Network traffic classification based- masked language regression model using CNN

Abstract

Network traffic classification task has become increasingly challenging. The objective behind this classification is to effectively handle bandwidth, prioritize certain types of traffic, enhance application performance, and more. In recent times, there has been a surge in exploring deep learning approaches for network traffic categorization. However, these models demand substantial volumes of training data. Additionally, many classification methods necessitate manual feature extraction, a process that is not only time-consuming but also laborious. Addressing the challenge of identifying optimal features to enhance classification accuracy, this work introduces a deep learning model designed for effective classification of network traffic. The model comprises the following key stages: (a) The dataset involves TCP flows captured from running different network stress and web crawling tools, (b) Pre-processing for removal of anomalies and noises using Label Encoder and OneHotEncoder, (c) The utilization of K-BERT for feature extraction aims to retrieve local spatial–temporal features, (d) feature selection using linear regression model (LASSO) and finally, and (e) The classification of network traffic involves neural network. The model serves to enhance the precision and efficiency of the classification mission. Through comprehensive experimental analysis, it was observed that the Masked Language-based Regression model surpassed other referenced models, achieving an exceptional accuracy of 0.97.