Network Traffic Classification Using Supervised Machine Learning Algorithms in Systems with NFV Architecture

Author:

Ilievski Gjorgji,Latkoski Pero

Publisher

Springer International Publishing

Reference32 articles.

1. Chiosi, M., et al.: Network Functions Virtualisation. Introductory White Paper. Cited 2019-10-10 Available at: https://portal.etsi.org/nfv/nfv_white_paper.pdf (2015)

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3. Frank, E., Hall, M.A., Witten, I.H.: Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 4th edn. San Francisco, CA, USA (2016). ISBN: 0128042915 9780128042915

4. Vergara-Reyes, J., Martinez-Ordonez, M.C., Ordonezy, A., Rendon O.M.C.: IP traffic classification in NFV: a benchmarking of supervised machine learning algorithms. In: IEEE Colombian Conference on Communications and Computing (2017). https://doi.org/10.1109/ColComCon.2017.8088199

5. Alshammari, R., Nur Zincir-Heywood, A.: Identification of VoIP encrypted traffic using a machine learning approach. J. King Saud Univ. Comput. Inf. Sci. Arch. 27(1), 77–92 (2015). https://doi.org/10.1016/j.jksuci.2014.03.013

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