面向网络流量分类的Mamba网络:引入数据增强的优化方法  

Mamba Network for Network Traffic Classification:An Optimized Approach with Data Augmentation

作　　者：赵新建 夏飞 朱凤玲 陈石 ZHAO Xinjian;XIA Fei;ZHU Fengling;CHEN Shi(Information and Telecommunication Branch of State Grid Jiangsu Electric Power CO.,LTD.,Nanjing 210000,China;School of Computer Science,Nanjing University,Nanjing 210023,China)

机构地区：[1]国网江苏省电力有限公司信息通信分公司,江苏南京210000 [2]南京大学计算机学院,江苏南京210023

出　　处：《软件导刊》2025年第3期99-108,共10页Software Guide

基　　金：国网江苏省电力有限公司科技项目(J2023178)。

摘　　要：网络流量分类在现代互联网环境中发挥着重要作用,对于流量来源理解、异常模式识别和网络攻击检测至关重要。然而,随着加密流量和匿名网络技术的广泛应用,传统基于规则的方法和深度报文检测方法逐渐面临挑战。近年来,机器学习和深度学习方法为网络流量分类提供了新的思路,但依赖于大量标注数据集且存在数据不平衡问题,使得模型训练效果受限。为了解决这些问题,提出在NetMamba微调阶段引入数据增强技术,通过合成流量样本扩充数据集规模、平衡类别样本并提高模型泛化能力。实验结果表明,数据增强能够有效提高恶意流量检测准确性和效率,同时可减少标注成本并防止过拟合。该方法为网络流量分类任务提供了新的解决方案,尤其在数据不足和类别不平衡的场景下具有重要意义。Network traffic classification plays a crucial role in modern internet environments,essential for understanding traffic sources,identifying abnormal patterns,and detecting network attacks.However,with the widespread use of encrypted traffic and anonymous network technologies,traditional rule-based methods and deep packet inspection(DPI)approaches face growing challenges.In recent years,machine learning and deep learning methods have provided new perspectives for network traffic classification,but their reliance on large labeled datasets and the issue of data imbalance limit the effectiveness of model training.To address these problems,this paper introduces data augmentation techniques during the fine-tuning phase of NetMamba.By synthesizing traffic samples,the approach expands the dataset,balances class distributions,and improves the model's generalization capability.Experimental results show that data augmentation can effectively enhance the accuracy and efficiency of malicious traffic detection,reduce labeling costs,and prevent overfitting.The proposed method offers a novel solution for network traffic classification,especially in scenarios with limited data and class imbalance,making it particularly valuable in enhancing the detection of malicious traffic.

关 键 词：网络流量分类 加密流量 预训练 Mamba 数据增强 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]