中图分类号： TP393.08
文献标识码： A
DOI： 10.19358/j.issn.2097-1788.2022.04.007
引用格式： 曾凡锋，谢世游，王景中. 基于组合神经网络的物联网入侵检测方法[J].网络安全与数据治理，2022，41(4)：42-48.

Intrusion detection method of Internet of Things based on combined neural network

Abstract： Aiming at the global feature extraction problem of traffic data of Internet of Things, the existing methods for network intrusion detection are improved, and an intrusion detection method based on combined neural network is proposed. Firstly, the spatial features of the traffic data in the data set are extracted by one-dimensional densely connected convolutional neural network, and then the gated recurrent unit is used to further extracts time series features to complete the spatiotemporal feature extraction of traffic data in Internet of Things. Finally, the UNSW-NB15 and Bot-iot datasets are used for multi-class training and testing of the combined neural network model. The experimental results show that the proposed method has certain improvement in the accuracy and other evaluation indicators, which shows the effectiveness of the method.

Key words : Internet of Things；intrusion detection；global feature extraction；combined neural network；multi classification