Current network traffic data show high-dimensional, polymorphic, and massive characteristics, which is a new challenge for intrusion detection. In order to address the limitations of low detection efficiency and lack of lightweight consideration in traditional intrusion detection models, a lightweight network intrusion detection model incorporating GRU and CNN is proposed. Firstly, redundant features in the dataset are removed by using extremely randomized trees. Secondly, feature extraction is performed by using GRU. By taking into account the long and short-term dependencies in the data, all hidden layer outputs are treated as sequence feature information for the next step; then a lightweight CNN model with structures such as inverse residual, depthwise separable convolution, and dilated convolution are used for spatial feature extraction; a channel attention mechanism is added to accelerate model convergence. Finally, experiments on the CIC-IDS2017 dataset show that the method has excellent detection performance, as well as the advantages of few model parameters, small model size, short training time, and short detection time, which is suitable for intrusion detection of network traffic.