Federated Learning to detect malicious network traffic in IoT environments

Abstract

This thesis consists in the development and analysis of Federated Learning to detect malicious network traffic in IoT environments. The motivation of the project is based on two main aspects, the poor security measures in the actual IoT devices and the necessity of preserving data privacy in Machine Learning processes. First, this project goes through an overview of Machine Learning concepts, followed by explanations of the most common attacks to IoT devices. We will also find information about Federated Learning and its main characteristics. Second, we can find two practical parts. The first one consists in applying Centralised Learning to the chosen dataset and with 3 different algorithms. After that, the entire dataset has been divided into several smaller ones representing different IoT devices in a network, and the same three algorithms have been applied following two different Federated Learning strategies.