Network intrusion detection method based on CAE-ResNet-BiLSTM

To address the issue of low detection accuracy in existing network intrusion detection technologies caused by insufficient learning of data feature information, this paper proposes a network intrusion detection model based on Convolutional Autoencoder (CAE), Residual Network (ResNet), and Bidirectional Long Short-Term Memory (BiLSTM) networks. This method first performs preliminary feature extraction using a CAE, then enhances feature processing capabilities with ResNet, and finally captures time series information with BiLSTM. Classification is carried out through a fully connected layer and an output layer. The validation results on the NSL-KDD dataset and UNSW-NB15 dataset show that the model achieved accuracy rates of 99.34% and 82.47%, with false positive rates of 0.376% and 6.088%, respectively. The experimental results indicate that the proposed model effectively addresses issues such as low detection accuracy caused by insufficient learning of data feature information.