A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications Fannia Pacheco, Ernesto Expósito, Mathieu Gineste To cite this version: Fannia Pacheco, Ernesto Expósito, Mathieu Gineste. A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications. Computer Networks, Elsevier, 2020, 173, pp.107213. 10.1016/j.comnet.2020.107213. hal-02615107 HAL Id: hal-02615107 https://hal.archives-ouvertes.fr/hal-02615107 Submitted on 22 May 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for Satellite Communications Fannia Pachecoa, Ernesto Expositoa, Mathieu Ginesteb ff.pacheco,
[email protected],
[email protected] aUniv Pau & Pays Adour, E2S UPPA, LIUPPA, EA3000, Anglet, 64600, France bD´epartement: Business Line Telecommunication, R&D department, Thales Alenia Space, TOULOUSE, 31100, France. Abstract Nowadays, the Internet network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact be- tween themselves to provide transmission paths of information between endpoints.