Please use this identifier to cite or link to this item:
Title: Aplicación de técnicas de machine learning a la ciberseguridad: Aprendizaje supervisado para la detección de amenazas web mediante clasificación basada en árboles de decisión
Author: Dueñas Quesada, José María
Director: Rifà Pous, Helena  
Tutor: Hernández Jiménez, Enric
Keywords: decision trees
machine learning
computer security
data analysis
supervised learning
Issue Date: 2-Jun-2020
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The purpose of this Master thesis is to develop a decision tree based predictive machine learning model which main task is to classify a set of HTTP requests in normal and anomalous ones. The thesis includes a state-of-the-art survey on the main applications of the machine learning in the cybersecurity field, the implementation of the proposed decision tree classifier model with the Python language and the Scikit-learn library and the analysis of the results obtained on the application of the implemented model against the CSIC-2010 dataset. The proposed model in this thesis achieved a 100% of accuracy in classifying the HTTP requests.
Language: Spanish
Appears in Collections:Bachelor thesis, research projects, etc.

Files in This Item:
File Description SizeFormat 
joseduenasTFM0620memoria.pdf3.54 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons