A knowledge-distilled graph attention student, trained on VGAE-selected samples, is claimed to improve CAN intrusion detection F1 by 16.2% on average and up to 55% on imbalanced datasets.
MSR- GCN: Multi-scale residual graph convolution networks for human motion prediction
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Multi-Stage Knowledge-Distilled VGAE and GAT for Robust Controller-Area-Network Intrusion Detection
A knowledge-distilled graph attention student, trained on VGAE-selected samples, is claimed to improve CAN intrusion detection F1 by 16.2% on average and up to 55% on imbalanced datasets.