English(EN) Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

图神经网络模型预测个体网络流量流

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了一种图神经网络（GNN）模型，能够预测个体流量级别（NetFlow）的网络流量。该模型能有效捕捉网络数据中的图结构和连接特征，在识别与连接相关的特定端口和IP地址方面优于现有预测方法。该方法展示了GNN在详细NetFlow预测方面的潜力。 AI

影响这项研究展示了图神经网络在细粒度网络流量预测方面的新颖应用，有望改善网络管理和安全。

排序理由该集群包含一篇学术论文，详细介绍了一种新的网络流量预测模型。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Georgios Anyfantis, Pere Barlet-Ros · 2026-09-17 04:00

使用预测掩码图自编码器预测个体NetFlows

arXiv:2604.20483v3 Announce Type: replace-cross Abstract: In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structure and the connection features. We use sli…