01533nas a2200229   4500000000100000000000100001008004100002260001200043653001900055653001900074653002700093653002100120100001500141700001800156700001200174245007800186856010000264300001200364490000600376520090700382022001401289       2020                            d  c03/202010aClassification10aNeural Network10aFeature Transformation10aInternet Traffic1 aB S Harish1 aN Nagadarshan1 aN Manju00aMultilayer Feedforward Neural Network for Internet Traffic Classification  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/11/ijimai20206_1_13_pdf_16647.pdf  a117-1220 v63 aRecently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard).  a1989-1660