网络安全与数据治理2024，Vol.43Issue(2)：9-15,7.DOI:10.19358/j.issn.2097-1788.2024.02.002
基于GRU-FedAdam的工业物联网入侵检测方法
The intrusion detection method for IIoT based on GRU-FedAdam
摘要
Abstract
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.关键词
工业物联网/入侵检测/GRU/联邦学习Key words
Industrial Internet of Things/intrusion detection/GRU/federated learning分类
信息技术与安全科学引用本文复制引用
谢承宗,王禹贺,王佰多,李世明..基于GRU-FedAdam的工业物联网入侵检测方法[J].网络安全与数据治理,2024,43(2):9-15,7.基金项目
中国高校产学研创新基金(2022HS055) （2022HS055）
河南省高等学校重点科研项目(21A413001) （21A413001）
