Transport layer identification of P2P traffic

By Thomas Karagiannis, Andre Broido, Michalis Faloutsos, K. Claffy. ACM/SIGCOMM Internet Measurement Conference, 2004

822 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/59cb3834846e9f748db6230a87629eb50c8cf6ca

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Citations in the corpus

Issues and future directions in traffic classification (2012, background): “Features include common flow properties (e.g., per-flow duration and volume, mean packet size) as well as more detailed properties, such as sizes and interpacket times of the first n packets of a flow, or entropy of byte distribution in packet headers or payload.”
A Survey on Internet Traffic Identification (2009, background): “It is already widely accepted that the use of well-known ports on its own to identify applications is obsolete [8][9][10][11].”
Internet traffic classification demystified: myths, caveats, and the best practices (2008, background): “More recently, increasingly popular applications such as those that support peer-to-peer (P2P) file sharing, hide their identity by assigning ports dynamically and/or using well-known ports of other applications, rendering port-based classification less reliable [24, 30, 37].”
Internet traffic classification using bayesian analysis techniques (2005, background): “[4] identify an additional application of traffic categorization; they note that the growth or decline of particular traffic types can have a direct impact on social and political deci-”
Traffic classification using clustering algorithms (2006, methodology): “Many recent studies confirm that port-based identification of network traffic is ineffective [8, 15].”
Bayesian Neural Networks for Internet Traffic Classification (2007, methodology): “The power of discrimination offered by certain features that are location-specific, like those derived from RTT, may change should the point of measurement be moved.”
Should internet service providers fear peer-assisted content distribution? (2005, background): “P2P traffic accounts for approximately a third of the traffic in each one of the three traces (identified by the methodology described in [11]), while 13%-15% of the total traffic (8%-11% of the total packets) in the link is due to BitTorrent flows.”
Rarest first and choke algorithms are enough (2006, methodology): “Our conclusions only hold in the context we explored, i.e., peer-to-peer .le replication in the Internet.”
Offline/realtime traffic classification using semi-supervised learning (2007, background): “seriously dented because of the evolution of applications that do not communicate on standardized ports [3,17,28].”
BLINC: multilevel traffic classification in the dark (2005, background): “Finally, the traces were captured several months apart from each other to minimize potential similarities in the oﬀered services and client interactions.”