AutoGraphAD applies a heterogeneous variational graph autoencoder with unsupervised and contrastive learning to detect network anomalies on connection-IP graphs without labeled data, achieving comparable performance to Anomal-E with over an order of magnitude faster training and inference.
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AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders
AutoGraphAD applies a heterogeneous variational graph autoencoder with unsupervised and contrastive learning to detect network anomalies on connection-IP graphs without labeled data, achieving comparable performance to Anomal-E with over an order of magnitude faster training and inference.