Machine learning based IP traffic classfication

Nowadays several topics such as improving the quality of service, bandwidth utilization, and creation of different service packages, have gained importance due to widespread use of Internet. It is crucial to identify and classify protocols and applications communicating through the network in order...

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Bibliographic Details
Main Authors: Taysi, Z. C., Karsligil, M. E., Yavuz, A. G., Sahin, R., Yilmaz, T., Demirel, H.
Format: Conference Proceeding
Language:eng ; tur
Subjects:
Classification
Instant messaging
Internet traffic
IP Flow
IP networks
IP packet
k-NN
Peer-to-Peer
Ports (Computers)
Postal services
Protocols
Random Forest
Support vector machines
SVM
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Summary:Nowadays several topics such as improving the quality of service, bandwidth utilization, and creation of different service packages, have gained importance due to widespread use of Internet. It is crucial to identify and classify protocols and applications communicating through the network in order to perform these tasks. There are three types of systems to classify protocols and applications communicating through the network, namely, port-based, payload-based and machine learning based. In this work, we focused on Instant Messaging (IM), Peer-to-peer (P2P), Social Networks, Video and Voice-over-IP (VoIP) classes which have higher importance for the Internet Service Providers. We evaluated the performance of our system with several classifiers. Random Forest classifier has had the highest success rate among others.
DOI:10.1109/SIU.2013.6531459