Tabular representation learning for network intrusion detection exhibits strong dataset-model dependency, with supervised methods outperforming unsupervised anomaly detection and limited but possible cross-dataset generalization.
Tabu- lar data contrastive learning via class-conditioned and feature-correlation based augmentation
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Evaluating Tabular Representation Learning for Network Intrusion Detection
Tabular representation learning for network intrusion detection exhibits strong dataset-model dependency, with supervised methods outperforming unsupervised anomaly detection and limited but possible cross-dataset generalization.