Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

Plataforma basada en redes neuronales para realizar pruebas de autenticación de usuarios mediante datos de ponibles

Durante esta última década la Inteligencia Artificial (IA) y más concretamente el aprendizaje profundo basado en redes neuronales se han convertido en un estándar en varios sectores que afectan diferentes campos de nuestra vida. Estos son capaces de resolver problemas complejos que requieren encontrar pa-trones ocultos en los datos que tratan. La identificación es la capacidad de identificar de forma exclusiva a un usuario de un sistema o una aplica-ción que se está ejecutando en el sistema. Estos modelos tienen la capacidad de identificar a los usuarios mediante los datos ponibles que recogen. Los datos ponibles en nuestro caso hacen referencia a los datos que recogen los relojes inteligentes y mediante los cuales se puede identificar al usuario según sus patro-nes. Debido a la amplia funcionalidad que ofrecen, es necesario probar ampliamente las distintas configu-raciones y capacidades que ofrecen. En este trabajo se propone una plataforma basada en redes neuronales capaz de realizar pruebas de autenticación de usuarios mediante datos de ponibles. En particular, se ha creado un framework o estructura que permite al usuario probar las diferentes funcionalidades que ofrece los modelos de aprendizaje profundo. En concreto, la plataforma permite la selección de dos tipos de mo-delos, un prototipo de Red Neuronal Artificial (ANN) como es el Perceptrón Multicapa (MLP) y un prototipo de Aprendizaje Profundo basado en Redes Neuronales Convolucionales (CNN).During this last decade, Artificial Intelligence (AI) and more specifically deep learning based on neural networks have become a standard in various sectors that affect different fields of our lives. These are ca-pable of solving complex problems that require finding hidden patterns in the data they deal with. Identification is the ability to uniquely identify a user of a system or an application running on the sys-tem. These models have the ability to identify users through the wearable data they collect. Wearable data refers to data that smart watches collect and through which the user can be identified based on their pat-terns. These models are capable of diagnosing cancer prematurely or proposing better eating habits. Due to the extensive functionality they offer, it is necessary to extensively test the various features and capabi-lities they offer. In this work, is proposed a platform based on neural networks capable of performing user authentication tests using wearable data. In particular, a has been created that allows the user to test the different functionalities offered by deep learning models. Specifically, the platform allows the selection of two types of models, an Artificial Neural Network (ANN) prototype such as the Multilayer Perceptron (MLP) and a Deep Learning prototype based on Convolutional Neural Networks (CNN).Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Grado en Ingeniería Informátic