计算机工程2026，Vol.52Issue(6)：53-67,15.DOI:10.19678/j.issn.1000-3428.0252301
基于机器学习与预训练模型的流量分析方法综述
Review of Traffic Analysis Methods Based on Machine Learning and Pre-trained Model
摘要
Abstract
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.关键词
流量分析/机器学习/深度学习/预训练模型/特征提取/联邦学习Key words
traffic analysis/Machine Learning(ML)/Deep Learning(DL)/pre-trained model/feature extraction/Federated Learning(FL)分类
信息技术与安全科学引用本文复制引用
李学相,郑永利,张怡泽,段鹏松..基于机器学习与预训练模型的流量分析方法综述[J].计算机工程,2026,52(6):53-67,15.基金项目
郑州市协同创新重大专项(20XTZX06013) （20XTZX06013）
中国工程科技发展战略河南研究院战略咨询研究项目(2022HENYB03) （2022HENYB03）
河南省科技攻关项目(232102210050,242102210060). （232102210050,242102210060）
