Network traffic clustering using random forest proximities

Wang, Yu, Xiang, Yang and Zhang, Jun 2013, Network traffic clustering using random forest proximities, in ICC 2013 : IEEE International Conference on Communications, IEEE, Piscataway, N.J., pp. 2058-2062, doi: 10.1109/ICC.2013.6654829.

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Title Network traffic clustering using random forest proximities
Author(s) Wang, Yu
Xiang, YangORCID iD for Xiang, Yang
Zhang, JunORCID iD for Zhang, Jun
Conference name IEEE International Conference on Communications (2013 : Budapest, Hungary)
Conference location Budapest, Hungary
Conference dates 9-13 Jun. 2013
Title of proceedings ICC 2013 : IEEE International Conference on Communications
Editor(s) Kim, Dong-In
Mueller, Peter
Publication date 2013
Conference series IEEE International Conference on Communications
Start page 2058
End page 2062
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Clustering
Machine Learning
Traffic Analysis
Summary The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for the task, which performs clustering based on Random Forest (RF) proximities instead of Euclidean distances. The approach consists of two steps. In the first step, we derive a proximity measure for each pair of data points by performing a RF classification on the original data and a set of synthetic data. In the next step, we perform a K-Medoids clustering to partition the data points into K groups based on the proximity matrix. Evaluations have been conducted on real-world Internet traffic traces and the experimental results indicate that the proposed approach is more accurate than the previous methods.
ISBN 9781467331227
Language eng
DOI 10.1109/ICC.2013.6654829
Field of Research 080503 Networking and Communications
080501 Distributed and Grid Systems
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
HERDC collection year 2013
Copyright notice ©2013, IEEE
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Document type: Conference Paper
Collections: School of Information Technology
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