  The large number of sensors and actuators that make up the Internet of Things
obliges these systems to use diverse technologies and protocols. This means
that IoT networks are more heterogeneous than traditional networks. This gives
rise to new challenges in cybersecurity to protect these systems and devices
which are characterized by being connected continuously to the Internet.
Intrusion detection systems (IDS) are used to protect IoT systems from the
various anomalies and attacks at the network level. Intrusion Detection Systems
(IDS) can be improved through machine learning techniques. Our work focuses on
creating classification models that can feed an IDS using a dataset containing
frames under attacks of an IoT system that uses the MQTT protocol. We have
addressed two types of method for classifying the attacks, ensemble methods and
deep learning models, more specifically recurrent networks with very
satisfactory results.
