Detection and Mitigation Framework of Peer-to-Peer Traffic in Campus Networks


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Abstract


Bandwidth-intensive applications, such as Peer-to-Peer applications, have changed the common properties of internet data. These have consumed most of the Internet bandwidth with balancing traffic in both directions. That affects the performance of traditional Internet applications. Therefore, the management of traffic plays an important role in increasing the performance of the network. This paper proposes a framework to detect and mitigate Peer-to-Peer traffic. This framework can be mainly used in situations where the existing framework is not efficient or cannot be used. The proposed framework is based on Snort and Support Vector Machine. Evaluation has been carried out through experiments on the traffic traces downloaded from different shared resources and captured from the campus network. Effectiveness of the current Peer-to-Peer detection methods is measured using controlled data sets, and a comparison using consensus methods is presented.
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Keywords


P2P Traffic; Traffic Classification; Snort; Features Selection; ML

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References


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