基于GRU-FedAdam的工业物联网入侵检测方法OA

Aiming at the problems of data privacy leakage and long training time of intrusion detection methods in Industrial Inter-net 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 detec-tion 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 con-verges 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 de-tection performance while preserving data privacy.