Multilayer Feedforward Neural Network for Internet Traffic Classification

Author
Keywords
Abstract
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).
Year of Publication
2020
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
6
Issue
Special Issue on Soft Computing
Number
1
Number of Pages
117-122
Date Published
03/2020
ISSN Number
1989-1660
Citation Key
URL
https://www.ijimai.org/journal/sites/default/files/files/2019/11/ijimai20206_1_13_pdf_16647.pdf
DOI
10.9781/ijimai.2019.11.002
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