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Title: Detección de anomalías en entornos del internet de las cosas
Author: Mellizo-Soto Díaz, Gonzalo
Director: Casas Roma, Jordi
Tutor: Hernández Gañán, Carlos
Keywords: machine learning
internet of things
anomaly detection
Issue Date: 8-Jun-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: In recent years the amount of connected devices has greatly increased, with an increasing number of applications in the industry each day. This devices can be subject of attacks causing instability or data leaks that can be dangerous both for the users and the enterprises, in order to avoid or confront them, security and early detection are becoming a must in a connected world. The focus is the monitoring and detection of the attacks in Internet of Things devices using state of the art Machine Learning techniques. Models such as SVM, DBScan or Isolation Forests have been used and assembled in order to identify with a better accuracy when an attack is happening. With this assembly, attack detection has increased up to 15% comparing to traditional methods and individual model usages and times have been considerably reduced. An active use of Machine Learning models has shown a great improvement at anomaly detection by securing the devices and decreasing the reaction times when facing attacks.
Language: Spanish
Appears in Collections:Bachelor thesis, research projects, etc.

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