Efficient Privacy‐Preserving Federated Deep Learning for Network Intrusion of Industrial IoT

Abstract

Intrusion detection systems play a very important role in industrial Internet network security. However, in the large‐scale, complex, and heterogeneous industrial Internet of Things (IoT), it is becoming more and more difficult to defend network intrusion threats due to the insufficiency of high‐quality attack samples. To solve the problem, an efficient federated network intrusion method called EFedID is proposed for industrial IoT, which can allow different industrial agents to collaboratively train a comprehensive detection model. Specifically, the adaptive gradient sparsification method is introduced to alleviate the communication and computation overheads. To protect the data privacy of the agents, a CKKS cryptosystem‐based secure communication protocol is designed to encrypt the model parameters through the federated training process. Our proposed system demonstrates exceptional detection performance on the NSL‐KDD, KDD CUP 99, and CICIDS 2017 datasets. Notably, on the NSL‐KDD dataset, the model compression rate reaches 9 times while the model accuracy reaches 84.31%. On the KDD CUP 99 dataset, the model compression rate reaches 8.9 times while the model accuracy reaches 97.3%. Lastly, on the CICIDS 2017 dataset, the model compression rate reached 6.173 times while the model accuracy reached 95.51%. The experimental results demonstrate that the proposed method is very suitable for effectively developing a high‐accuracy detection model while protecting the data information of industrial agents. Furthermore, the method can be extended to other recent deep learning networks for intrusion detection.