Please use this identifier to cite or link to this item:
http://hdl.handle.net/10609/138409
Title: | Inteligencia Artificial para la detección de binarios maliciosos |
Author: | Díaz Navarro, Jorge |
Tutor: | Nuñez Do Rio, Joan Manuel |
Others: | Ventura, Carles |
Abstract: | The execution of malware on computer systems is a problem that affects society as a whole and whose economic consequences are increasing year after year. The improved technical capabilities of malicious binaries to camouflage themselves and remain undetected limit the protection capacity of traditional antivirus software, which poses a very high risk to the security of organizations and individuals. In this situation, Artificial Intelligence offers effective techniques that improve current defensive capabilities in malware detection. However, current research does not provide detailed tools or references that allow cybersecurity professionals to improve their own detection products, so in many cases the current limitations are still in place. Through this work, multiple effective Machine Learning models have been created that have been able to detect previously unknown malware samples. It has also provided the details, code and references necessary for cybersecurity professionals to be able to create their own detection models. |
Keywords: | artificial intelligence cybersecurity malware |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 4-Jan-2022 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
jdiaznavTFM0122memoria.pdf | Memoria del TFM | 1,06 MB | Adobe PDF | View/Open |
Share:
This item is licensed under a Creative Commons License