Title :
Machine learning based IP traffic classfication
Author :
Taysi, Z.C. ; Karsligil, M.E. ; Yavuz, A.G. ; Sahin, Ramazan ; Yilmaz, Tuba ; Demirel, Hasan
Author_Institution :
Akilli Sistemler Laboratuari, Yildiz Teknik Univ., Istanbul, Turkey
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 :
IP networks; Internet telephony; learning (artificial intelligence); peer-to-peer computing; quality of service; Internet service providers; P2P; VoIP; bandwidth utilization; classify protocols; instant messaging; machine learning based IP traffic classification; peer-to-peer; quality of service; random forest classifier; service package; social networks; video; voice-over-IP; IP networks; Instant messaging; Ports (Computers); Postal services; Protocols; Support vector machines; Classification; IP Flow; IP packet; Internet traffic; Peer-to-Peer; Random Forest; SVM; k-NN;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2013 21st
Conference_Location :
Haspolat
Print_ISBN :
978-1-4673-5562-9
Electronic_ISBN :
978-1-4673-5561-2
DOI :
10.1109/SIU.2013.6531459
