Federated learning-based intrusion detection system for the internet of things using unsupervised and supervised deep learning models
Babatunde Olanrewaju-George, Bernardi Pranggono
Abstract
The adoption of the Internet of Things (IoT) in our technology-driven society is hindered by security and data privacy challenges. To address these issues, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) can be applied to build Intrusion Detection Systems (IDS) that help securing IoT networks. Federated Learning (FL) is a decentralized approach that can enhance performance and privacy of the data by training IDS on individual connected devices. This study proposes the use of unsupervised and supervised DL models trained via FL to develop IDS for IoT devices. The performance of FL-trained models is compared to models trained via non-FL using the N-BaIoT dataset of nine IoT devices. To improve the accuracy of DL models, a randomized search hyperparameter optimization is performed. Various performance metrics are used to evaluate the prediction results. The results indicate that the unsupervised AutoEncoder (AE) model trained via FL is the best overall in terms of all metrics, based on testing both FL and non-FL trained models on all nine IoT devices.