A heterogeneous VGAE with masking, edge dropping, and negative sampling flags anomalous network connections via a weighted reconstruction/regularization score, matching Anomal-E F1 with fewer false positives but lower recall, and faster inference.
Network intrusion datasets: A survey, limitations, and recommendations
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AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders
A heterogeneous VGAE with masking, edge dropping, and negative sampling flags anomalous network connections via a weighted reconstruction/regularization score, matching Anomal-E F1 with fewer false positives but lower recall, and faster inference.