Adding class-specific batch normalization to a conditional variational autoencoder improves synthetic minority-class network traffic generation enough to nudge a Decision Tree NIDS F1-score from 72.74% to 78.19% on NSL-KDD.
Intrusion detection system after data augmentation schemes based on the vae and cvae
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$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Adding class-specific batch normalization to a conditional variational autoencoder improves synthetic minority-class network traffic generation enough to nudge a Decision Tree NIDS F1-score from 72.74% to 78.19% on NSL-KDD.