Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

• URL: http://arxiv.org/abs/2604.20483v2
• Date: Thu, 23 Apr 2026 12:47:14 GMT
• Title: Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
• Abstract summary: We propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow)<n>We use sliding-windows to split the network traffic in equal-sized heterogeneous bidirectional graphs containing IP, Port, and Connection nodes.<n>Our approach shows superior results when identifying the Port and IP to which connections attach, while feature reconstruction remains competitive.
• Score: 2.4159082914715495
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 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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