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Title: Entrenament d'un arbre de decisió per a la detecció d'atacs distribuïts de denegació de servei (DDoS). Una aproximació
Author: Farràs i Ballabriga, Gerard
Director: Rifà Pous, Helena  
Tutor: Hernández Jiménez, Enric
Keywords: cyber security dataset
machine learning
decision trees
DDoS
Issue Date: 29-Dec-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: In the information and knowledge society of the 21st century, the information of each organization is highly valuable. The possibility of uninterrupted and secure use of information systems becomes an essential task for public and private organizations around the world. Within this context, the operational continuity of all information systems must be guaranteed. Distributed denial of service attacks (DDoS) are attacks that, using multiple techniques, impair the continued use of information systems. It is necessary, therefore, to find tools that mitigate these types of attacks. There are currently software-based mechanisms that ensure information systems, but work must be done to implement them with low-cost computational techniques and also ensure that they are updated day by day. A field currently in search and with a lot of future projection involves using artificial intelligence and machine learning techniques for real-time detection of what an attack could be. In this work, an implementation of a decision tree is carried out using machine learning techniques on a set of network traffic data (dataset) with DDoS attacks.
Language: Catalan
URI: http://hdl.handle.net/10609/108166
Appears in Collections:Bachelor thesis, research projects, etc.

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