A Cutting‐Edge Hybrid Deep Learning Technique with Low Rank Approximation for Attacks Classification on IoT Traffic Data

Abstract

ABSTRACTNetwork 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 a multi‐layer perceptron (MLP), a hybrid convolutional neural network (CNN), and a low‐rank approximation model. While the CNN and MLP models extract features and categorize 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. LR‐CNN‐MLP achieves the highest results of performance metrics such as 0.944 precision, 0.979 recall, 0.961 F1‐score and 98.17 accuracy.