An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
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 through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Revealing skype traffic: when randomness plays with you
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
A Measurement Study of a Large-Scale P2P IPTV System
IEEE Transactions on Multimedia
MINETRAC: mining flows for unsupervised analysis & semi-supervised classification
Proceedings of the 23rd International Teletraffic Congress
Kiss to abacus: a comparison of P2P-TV traffic classifiers
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
Reviewing traffic classification
DataTraffic Monitoring and Analysis
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We present a novel methodology to accurately classify the traffic generated by P2P-TV applications, relying only on the count of packets they exchange with other peers during small time-windows. The rationale is that even a raw count of exchanged packets conveys a wealth of useful information concerning several implementation aspects of a P2P-TV application --- such as network discovery and signaling activities, video content distribution and chunk size, etc. By validating our framework, which makes use of Support Vector Machines, on a large set of P2P-TV testbed traces, we show that it is actually possible to reliably discriminate among different applications by simply counting packets.