Review of Traffic Analysis Methods Based on Machine Learning and Pre-trained Model
摘要
With the popularization of the Internet and the diversification of applications, fine-grained classification of massive network traffic has become key to optimizing quality of service and analyzing user behavior patterns. This paper presents an overview of Machine Learning (ML)-based and pretrained model-based network traffic analysis methods to promote further research and development in this field through multidimensional comparison and analysis. First, the complete traffic classification pipeline is deconstructed, covering data acquisition, preprocessing, and feature extraction, and the practical value of data balancing techniques is examined. The data format, scale, and scene suitability of mainstream public datasets are introduced, compared, and analyzed from multiple perspectives, highlighting their data distribution, feature redundancy, and timeliness problems. Second, it summarizes the limitations of traditional algorithms in handling high-dimensional data and meeting real-time requirements, and outlines the trend of applying pretrained models in traffic analytics, through a focused comparative analysis of experimental results. This review includes breakthroughs in Transformer-based pretrained models, their fusion with Deep Learning (DL) models, and advances in lightweight pretrained models for traffic classification. Finally, by considering dynamic research trends, the opportunities and challenges in future applications of pretrained models are discussed, and their limitations in terms of computational cost and privacy protection are analyzed.