Adaptive Convolutional Neural Network Structure for Network Traffic Classification

Abstract

Network traffic classification has been highly concerned by academia and industry for decades. In recent years, deep learning has attracted many scholars to use it in network traffic classification due to its excellent performance in the fields of computer vision and natural language processing. However, the performance of the neural network depends on its structure in the same dataset. When looking for the neural network to classify network traffic, it is necessary to constantly adjust the structure of the neural network to achieve better results, which is very time-consuming and experience-dependent. To solve the above problem, this paper proposes an Adaptive Convolutional Neural Network Structure for Network Traffic Classification (ACNNS-NTC) algorithm. The proposed algorithm first pre-processes the network traffic data used for training and testing, and then uses particle swarm optimization algorithm to optimize the network structure of the convolutional neural network, to generate convolutional neural network structure for network traffic classification, and verify the classification results. Experimental results show that the accuracies of the ACNNS-NTC algorithm on public datasets (ISCX-IDS2012, USTC-TFC2016, CIC-IDS2017) are above 99%. At the same time, the generated convolutional neural network has a more succinct structure and fewer model parameters compared with the existing methods.