With the increasing sophistication of network attacks, traditional intrusion detection systems face the dual challenges of high-dimensional traffic feature extraction difficulties and category imbalance. We introduce a novel intrusion detection technique called CGM in this paper, which integrates CNN and Bidirectional Gated Recurrent Units (BiGRU), and is optimized by multi-head attention mechanism to improve detection performance. Our proposed method differs from existing approaches in the following aspects. Firstly, the CVAE-GAN-NCR algorithm is used to resample the dataset to efficiently generate minority class samples. Secondly,we incorporate Recursive Feature Elimination (RFE) combined with Random Forest (RF) to optimize feature selection. Finally, we use a multi-head attention mechanism to optimize the CNN - BiGRU model to improve the model’s predictive accuracy and feature representation. To validate our approach, we conduct experiments on the CSE-CICIDS2018 dataset, achieving a multi-class classification accuracy of 98.04%. The method not only optimizes the data processing flow, but also improves the accuracy and robustness of malicious traffic detection by fusing deep learning and feature selection techniques.