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International Journal of Computer Applications
Article . 2014 . Peer-reviewed
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HTTP Traffic Graph Clustering using Markov Clustering Algorithm

Authors: Theophilus Wellem; Yessica Nataliani;

HTTP Traffic Graph Clustering using Markov Clustering Algorithm

Abstract

Graph-based techniques and analysis have been used for IP network traffic analysis. The objective of this paper is to study the hosts’ interaction behavior and use graph clustering algorithm, the Markov clustering algorithm, to group (cluster) hosts which have interaction using the HTTP protocol. Using real network traces, the clustering results show that MCL algorithm successfully group the hosts to their corresponding clusters. Analyzing the clustering results, it is showed that communications between one source IP address to one destination IP address, one source IP address to several (different) destination IP addresses, and several (different) source IP addresses to one destination IP address, are grouped to their own clusters. General Terms Algorithms, Measurement

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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