Abstract: Aiming at the complexity and diversity of current network intrusion, the traditional model was insufficient to extract traffic characteristics, and had low accuracy, we proposed an intrusion detection method based on CR-BiGRU hybrid model improved by merging residual network. Firstly, the dataset was normalized and one-hot encoding treatment in the model. Secondly, the convolutional neural network based on the residual network was used to extract the spatial features. Finally,   the bidirectional gated neural network was used to extract the temporal features,  complete the training of the model and realize the intrusion detection of the abnormal network. In order to illustrate the applicability of the model, comparative analysis experiments were conducted based on NSL-KDD and UNSW-NB15 datasets. The results show that the accuracy of the method based on the above datasets is 99.40% and 83.79% respectively, which is obviously superior to the classical network intrusion detection algorithm, and can effectively improve the accuracy of network intrusion detection, so as to  better ensure the  communication security of network data.

Key words: intrusion detection, deep learning, network traffic, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU)