Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit

Xingyuan Liu; Xianghua Miao

doi:10.1117/12.2636377

6 May 2022 Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit

Xingyuan Liu, Xianghua Miao

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121760M (2022) https://doi.org/10.1117/12.2636377
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Intrusion detection based on machine learning plays an important role in the security protection of the network environment. In order to improve the accuracy of network intrusion detection, intrusion detection algorithm based on dilated convolution and a two-way gating unit is proposed. Firstly, dilated convolution (DCNN) is used to increase the receptive field of information and extract spatial features. Secondly, the bidirectional gated cyclic unit (bigru) model is used to capture the temporal features of data, and the attention mechanism is introduced to increase the calculation of feature importance. Compared with the classical machine learning classifier, this model has a high detection rate. The multi-classification experiments on the famous UNSW-NB15 data set show that the model has leading performance, and its classification accuracy can reach 97.14%.

Citation Download Citation

Xingyuan Liu and Xianghua Miao "Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121760M (6 May 2022); https://doi.org/10.1117/12.2636377

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