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http://hdl.handle.net/10609/97586
Title: Técnicas de aprendizaje automático para la detección de ataques en el tráfico de red
Author: Valencia Peral, Andrés
Director: Hernández Jiménez, Enric
Tutor: Rifà Pous, Helena  
Keywords: machine learning
deep learning
neural networks
IDS
Issue Date: 4-Jun-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The purpose of the project will be to carry out a general analysis of the currently available automatic learning techniques applied to the implementation of a network intrusion detection system. The different techniques existing today will be described in a simple way, highlighting the fundamental differences between classical Machine Learning techniques as opposed to those of Deep Learning using Neural Networks, as well as the underlying relationship between them. We will proceed with a more exhaustive study of one of the techniques of automatic learning, the decision tree, and finally we will deal with some detail of the aspects to be taken into account in the implementation of a neural network to tackle the problem, with special focus on the choice of training hyperparameters and the consequences that such decisions entail. We will conclude that due to the nature of the problem posed, which has extremely abundant sample sets to be used to train the desired models, the application of these techniques offers a decisive advantage over other traditional techniques based on rules and signatures.
Language: Spanish
URI: http://hdl.handle.net/10609/97586
Appears in Collections:Bachelor thesis, research projects, etc.

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