FedHome: A federated learning framework for smart home device classification and attack detection by broadband service providers

Abstract

The rise of the Internet of Things (IoT) has led to the integration of various devices into smart homes, significantly increasing the complexity and vulnerability of home networks. Consequent network performance issues often lead to complaints directed at Broadband Service Providers (BSPs), which may arise from either legitimate usage or malicious cyber attacks. BSPs, however, lack visibility into client-side networks, which is partly due to privacy concerns. This makes it hard to identify the true cause of performance problems. While previous research has tackled these challenges using Machine Learning (ML) techniques, few studies have approached the problem from the perspective of BSPs. They need a solution that is scalable, accurate, and privacy-preserving. Existing centralized ML models fail to generalize across these heterogeneous environments and provide low accuracy. We address this gap by introducing a novel Federated Learning (FL) framework for smart home device classification and attack detection. The proposed approach offers a privacy-preserving, scalable framework that can achieve accuracies of more than 80%. This framework can be installed inside the existing resource-constrained home gateways, making it suitable for large-scale deployment by BSPs.