Internet Traffic Classification for Scalable QOS Provision
Abstract
A new scheme that classifies the Internet traffic according to their application types for scalable QoS provision is proposed in this work. The traditional port-based classification method does not yield satisfactory performance, since the same port can be shared by multiple applications. Furthermore, asymmetric routing and errors of modern measurement tools such as PCF and NetFlow degrades the classification performance. To address these issues, the proposed classification process consists of two steps: feature selection and classification. Candidate features that can be obtained easily by ISP are examined. Then, we perform feature reduction so as to balance the performance and complexity. As to classification, the REPTree and the bagging schemes are adopted and compared. It is demonstrated by simulations with real data that the proposed classification scheme outperforms existing techniques
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It is not always possible to collect bidirectional flows due to routing asymmetries.
“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 [347]
REPTree achieves over 90% accuracy in classification of unidirectional flows and plateaus at seven features.
“REPTree achieves over 90% accuracy in classification of unidirectional flows and plateaus at seven features.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [347]
DT performs better than NBKE with respect to execution time and space in memory.
“However, DT performs better than NBKE, with respect to execution time and space in memory [347].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [347]
Flow feature-based traffic classification has the potential to overcome numerous limitations of other techniques, such as unregistered port numbers, encrypted packet payload, routing asymmetries, and high storage and computational overhead.
“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 [347]
DT suffers from overfitting with noisy data, which deteriorates performance.
“Unfortunately, DT suffers from overfitting with noisy data, which deteriorates performance [347].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [347]
NBKE incurs increased inference time, especially for classifying unknown flows.
“However, it incurs increased inference time, especially for classifying unknown flows [347].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [347]
NBKE classification accuracy deteriorates dramatically with increasing number of features.
“This is in contrast to NBKE, where the classification accuracy deteriorates dramatically with increasing number of features.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [347]
Bulk data transfer applications exhibit higher burst than streaming applications.
“Evidently, bulk data transfer applications exhibit higher burst than streaming applications.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [347]
Cites
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 · see citing works
Roughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494 · see citing works
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064220 · Cited by 460 · see citing works
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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 · see citing works
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 · see citing works
- Authors
- Park, J., Tyan, H.-R., & Kuo, C.-C.
- Affiliations
- University of Southern California (US)
- Chung Yuan Christian University (TW)
- ORCID
- Kuo, C.-c.
- Venue
- 2006 IEEE International Conference on Multimedia and Expo
- Publisher
- IEEE
- Published
- 2006-07
- Pages
- 1221-1224
- OpenAlex
- W2135445399
- Cited by
- 56
- References
- 3
- Citations by year
- 2022: 4 · 2021: 4 · 2020: 6 · 2019: 2 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 2 · 2014: 4 · 2013: 5 · 2012: 4
- FWCI
- 1.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia and Expo (ICME) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- internet traffic classification, application-level traffic classification, quality of service provisioning, asymmetric routing, NetFlow, feature selection, feature reduction, REPTree, bagging
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