Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
A case for end system multicast (keynote address)
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Scalable application layer multicast
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
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
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Clustering and sharing incentives in BitTorrent systems
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Challenges, design and analysis of a large-scale p2p-vod system
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Unmasking the growing UDP traffic in a campus network
PAM'12 Proceedings of the 13th international conference on Passive and Active Measurement
A framework for monitoring and measuring a large-scale distributed system in real time
Proceedings of the 5th ACM workshop on HotPlanet
Reviewing traffic classification
DataTraffic Monitoring and Analysis
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Due to the significant increase of peer-to-peer (P2P) traffic in the past few years, more attentions are put on designing effective methodologies of monitoring and identifying P2P traffic. In this paper, we propose a novel approach to measure and discover the special characteristics of P2P applications, the periodic behaviors, from the packet traces. We call this the "periodic behavioral spectrum" (PBS) of P2P applications. This new finding, learning the characteristics of P2P traffic from a new angle, could enhance our understanding on P2P applications. To show the effectiveness of our approach, we not only provide justifications as to why P2P applications should have some inherent periodic behaviors, but also conduct hundreds of experiments of applying the approach on several popular P2P applications.