  The continuous strengthening of the security posture of IoT ecosystems is
vital due to the increasing number of interconnected devices and the volume of
sensitive data shared. The utilisation of Machine Learning (ML) capabilities in
the defence against IoT cyber attacks has many potential benefits. However, the
currently proposed frameworks do not consider data privacy, secure
architectures, and/or scalable deployments of IoT ecosystems. In this paper, we
propose a hierarchical blockchain-based federated learning framework to enable
secure and privacy-preserved collaborative IoT intrusion detection. We
highlight and demonstrate the importance of sharing cyber threat intelligence
among inter-organisational IoT networks to improve the model's detection
capabilities. The proposed ML-based intrusion detection framework follows a
hierarchical federated learning architecture to ensure the privacy of the
learning process and organisational data. The transactions (model updates) and
processes will run on a secure immutable ledger, and the conformance of
executed tasks will be verified by the smart contract. We have tested our
solution and demonstrated its feasibility by implementing it and evaluating the
intrusion detection performance using a key IoT data set. The outcome is a
securely designed ML-based intrusion detection system capable of detecting a
wide range of malicious activities while preserving data privacy.
