通过两个镜头的流量:用于物联网流量分类的双分支视觉转换器

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Wen Yang, Chenxi Tang, Chaowei Tang, Jingwen Lu, Jing Si, Zhuo Zeng, Wenyu Ma
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引用次数: 0

摘要

物联网流量分类通过分析网络流量,识别不同的通信活动,为企业提供高效、可靠的服务质量(QoS)。然而,随着物联网设备的急剧增加,来自智能家居、工业传感器网络和其他不同流量的流量表现出高度的多样性和复杂性。这不仅给网络资源管理带来巨大挑战,也对网络空间安全构成严重威胁。为了解决这一问题,我们提出了基于视觉变换(ViT)的网络流量分类模型Bimodal TrafficNet,通过融合两种模式信息、交通图像和统计特征来提高模型的分类精度和泛化能力。该模型包含两个核心分支:交通图像分支(I-Branch)和统计特征分支(F-Branch)。前者侧重于捕获网络流量的详细特征,而后者侧重于流量的全局行为,并使用双峰交叉注意模块(BCA模块)加强两个分支之间的协同效应。此外,I-Branch还引入了像素级交互关注模块(PLIA),进一步优化网络流量图像特征的表示。实验结果表明,与现有方法相比,Bimodal TrafficNet在Edge-IIoTset、CICIoT2022、ISCXVPN2016和USTC-TFC2016四个公共数据集上表现最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic through two lenses: A dual-branch vision transformer for IoT traffic classification
Internet of Things (IoT) traffic classification identifies different communication activities by analyzing network traffic to ensure efficient and reliable Quality of Service (QoS) for businesses. However, with the dramatic increase in IoT devices, traffic from smart homes, industrial sensor networks, and other diverse traffic exhibits a high degree of diversity and complexity. This not only brings great challenges to network resource management, but also poses a serious threat to cyberspace security. To address this challenge, we propose Bimodal TrafficNet, a network traffic classification model based on Vision Transformer (ViT), to improve the classification accuracy and generalization ability of the model by fusing two modal information, traffic images and statistical features. The model contains two core branches: a traffic image branch (I-Branch) and a statistical feature branch (F-Branch). The former focuses on capturing the detailed features of network traffic, whereas the latter focuses on the global behavior of traffic and strengthens the synergistic effect between the two branches using a Bimodal Cross-Attention module (BCA module). In addition, the I-Branch introduces a Pixel-Level Interactive Attention module (PLIA module) to further optimize the representation of network traffic image features. The experimental results show that Bimodal TrafficNet performs best on four public datasets: Edge-IIoTset, CICIoT2022, ISCXVPN2016, and USTC-TFC2016, compared to existing methods.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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