用图神经网络预测网络流量，精准识别连接的端口和IP。
Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
- 构建滑动窗口的异构双向图，融合IP、端口和连接节点
- 在端口与IP归属预测上优于主流基线模型
- 适合网络异常检测与流量管理场景
本文提出一种基于图神经网络的流量预测方法，通过滑动窗口将网络流量划分为包含IP、端口和连接节点的异构双向图，利用GNN建模图结构与连接特征的演化过程。实验表明，该方法在识别连接所属端口和IP方面表现优异，特征重建性能也与强基线模型相当。整体工作展示了图神经网络在逐流网络流量预测中的可行性与有效性。
原文摘要 · Abstract (English)
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 sliding-windows to split the network traffic in equal-sized heterogeneous bidirectional graphs containing IP, Port, and Connection nodes. We then use the GNN to model the evolution of the graph structure and the connection features. Our approach shows superior results when identifying the Port and IP to which connections attach, while feature reconstruction remains competitive with strong forecasting baselines. Overall, our work showcases the use of GNNs for per-flow NetFlow prediction.
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