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.