网络安全与数据治理2026，Vol.45Issue(1)：1-8,8.DOI:10.19358/j.issn.2097-1788.2026.01.001
基于双模型的半监督流形混合流量分类方法
A semi-supervised manifold mixup traffic classification method based on Mean-Teacher
摘要
Abstract
Deep Learning techniques have been widely applied in the field of network traffic classification.However,there still exist various challenges,including dependency on large scale data and overfitting.To address these issues,a semi-supervised deep learning method combi-ning mean teacher and manifold mixup is proposed.This method employs a teacher-student architecture,utilizing Exponential Moving Average(EMA)to assist the model learning process and to enhance the generalization capability of model.Additionally,manifold mixup in the feature space effectively refines the model's decision boundary,strengthening robustness.Experimental results demonstrate that with only 1 000 sam-ples per class,the method achieves over 90%accuracy across three network traffic datasets while maintaining outstanding performance under few-shot condition.关键词
流量分类/半监督学习/流形混合/教师-学生模型Key words
traffic classification/semi-supervised learning/manifold mixup/teacher-student model分类
信息技术与安全科学引用本文复制引用
马可,何明枢,蔡晶晶,王小娟..基于双模型的半监督流形混合流量分类方法[J].网络安全与数据治理,2026,45(1):1-8,8.基金项目
国家自然科学基金(62402053,62227805) （62402053,62227805）
中央高校基本科研业务费专项资金(2025KYQD17(BUPT)) （2025KYQD17(BUPT）
