Presents a conceptual architecture and research agenda for explainable AI-assisted, bounded mitigation using eBPF/XDP at IoT edge gateways, without experimental results.
IEEE Access8, 165130–165150 (2020)
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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.
citing papers explorer
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A Deployment-Oriented Framework for Explainable AI-Assisted eBPF/XDP Mitigation at the IoT Edge
Presents a conceptual architecture and research agenda for explainable AI-assisted, bounded mitigation using eBPF/XDP at IoT edge gateways, without experimental results.
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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.