## 1 Introduction
[21; 22] improved intrusion detection classification models using Decision Tree and Random Forest algorithms, but their accuracy decreased when dealing with high-dimensional data. [PERSON] et al. [23] combined a simple Bayesian algorithm with SVM to improve intrusion detection rates, but this resulted in a high false alarm rate. The aforementioned shallow machine learning algorithms are no longer sufficient to address the current challenges of diverse network intrusion attacks and increasing traffic [24]. Deep learning-based models offered greater potential for capturing complex features and data patterns, making them effective in addressing new threats. [PERSON] et al. [25] proposed an improved CNN model that outperformed traditional machine learning methods when validated on the KDD99 dataset. [PERSON] et al. [26] developed a CNN-based model using the KDD Cup 1999 and CSE-CIC-IDS 2018 datasets to detect various types of denial-of-service (DoS) attacks. Experimental results show that the CNN model outperformed shallow machine learning algorithms. The feature extraction capability of CNN enabled it to learn more complex attack patterns from network data and improve detection accuracy. However, simple CNN models have limitations, such as instability and overfitting. To solve these issues, [PERSON] et al. [27] introduced the residual structure into the CNN network. Experimental comparisons showed that the model with the residual structure demonstrated higher stability when the input data contained more information. [PERSON] et al. [28] proposed Juggler-ResNet, which used a fusible residual structure, retained the feature extraction capability of the residual structure, and alleviated the gradient vanishing problem during training. [PERSON] et al. [29] introduced an enhanced ResNet architecture for feature learning, which extracted deeper features from traffic trajectories. The above research demonstrated that the residual structure effectively alleviated the model degradation problem and improved stability, but its feature extraction ability remained insufficient. To further enhance feature extraction, researchers actively explored multiscale feature fusion methods. [PERSON] et al. [30] designed a multiscale CNN model with multiple convolutional layers, which better captured local features at different levels and improved the detection accuracy. [PERSON] et al. [31] proposed an intrusion detection model that integrated a residual network (RESNET) and a parallel cross-convolutional neural network, using parallel cross-multi-scale fusion features to improve the detection accuracy of abnormal data traffic in imbalanced datasets. [PERSON] et al. [32] proposed a raw data detection module that used the Inception model to automatically learn multiscale features, avoiding the tedious process of feature extraction. Multiscale feature fusion methods capture more comprehensive information from data by fusing features at different levels and scales. However, after multiscale feature fusion, unreasonable weight distribution negatively impacted model performance [33]. [PERSON] et al. [34] integrated multiple attention mechanisms into CNNs to evaluate input features from different perspectives and assign appropriate weights, which improved prediction performance. [PERSON] et al. [35] proposed a hierarchical CNN-attention network to extract multidimensional features from traffic data through a hierarchical structure, which reduced the false alarm rate. [PERSON] et al. [36] analyzed the temporal changes in network traffic, applied the attention mechanism for data classification, and validated the approach using the UNSW-NB15 dataset, which improved classification accuracy. [PERSON] et al. [37] introduced the attention mechanism into the network intrusion detection model, which effectively improved the accuracy and reduced the false alarm rate.