Journal Article

·2013

Machine learning based IP traffic classfication

Z. Cihan Taysi YTU , M. Elif Karslıgil YTU , A. Gökhan Yavuz YTU , R.C. Sahin , Tuğba Yılmaz , H. Demirel

Abstract

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.

Keywords

Voice over IP Computer science Traffic classification The Internet Quality of service Computer network Classifier (UML) Payload (computing) Peer-to-peer Port (circuit theory) Machine learning Bandwidth (computing) Multimedia Artificial intelligence World Wide Web Network packet

Subject Areas

Internet Traffic Analysis and Secure E-voting ·Artificial Intelligence ·Physical Sciences
Network Security and Intrusion Detection ·Computer Networks and Communications ·Physical Sciences
Network Packet Processing and Optimization ·Hardware and Architecture ·Physical Sciences