  The current amount of IoT devices and their limitations has come to serve as
a motivation for malicious entities to take advantage of such devices and use
them for their own gain. To protect against cyberattacks in IoT devices,
Machine Learning techniques can be applied to Intrusion Detection Systems.
Moreover, privacy related issues associated with centralized approaches can be
mitigated through Federated Learning. This work proposes a Host-based Intrusion
Detection Systems that leverages Federated Learning and Multi-Layer Perceptron
neural networks to detected cyberattacks on IoT devices with high accuracy and
enhancing data privacy protection.
