparison with existing approaches.
U2 - 10.1109/ICC.2013.6654911
DO - 10.1109/ICC.2013.6654911
M3 - Conference contribution/Paper
SN - 9781467331203
SP - 2513
EP - 2518
BT - 2013 IEEE International Conference on Communications (ICC)
PB - IEEE
T2 - IEEE ICC
Y2 - 9 June 2013 through 13 June 2013
ER -