The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Identifying and discriminating between web and peer-to-peer traffic in the network core
Abstract
Traffic classification is the ability to identify and categorize network traffic by application type. In this paper, we consider the problem of traffic classification in the network core.Classification at the core is challenging because only partial information about the flows and their contributors is available. We address this problem by developing a framework that can classify a flow using only unidirectional flow information. We evaluated this approach using recent packet traces that we collected and pre-classified to establish a "base truth". From our evaluation, we find that flow statistics for the server-to-client direction of a TCP connection provide greater classification accuracy than the flow statistics for the client-to-server direction. Because collection of the server-to-client flow statistics may not always be feasible, we developed and validated an algorithm that can estimate the missing statistics froma unidirectional packet trace.
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In the citing paper
“Also, it is cumbersome to manually maintain and adapt the signatures to the ever growing number of applications and their dynamics [138].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [138]
In the citing paper
“Furthermore, with the rise in security and privacy concerns, payload is often encrypted and its access is prohibited due to privacy laws.This makes it non-trivial to infer a signature for an application class using payload [54,138].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [138]
In the citing paper
“It has the potential to overcome numerous limitations of other techniques, such as unregistered port numbers, encrypted packet payload, routing asymmetries, high storage and computational overhead [55,138,176,347].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [138]
In the citing paper
“It is not always possible to collect bidirectional flows due to routing asymmetries [138,347].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [138]
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Metadata
- Authors
- Erman, J., Mahanti, A., Arlitt, M., & Williamson, C.
- Affiliations
- University of Calgary (CA)
- Venue
- Proceedings of the 16th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2007-05-08
- Pages
- 883-892
- OpenAlex
- W2120955848
- Cited by
- 213
- References
- 31
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 7 · 2022: 7 · 2021: 7 · 2020: 12 · 2019: 10 · 2018: 3 · 2017: 3 · 2016: 6 · 2015: 10 · 2014: 12 · 2013: 21 · 2012: 21
- FWCI
- 13.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- network traffic classification, website traffic, peer-to-peer traffic, core network, packet traces
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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