Abstract : Anomaly detection in the industrial internet of things (IIoT) devices is significant due to its fundamental role in protecting modern critical infrastructure. In the IIoT, anomaly detection can be carried out by training machine learning models. Data sharing between factories can expand the data from which the model is trained, thus improving the performance of the model. However, due to the sensitivity and privacy of IIoT data, it is also difficult to build a high-performance anomaly detection model between factories. To address this problem, we design an anomaly detection method for IIoT devices combined blockchain of main-side structure and federated learning. We store the global model on the main-chain while the side-chain records the hash value of the global models and local models, which updated by participating nodes, controlling nodes access to the global model through the main-side blockchain and the smart contracts. Only the nodes participating in the current federated learning training can get the latest global model, so as to encourage the nodes to take part in the training of the global model. We designed a proof of accuracy consensus algorithm, and select the nodes to participate in training according to the accuracy of the local model on the test dataset to resist the poisoning attack of the models. We also use the local differential privacy (LDP) algorithm to protect user data privacy from model inference attacks by adding noise to the local model. Finally, we propose a new algorithm named Fed\_Acc to keep the accuracy of the global model stable when the users add a lot of noise to their local models.
Index terms : IIoT , blockchain , federated learning , anomaly detection , privacy protection