面向卫星网络的流量分类方法研究

doi:10.19678/j.issn.1000-3428.0069654

摘要/Abstract

摘要： 卫星网络具有覆盖范围广、机动性强及功耗超低等优势,可作为地面通信网络的重要补充和延伸,推动构建空天地一体化网络。然而,随着卫星业务的开放普及,卫星网络流量激增且日益复杂,给卫星网络的管理及业务调度带来了严峻挑战。显然,设计一种高效的网络流量分类方法,给不同类型的卫星网络流量分配合理的计算资源,成为缓解卫星网络压力的关键。基于端口、载荷、统计以及行为的传统网络流量分类方法存在有效性、隐私性等问题,已经不再满足复杂网络业务的需求。随着大模型的发展,各种大模型技术得到广泛应用。因此,为提升卫星网络的业务调度效率并优化卫星网络算力,提出一种基于全局感知模块(GPM)-ViT(Vision Transformer)模型的网络流量分类方法。基于网络流量数据,将流量会话数据转化为灰度图片,经过特征提取模块,充分提取图片全局和局部信息。将处理后的数据输入ViT,利用其多头注意力机制提取数据关联信息,增强分类能力。实验结果表明,GPM-ViT模型的分类准确率达到97.86%,相比基准模型有所提升。

关键词: 网络流量, 分类, 卫星网络, 特征, ViT网络

Abstract: Satellite networks have wide coverage, strong mobility, and ultralow power consumption, which allow them to act as an extension to ground communication networks, thereby promoting the construction of integrated space-ground networks. However, the opening and popularization of satellite services have increased network traffic and made it more complex, making their management and service scheduling challenging. Thus, designing an efficient network traffic classification method and allocating reasonable computing resources to different types of satellite network traffic have become critical to alleviating the pressure on satellite networks. Traditional network traffic classification methods based on ports, payloads, statistics, and behavior have issues concerning effectiveness and privacy, making them inadequate for complex network services. Various technologies are widely applied in the development of large models. Therefore, to enhance the operational efficiency of satellite networks and optimize their computing power, this study proposes a network traffic classification method based on the Global Perception Module (GPM) and ViT (Vision Transformer) model. This method transforms network traffic data into grayscale images and extracts features to fully capture global and local information. The processed data are then input into the ViT model, which leverages its multihead attention mechanism to extract data correlation information and enhance classification capability. Experimental results indicate that the accuracy of the GPM-ViT model reaches 97.86%, which is a significant improvement over that of baseline models.

Key words: network traffic, classification, satellite network, feature, ViT network

中图分类号:

TN929

莫定涛, 俱莹, 李文进, 张亚生, 何辞, 董飞虎. 面向卫星网络的流量分类方法研究[J]. 计算机工程, 2025, 51(5): 9-19.

MO Dingtao, JU Ying, LI Wenjin, ZHANG Yasheng, HE Ci, DONG Feihu. Research on Traffic Classification Method for Satellite Networks[J]. Computer Engineering, 2025, 51(5): 9-19.

https://www.ecice06.com/CN/Y2025/V51/I5/9

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