Pérez-Jove, RubénMunteanu, Cristian-RobertDorado, JuliánPazos, A.Vázquez-Naya, José2026-02-052026-02-052026-01-12R. Pérez-Jove, C. R. Munteanu, J. Dorado, A. Pazos, and J. Vázquez-Naya, "Network traffic foundation models: A systematic review", Computer Networks, Vol. 276, Feb. 2026, 111998, https://doi-org.accedys.udc.es/10.1016/j.comnet.2026.1119981872-7069https://hdl.handle.net/2183/47246Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Network Traffic Foundation Models (NT-FMs) aim to enable a “train-once, adapt-anywhere” paradigm for automated traffic management, yet current research remains fragmented. This systematic literature review analyses 51 primary studies published between 2017 and 2025. To evaluate the feasibility of developing NT-FMs, we address 4 research questions: (RQ1) we map prevailing AI architectures and their training regimes, including self-supervised pretraining objectives; (RQ2) we assess how network traffic is represented for input to NT-FMs; (RQ3) we examine the network environments and tasks to which NT-FMs have been applied; and (RQ4) we survey publicly available datasets suitable for training NT-FMs. Our content and bibliometric analyses indicate a rapidly expanding body of work, yet cross-task transfer remains limited; terabyte-scale corpora are scarce; and efficiency techniques are only beginning to emerge. Taken together, these findings indicate that NT-FM development is feasible but currently constrained by data scale and transfer limitations. We distil open challenges and propose a research agenda aimed at scalable, robust, and reliable NT-FMs.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Fine-tuningFoundation modelsNetwork managementNetwork securityNetwork trafficPretrainingSelf-supervised learningjournal articleopen access10.1016/j.comnet.2026.111998