The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification
Abstract
The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular methods such as port number and payload-based identification exhibit a number of shortfalls. An alternative is to use machine learning (ML) techniques and identify network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions. The performance impact of feature set reduction, using Consistency-based and Correlation-based feature selection, is demonstrated on Naïve Bayes, C4.5, Bayesian Network and Naïve Bayes Tree algorithms. We then show that it is useful to differentiate algorithms based on computational performance rather than classification accuracy alone, as although classification accuracy between the algorithms is similar, computational performance can differ significantly.
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Cites
(1998) · Cited by 3,476
Estimating Continuous Distributions in Bayesian Classifiers
John, G. H., & Langley, P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4964 · Cited by 2,885
Proceedings of the 20th conference on Uncertainty in artificial intelligence
Meek, C., Chickering, M., & Halpern, J. Y. (2004) · Cited by 1,574
Scaling Up the Accuracy of Naive-Bayes Classifiers : a Decision-Tree Hybrid
Kohavi, R. (1996) · Cited by 1,414
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. (2005) · Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems · doi:10.1145/1064212.1064220 · Cited by 1,208
Lim, T.-S., Loh, W.-Y., & Shih, Y.-S. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,133
BLINC: multilevel traffic classification in the dark
Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications · doi:10.1145/1080091.1080119 · Cited by 997
Consistency-based search in feature selection
Dash, M., & Liu, H. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(03)00079-1 · Cited by 889
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
Flow Clustering Using Machine Learning Techniques
McGregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24668-8_21 · Cited by 521
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
Self-citationBoutaba, 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
Issues and future directions in traffic classification
Dainotti, A., Pescape, A., & Claffy, K. (2012) · IEEE Network · doi:10.1109/mnet.2012.6135854 · 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
Metadata
- Authors
- Williams, N., Zander, S., & Armitage, G.
- Venue
- ACM SIGCOMM Computer Communication Review
- Published
- 2006-10-10
- Volume
- 36
- Issue
- 5
- Pages
- 5-16
- ISSN
- 0146-4833 (ISSN-L)
- OpenAlex
- W2073089243
- Cited by
- 698
- References
- 14
- FWCI
- 33.30
- Type
- journal article
- Language
- en
- Open access
- closed
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