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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Citations
Cites
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Bro: a system for detecting network intruders in real-time
Paxson, V. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00112-7 · Cited by 2,636
Data mining introductory and advanced topics
Dunham, M. H. (2003) · Cited by 1,447
Cluster analysis for gene expression data: a survey
Jiang, D., Tang, C., & Zhang, A. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.68 · Cited by 1,238
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. V. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064220 · Cited by 1,216
Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080119 · Cited by 997
Accurate, scalable in-network identification of p2p traffic using application signatures
Sen, S. B., Spatscheck, O., & Wang, D. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988742 · Cited by 811
Toward the Accurate Identification of Network Applications
Moore, A. W., & Papagiannaki, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31966-5_4 · Cited by 801
Traffic classification using clustering algorithms
Erman, J., Arlitt, M. F., & Mahanti, A. (2006) · doi:10.1145/1162678.1162679 · Cited by 712
Self-citation
Research works printed but not linked: 1
- Cache Logic . Peer-to-Peer in 2005 , http://www.cachelogic.com/home/pages/research/, 2005. Cache Logic. Peer-to-Peer in 2005, http://www.cachelogic.com/home/pages/research/, 2005.
Cited by
Sorted by citations
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
FS-Net: A Flow Sequence Network For Encrypted Traffic Classification
Liu, C., He, L., Xiong, G., Cao, Z., & Li, Z. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737507 · Cited by 630
Internet traffic classification demystified: myths, caveats, and the best practices
Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., & Lee, K. (2008) · Proceedings of the 2008 ACM CoNEXT Conference on - CONEXT '08 · doi:10.1145/1544012.1544023 · Cited by 485
Robust Network Traffic Classification
Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2014.2320577 · Cited by 391
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 ›
Are you an author, or do you see a problem with this page? Request a review
