An Empirical Study of the Deep Gradient Network Model to Perform Efficient Packet Classification

Abstract

Abstract The fast expansion of the Internet has increased the significance of packet classification in a network environment. The classification of the packets was done using various centred-on data structure techniques. However, the current algorithms have an issue with time budget and can't handle big rule sets. The main challenge is creating a packet classification algorithm that adds processing overhead and can handle enormous sets of classification rules. The proposed deep gradient network model (DGN) fixes these difficult problems. The anticipated model assists in handling complex tasks with higher prediction accuracy. The network adopts the dense network modelling concept to measure the feature representation and performs the classification process to fulfil prediction accuracy. The model works well compared to other approaches where the analysis is done with the standard benchmark ACL and evaluation is done with k-NN, SVM and DNN. Based on the outcomes, it is proven that the model establishes a better trade-off compared to other approaches.