C4.5: programs for machine learning
C4.5: programs for machine learning
New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
A Machine Learning Approach for Efficient Traffic Classification
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Semantic networking: Flow-based, traffic-aware, and self-managed networking
Bell Labs Technical Journal - Core and Wireless Networks
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
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The Semantic Networking concept has been introduced to solve the QoS, scalability and complexity challenges for the Future of Internet. Based on traffic awareness and considering flow entities, it contributes to an adaptive management of the network and provides better knowledge of the transported traffic. Studying the processing time of the classification compatible with real-time operation of such networks is a key question for implementation purposes. In this paper, we present interesting techniques for classification of traffic in semantic networks. The Sample & Hold and multi-stage filter schemes are studied to detect the biggest flows. Their performance is evaluated on real traffic traces. In addition the classification of traffic according to the originating application is investigated. In particular, we analyze the influence of many parameters derived from a traffic flow on the performance of application identification and classify them according to their accuracy. By doing this, a light scheme is proposed able to classify accurately the traffic. We finally discuss the architecture of an hardware implementation to validate the concept of semantic networking.