  Nowadays, blockchain-based technologies are being developed in various
industries to improve data security. In the context of the Industrial Internet
of Things (IIoT), a chain-based network is one of the most notable applications
of blockchain technology. IIoT devices have become increasingly prevalent in
our digital world, especially in support of developing smart factories.
Although blockchain is a powerful tool, it is vulnerable to cyber attacks.
Detecting anomalies in blockchain-based IIoT networks in smart factories is
crucial in protecting networks and systems from unexpected attacks. In this
paper, we use Federated Learning (FL) to build a threat hunting framework
called Block Hunter to automatically hunt for attacks in blockchain-based IIoT
networks. Block Hunter utilizes a cluster-based architecture for anomaly
detection combined with several machine learning models in a federated
environment. To the best of our knowledge, Block Hunter is the first federated
threat hunting model in IIoT networks that identifies anomalous behavior while
preserving privacy. Our results prove the efficiency of the Block Hunter in
detecting anomalous activities with high accuracy and minimum required
bandwidth.
