Real-Time traffic classification based on cosine similarity using sub-application vectors

Authors:
Cihangir Beşiktaş;Hacı Ali Mantar
Affiliations:
Department of Computer Engineering, Gebze Institute of Technology, Turkey and Center of Research for Advanced Technologies of Informatics and Information Security, TUBITAK BILGEM, Turkey;Department of Computer Engineering, Gebze Institute of Technology, Turkey
Venue:
TMA'12 Proceedings of the 4th international conference on Traffic Monitoring and Analysis
Year:
2012

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Abstract

Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy.