Network traffic clustering using random forest proximities
Contribuinte(s) |
Kim, Dong-In Mueller, Peter |
---|---|
Data(s) |
01/01/2013
|
Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30060786/evid-iccconf-2013.pdf http://dro.deakin.edu.au/eserv/DU:30060786/evid-networktrafficpeerreview-2013.pdf http://dro.deakin.edu.au/eserv/DU:30060786/wang-networktraffic-2013.pdf http://doi.org/10.1109/ICC.2013.6654829 |
Direitos |
2013, IEEE |
Palavras-Chave | #Clustering #Machine Learning #Traffic Analysis |
Tipo |
Conference Paper |