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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/39457

    Título
    Novel applications of Machine Learning to Network Traffic Analysis and Prediction
    Autor
    López Martín, ManuelAutoridad UVA
    Director o Tutor
    Carro Martínez, BelénAutoridad UVA
    Sánchez Esguevillas, Antonio JavierAutoridad UVA
    Editor
    Universidad de Valladolid. Escuela Técnica Superior de Ingenieros de TelecomunicaciónAutoridad UVA
    Año del Documento
    2019
    Titulación
    Doctorado en Tecnologías de la Información y las Telecomunicaciones
    Resumen
    It is now clear that machine learning will be widely used in future telecommunication networks as it is increasingly used in today's networks. However, despite its increasing application and its enormous potential, there are still many areas in which the new techniques developed in the area of machine learning are not yet fully utilized. The aim of this thesis is to present the application of innovative techniques of machine learning (ML-Machine Learning) in the field of Telecommunications, and specifically to problems related to the analysis and prediction of traffic in data networks (NTAP - Network Traffic Analysis and Prediction). The applications of NTAP are very broad, so this thesis focuses on the following five specific areas: - Prediction of connectivity of wireless devices. - Security intrusion detection, using network traffic information - Classification of network traffic, using the headers of the transmitted network packets - Estimation of the quality of the experience perceived by the user (QoE) when viewing multimedia streaming, using aggregate information of the network packets - Generation of synthetic traffic associated with security attacks and use of that synthetic traffic to improve security intrusion detection algorithms. The final intention is to create prediction and analysis models that produce improvements in the NTAP areas mentioned above. With this objective, this thesis provides advances in the application of machine learning techniques to the area of NTAP. These advances consist of: - Development of new machine learning models and architectures for NTAP - Define new ways to structure and transform training data so that existing machine learning models can be applied to specific NTAP problems. - Define algorithms for the creation of synthetic network traffic associated with specific events in the operation of the network (for example, specific types of intrusions), ensuring that the new synthetic data can be used as new training data. - Extension and application of classic models of machine learning to the area of NTAP, obtaining improvements in the classification or regression metrics and/or improvements in the performance measures of the algorithms (e.g. training time, prediction time, memory needs, ...).
    Materias Unesco
    3325 Tecnología de las Telecomunicaciones
    1209.03 Análisis de Datos
    1206.01 Construcción de Algoritmos
    Departamento
    Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática
    DOI
    10.35376/10324/39457
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/39457
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • Tesis doctorales UVa [2372]
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    Nombre:
    Tesis1639-191125.pdf
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    5.171Mb
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    Attribution-NonCommercial-NoDerivatives 4.0 InternacionalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional

    Universidad de Valladolid

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