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.

翻译：本文提出了一种概念验证型图神经网络模型，通过精确建模图结构与连接特征，成功实现了网络流级流量（NetFlow）的预测。我们采用滑动窗口将网络流量切分为等长的异质双向图，其中包含IP节点、端口节点和连接节点。随后利用图神经网络对图结构演化及连接特征进行建模。在识别连接所对应的端口与IP地址时，本方法展现出优越性能，而特征重构能力也与强时间序列基准方法保持竞争力。总体而言，本工作展示了图神经网络在逐流NetFlow预测中的应用。