Federated learning for misbehaviour detection with variational autoencoders and Gaussian mixture models

Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning models while data sources' privacy is still preserved. However, most of existing FL approaches are based on supervised techniques, which could require resource-intensive activities and human intervention to obtain labelled datasets. Furthermore, in the scope of cyberattack detection, such techniques are not able to identify previously unknown threats. In this direction, this work proposes a novel unsupervised FL approach for the identification of potential misbehavior in vehicular environments. This paper presents a cloud-based approach to detect misbehavior in vehicular networks. Our method combines Gaussian Mixture Models and Variational Autoencoders in an FL setting using the VeReMi dataset, allowing each vehicle to train on its own data while sharing insights through a central repository of anomalous events. We employ Restricted Boltzmann Machines to ensure the convergence of the model and Fed+ aggregation function to improve the performance of the model in non-identical and independently distributed scenarios. Experimental results on the VeReMi dataset show that our framework effectively identifies malicious behaviors, enabling robust, collective defense strategies across multiple vehicles. In particular, our approach provides better performance (more than 80%) compared to recent proposals, which are usually based on supervised techniques and artificial divisions of the VeReMi dataset.