mmdump: a tool for monitoring internet multimedia traffic
ACM SIGCOMM Computer Communication Review
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Profiling internet backbone traffic: behavior models and applications
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Semi-supervised network traffic classification
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Offline/realtime traffic classification using semi-supervised learning
Performance Evaluation
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
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
Traffic classification using a statistical approach
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
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With the development of network and multimedia coding techniques, more and more Voice over Internet Protocol (VoIP) applications have emerged. The traffic identification on VoIP applications becomes an important issue in network management and traffic analysis. In this paper, a new traffic identification scheme, which combines traffic flow statistic analysis with host behavior estimation, is proposed to identify the VoIP traffic at the transport layer of the Internet. The host IP addresses and the port numbers are examined as the host behavior to distinguish the VoIP traffic from traditional traffic flows. The packet size has been modeled by a function of entropy while the inter-packet time has been modeled by the self-adaptive estimation. The experiment results show that our scheme could obtain a stable performance. At the same time, the proposed scheme could maintain its validity when existing VoIP applications are updated or the new ones admitted. Both accuracy and flexibility can be improved.