The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Internet traffic classification using bayesian analysis techniques
Abstract
Accurate traffic classification is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service to providing operators with useful forecasts for long-term provisioning. We apply a Naïve Bayes estimator to categorize traffic by application. Uniquely, our work capitalizes on hand-classified network data, using it as input to a supervised Naïve Bayes estimator. In this paper we illustrate the high level of accuracy achievable with the Naïve Bayes estimator. We further illustrate the improved accuracy of refined variants of this estimator. Our results indicate that with the simplest of Naïve Bayes estimator we are able to achieve about 65 % accuracy on per-flow classification and with two powerful refinements we can improve this value to better than 95%; this is a vast improvement over traditional techniques that achieve 50–70%. While our technique uses training data, with categories derived from packet-content, all of our training and testing was done using header-derived discriminators. We emphasize this as a powerful aspect of our approach: using samples of well-known traffic to allow the categorization of traffic using commonly-available information alone.
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Citations
Cites
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Paxson, V., & Floyd, S. (1995) · IEEE/ACM Transactions on Networking · doi:10.1109/90.392383 · Cited by 3,708
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Toward the Accurate Identification of Network Applications
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Empirically derived analytic models of wide-area TCP connections
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Cited by
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
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Traffic classification on the fly
Bernaille, L., Teixeira, R., Akodkenou, I., Soule, A., & Salamatian, K. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1129582.1129589 · Cited by 585
Bayesian Neural Networks for Internet Traffic Classification
Auld, T., Moore, A. W., & Gull, S. F. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883010 · Cited by 494
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Metadata
- Authors
- Moore, A. W., & Zuev, D.
- Venue
- Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
- Publisher
- ACM
- Published
- 2005-06-06
- Pages
- 50-60
- OpenAlex
- W2012095206
- Cited by
- 1,208
- References
- 24
- FWCI
- 41.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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