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Title: Implementación de Security Data Lake con Splunk. Creación de reglas de correlación y modelos para la detección avanzada de amenazas
Author: García Hidalgo, Tomás
Tutor: Miguel Moneo, Jorge  
Others: Rifà-Pous, Helena  
Keywords: SDL
machine learning
anomaly detection
cyber security
Issue Date: 9-Jan-2023
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The purpose of this work is to expose the current problems and limitations of security event collection and analysis systems (SIEM), and to propose a solution based on the design of a security data lake (SDL) system as a way to overcome these limitations. The application context of this work is organizations that need a platform for early detection of threats and malicious events in their systems and network devices. This is especially important due to the exponential increase in the number of network systems and devices, the digitization of processes and the need to meet certain compliance regulations. The methodology used in this work has consisted of a literature review on the principles of large data management systems, especially SDLs, and the proposal of an SDL system using Splunk as the base technology. The benefits offered by Splunk for the development of a distributed SDL system capable of managing large amounts of data have been detailed, and it has been explained how both correlation rules and anomaly detection models can be implemented using machine learning techniques. As for the results, a comparison has been made between the different techniques for detecting malicious patterns through the data collected by the system, highlighting the flexibility of threat detection models versus correlation rules.
Language: Spanish
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

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