Multilayer Feedforward Neural Network for Internet Traffic Classification

TitleMultilayer Feedforward Neural Network for Internet Traffic Classification
Publication TypeJournal Article
Year of Publication2020
AuthorsManju, N., B. S. Harish, and N. Nagadarshan
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
IssueSpecial Issue on Soft Computing
Date Published03/2020

Recently, 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).

KeywordsClassification, Feature Transformation, Internet Traffic, Neural Network
IJIMAI20206_1_13.pdf356.04 KB