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：在本文中，我们提出了一种概念验证的图神经网络模型，能够通过精确建模图结构和连接特征，成功预测网络流级别的流量（NetFlow）。我们采用滑动窗口将网络流量分割为大小相等的异构图，包含IP、端口和连接节点。随后利用图神经网络对图结构的演变及连接特征进行建模。我们的方法在识别连接所附着的端口和IP方面表现出色，同时特征重建能力与强预测基线保持竞争力。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。