A Survey on Classification of Traffic using Clustering Algorithms
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Abstract
The traffic classification is essential for network management and it has become more challenging in the last couple of years. The research community has explored, developed and proposed several classification approaches. The continued increase of different Internet application behaviors covers up some applications to avoid filtering or blocking are among the reasons the traffic classification remains a challenge in Internet research. This survey paper looks at emerging research on both supervised and unsupervised clustering to assist in the classification process. In this article we review recent laurels and discuss various research trends in Clustering algorithms. We outline the obstinately mysterious challenges in the field over the last decade and suggest strategies for tackling these challenges to promote headway in the art of Internet traffic classification.
Keywords: Clustering approaches, Machine learning approaches, Traffic Classification
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