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A Longitudinal Study of P2P Traffic Classification

Madhukar, A., Williamson, C.·2006·14th IEEE International Symposium on Modeling, Analysis, and Simulation

Abstract

This paper focuses on network traffic measurement of Peer-to- Peer (P2P) applications on the Internet. P2P applications supposedly constitute a substantial proportion of today's Internet traffic. However, current P2P applications use several obfuscation techniques, including dynamic port numbers, port hopping, HTTP masquerading, chunked file transfers, and encrypted payloads. As P2P applications continue to evolve, robust and effective methods are needed for P2P traffic identification. The paper compares three methods to classify P2P applications: port-based classification, application-layer signatures, and transport-layer analysis. The study uses empirical network traces collected from the University of Calgary Internet connection for the past 2 years. The results show that port-based analysis is ineffective, being unable to identify 30%-70% of today's Internet traffic. Application signatures are accurate, but may not be possible for legal or technical reasons. The transport-layer method seems promising, providing a robust means to assess aggregate P2P traffic. The latter method suggests that 30%-70% of the campus Internet traffic for the past year was P2P.

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Metadata

Authors
Madhukar, A., & Williamson, C.
Venue
14th IEEE International Symposium on Modeling, Analysis, and Simulation
Publisher
IEEE
Published
2006-10-24
Pages
179-188
OpenAlex
W1495633614
Cited by
247
References
0
FWCI
8.64
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Longitudinal Study of P2P Traffic Classification. https://app.cochat.ai/papers/10.1109/mascots.2006.6
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Madhukar, A., & Williamson, C. (2006). A Longitudinal Study of P2P Traffic Classification. 14th IEEE International Symposium on Modeling, Analysis, and Simulation. https://doi.org/10.1109/mascots.2006.6

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.