中圖分類號：TP393文獻標識碼：ADOI: 10.19358/j.issn.2097-1788.2024.02.002
引用格式：謝承宗，王禹賀，王佰多，等.基于GRU-FedAdam的工業(yè)物聯(lián)網入侵檢測方法［J］.網絡安全與數據治理，2024，43（2）：9-15.

The intrusion detection method for IIoT based on GRU-Fed

Abstract： Aiming at the problems of data privacy leakage and long training time of intrusion detection methods in Industrial Internet of Things, this paper proposes an intrusion detection method based on GRU-FedAdam. The method firstly adopts federated learning to collaboratively train the intrusion detection model to protect the client data privacy, secondly adopts an intrusion detection model based on the gated recurrent unit (GRU) and Adam optimization algorithm to increase the training speed of the client model. In this paper, the TON_IoT dataset is selected as the experimental data, and the training time is reduced by 4 s compared with the single layer LSTM model after two communication rounds of computation; the training model using Adam algorithm converges faster than the SGD algorithm， and the accuracy of the intrusion detection model reaches 0.99. Experimental results show that the intrusion detection method based on GRU-FedAdam can effectively reduce training time and achieve superior intrusion detection performance while preserving data privacy.

Key words : Industrial Internet of Things; intrusion detection; GRU; federated learning