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.
IEEE Transactions on Intelligent Transportation Systems23(3), 2523–2537 (2022)
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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.