The increasing frequency and complexity of network attacks underscore the need for robust detection models capable of accurately identifying malicious activities in network traffic.In this paper, we propose GRU-ResCBANet, a novel deep learning-based hybrid model for network traffic anomaly detection.The proposed model utilizes a parallel two-stage feature extraction and fusion technique.
In the first stage, we enhance us polo assn mens sweaters the convolutional neural network by introducing residual units and convolutional block attention module to improve spatial and channel feature extraction.In the second stage, gated recurrent units are used to capture temporal dependencies in the network traffic data.The features extracted from both stages are then fused to form a comprehensive representation of the data.
Finally, abnormal traffic detection is performed through a fully connected layer, which classifies the read more network traffic.We evaluate GRU-ResCBANet’s performance on the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets, achieving binary classification accuracies of 99.70%, 99.
16%, and 99.83%, and multi-class classification accuracies of 99.69%, 98.
23%, and 99.79%, respectively.Comparative analysis with eight other models, as well as models reported in the literature, demonstrates that GRU-ResCBANet offers superior detection performance in both binary and multi-class classification tasks.