TabPFNv2.5 delivers 40x faster inference than Random Forest at 97% binary accuracy on TON IoT data, enabling a hybrid pipeline for real-time IoT threat screening in smart cities.
Journal of Net- work and Systems Management29, 1–18 (2021)
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Optimizing IoT Intrusion Detection with Tabular Foundation Models for Smart City Forensics
TabPFNv2.5 delivers 40x faster inference than Random Forest at 97% binary accuracy on TON IoT data, enabling a hybrid pipeline for real-time IoT threat screening in smart cities.