2026-04-14T05:58:29Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3496342025-07-17T15:50:37Zcom_2072_1033col_2072_452951

Técnicas de machine learning para detección de intrusos en redes López Donoso, Sheila Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica León Abarca, Olga Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Seguretat en xarxes Ciber-atacs Detecció d'intrusos Aprenentatge automàtic Cyberattacks are sets of actions directed against information systems that operate on the network, canceling their services. With the increasing complexity of websites and the rapid development of applications today, the possibility of being attacked increases exponentially. The digital transformation has achieved a great number of benefits, but at the same time, life in an interconnected society has brought a series of disadvantages, thus being more exposed to cyberattacks. In addition, current technological development has also benefited "Cybercriminals", which are able to carry out sophisticated computer attacks. This panorama is an invitation to reflect on the importance of protecting networks and systems, as well as the privacy and digital rights of citizens. Having a good cybersecurity system will help to detect and protect systems against various threats. The use of Machine Learning algorithms has been the proposal of this project for the detection of cyberattacks in a network. This work presents an overview of the main threats to computer networks. Besides, a dataset containing samples from network connections, both normal and malicious, and a set of Machine Learning algorithms implemented in Python have been applied to it. The algorithms used are K-Nearest Neighbor (KNN), Random Forest (RF) and Principal Component Analysis (PCA). The performance for each algorithm has been calculated by modifying different parameters against the input data set and after having analyzed the behavior of each one, a comparison has been made between them. 2021-07-15 Bachelor thesis https://hdl.handle.net/2117/349634 PRISMA-156829 spa Restricted access - author's decision application/pdf Universitat Politècnica de Catalunya