Abstract
In this paper, we present an architectural framework to perform Internet traffic classification in Satellite Communications for QoS management. Such framework is based on Machine Learning techniques. We propose the elements that the framework should include, as well as an implementation proposal. We define and validate some of its elements by evaluating an Internet dataset generated on an emulated Satellite Architecture. We also outline some discussions and future works that should be addressed in order to have an accurate Internet classification system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bertaux, L., et al.: Software defined networking and virtualization for broadband satellite networks. IEEE Commun. Mag. 53(3), 54–60 (2015)
Deri, L., Martinelli, M., Bujlow, T., Cardigliano, A.: nDPI: Open-source high-speed deep packet inspection. In: 2014 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 617–622 (2014)
Ferrús, R., Koumaras, H., et al.: Sdn/nfv-enabled satellite communications networks: opportunities, scenarios and challenges. Phys. Commun. 18, 95–112 (2016). special Issue on Radio Access Network Architectures and Resource Management for 5G
Garcia, J., Korhonen, T., Andersson, R., Västlund, F.: Towards video flow classification at a million encrypted flows per second. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp. 358–365, May 2018
ITU-T: End-user multimedia qos categories. Technical report, TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (2001)
Jin, Y., Duffield, N., Erman, J., Haffner, P., Sen, S., Zhang, Z.L.: A modular machine learning system for flow-level traffic classification in large networks. ACM Trans. Knowl. Discov. Data 6(1), 4:1–4:34 (2012)
Moore, B., Ellesson, E., Strassner, J., Westerinen, A.: Policy core information model - version 1 specification, internet Engineering Task Force (IETF). https://tools.ietf.org/html/rfc3060
Ng, B., Hayes, M., Seah, W.K.G.: Developing a traffic classification platform for enterprise networks with SDN: Experiences & lessons learned. In: 2015 IFIP Networking Conference (IFIP Networking), pp. 1–9, May 2015
Pacheco, F., Exposito, E., Aguilar, J., Gineste, M., Baudoin, C.: A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2018
Pacheco, F., Exposito, E., Gineste, M., Baudoin, C., Aguilar, J.: Towards the deployment of machine learning solutions in network traffic classification: a systematic survey. IEEE Communications Surveys Tutorials, p. 1 (2018)
Pacheco, F., Exposito, E., Gineste, M., Budoin, C.: An autonomic traffic analysis proposal using machine learning techniques. In: Proceedings of the 9th International Conference on Management of Digital EcoSystems, MEDES 2017, pp. 273–280 (2017)
Pietrzyk, M., Costeux, J.L., Urvoy-Keller, G., En-Najjary, T.: Challenging statistical classification for operational usage: the adsl case. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, IMC 2009, pp. 122–135 (2009)
Siller, M., Woods, J.C.: QoS arbitration for improving the QoE in multimedia transmission. In: 2003 International Conference on Visual Information Engineering VIE 2003 (2003)
Trestian, I., Ranjan, S., Kuzmanovic, A., Nucci, A.: Googling the internet: profiling internet endpoints via the world wide web. IEEE/ACM Trans. Networking 18(2), 666–679 (2010)
Yavatkar, R., Pendarakis, D., Guerin, R.: A framework for policy-based admission control, internet Engineering Task Force (IETF). https://tools.ietf.org/html/rfc2753
Acknowledgment
We want to thank the Centre National d’Études Spatiales (CNES), Toulouse, France for allowing us to use the SAT data, which is developed under the project R&T CNES: Application du Machine Learning au Satcom.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pacheco, F., Exposito, E., Gineste, M. (2019). A Wearable Machine Learning Solution for Internet Traffic Classification in Satellite Communications. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-33702-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33701-8
Online ISBN: 978-3-030-33702-5
eBook Packages: Computer ScienceComputer Science (R0)