Modeling and performance analysis of BitTorrent-like peer-to-peer networks
Proceedings of the 2004 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
Performance evaluation for unsolicited grant service flows in 802.16 networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Quantifying Skype user satisfaction
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Identifying and discriminating between web and peer-to-peer traffic in the network core
Proceedings of the 16th international conference on World Wide Web
A markovian signature-based approach to IP traffic classification
Proceedings of the 3rd annual ACM workshop on Mining network data
Outbound SPIT filter with optimal performance guarantees
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Owing to the enormous growth of VoIP applications, an effective means of identifying VoIP is now essential for managing a number of network traffic issues, such as reserving bandwidth for VoIP traffic, assigning high priority for VoIP flows, or blocking VoIP calls to certain destinations. Because the protocols, port numbers, and codecs used by VoIP services are shifting toward proprietary, encrypted, and dynamic methods, traditional VoIP identification approaches, including port- and payload-based schemes, are now less effective. Developing a traffic identification scheme that can work for general VoIP flows is therefore of paramount importance. In this paper, we propose a VoIP flow identification scheme based on the unique interaction pattern of human conversations . Our scheme is particularly useful for two reasons: 1) flow detection relies on human conversations rather than packet timing; thus, it is resistant to network variability; and 2) detection is based on a short sequence of voice activities rather than the whole packet stream. Hence, the scheme can operate as a traffic management module to provide QoS guarantees or block VoIP calls in real time. The performance evaluation, which is based on extensive real-life traffic traces, shows that the proposed method achieves an identification accuracy of 95% in the first 4 seconds of the detection period and 97% in 11 seconds.