Building a Robust Federated Learning based Intrusion Detection System in Internet of Things


Afrooz Rahmati, Afra Mashhadi, and Geethapriya Thamilarasu, University of Washington Bothell, USA


The Internet of Things (IoT) has emerged as the next big technological revolution in recent years with the potential to transform every sphere of human life. As devices, applications, and communication networks become increasingly connected and integrated, security and privacy concerns in IoT are growing at an alarming rate as well. While existing research has largely focused on centralized systems to detect security attacks, these systems do not scale well with the rapid growth of IoT devices and pose a single-point of failure risk. Furthermore, since data is extensively dispersed across huge networks of connected devices, decentralized computing is critical. Federated learning (FL) systems in the recent times has gained popularity as the distributed machine learning model that enables IoT edge devices to collaboratively train models in a decentralized manner while ensuring that data on a user's device stays private without the contents or details of that data ever leaving that device. In this paper, we propose a federated learning based intrusion detection system using LSTM Autoencoder. The proposed technique allows IoT devices to train a global model without revealing their private data, enabling the training model to grow in size while protecting each participants local data. We conduct extensive experiments using the BoT-IoT data set and demonstrate that our solution can not only effectively improve IoT security against unknown attacks but also ensure users data privacy.


Internet of Things, security, Intrusion Detection system, Federated learning, Deep embedded clustering

Full Text  Volume 14, Number 2