Beyond data sharing: enhancing IoT intrusion detection with blockchain-enabled federated learning

Federated learning (FL) is a decentralized machine learning (ML) approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at a central server. This methodology eliminates the need for data sharing between IoT devices while fostering collaborative model improvement. Nonetheless, concerns arise from the lack of transparency regarding the shared local models and the aggregation techniques employed. This lack of transparency can potentially lead to model poisoning attacks and hinder collaborators from using alternative aggregation methods that better align with their specific use cases. To address this issue, this study proposes a blockchain-based approach using FL to ensure transparent, immutable records of model updates, thereby bolstering security and trust for intrusion detection in IoT devices. In contrast to traditional synchronization- or periodic-update-based approaches, this study proposes a novel time-independent aggregation method for FL blockchain, enabling greater flexibility. Furthermore, the proposed blockchain allows various users to utilize their own aggregation methods, rather than a fixed one, based on their needs, resources, and availability. We also developed a user interface for the proposed blockchain system to visualize various aspects of the method, such as model aggregation. The proposed system is tested using traditional metrics, such as AI model performance, as well as extensive user testing.