The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Traffic classification using clustering algorithms
Abstract
Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult with many peer-to-peer (P2P) applications using dynamic port numbers, masquerading techniques, and encryption to avoid detection. An alternative approach is to classify traffic by exploiting the distinctive characteristics of applications when they communicate on a network. We pursue this latter approach and demonstrate how cluster analysis can be used to effectively identify groups of traffic that are similar using only transport layer statistics. Our work considers two unsupervised clustering algorithms, namely K-Means and DBSCAN, that have previously not been used for network traffic classification. We evaluate these two algorithms and compare them to the previously used AutoClass algorithm, using empirical Internet traces. The experimental results show that both K-Means and DBSCAN work very well and much more quickly then AutoClass. Our results indicate that although DBSCAN has lower accuracy compared to K-Means and AutoClass, DBSCAN produces better clusters.
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Cites
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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
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
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Internet traffic classification demystified: myths, caveats, and the best practices
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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., Arlitt, M., & Mahanti, A.
- Venue
- Proceedings of the 2006 SIGCOMM workshop on Mining network data
- Publisher
- ACM
- Published
- 2006-09-11
- Pages
- 281-286
- OpenAlex
- W2121973001
- Cited by
- 708
- References
- 21
- FWCI
- 24.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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