Network security is experiencing huge challenges as network attacks on traffic data become more frequent and sophisticated. In this paper, we employ hybrid deep learning models and low-rank approximation to present a novel method for multi-label categorization of network assaults on traffic data. Our suggested solution, LR-CNN-MLP, consists of three models While the CNN and MLP models extract features and categorise data, respectively, the low-rank approximation model reduces the input's dimensionality. Overall, by combining hybrid models and low-rank approximation, our proposed LR-CNN-MLP approach provides a promising solution for multi-label categorization of network attacks on traffic data.