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.