Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

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.

Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)

arXiv:2604.20483 [cs.NI]

(or arXiv:2604.20483v3 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2604.20483 Focus to learn more arXiv-issued DOI via DataCite

Proc. IEEE MNC, 2026, pp. 1-6

https://doi.org/10.1109/MNC69143.2026.11680744 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.