Open access
Sep 2026
A Hybrid SMOTE-CTGAN and VAE-LSTM Framework for Interpretable Intrusion Detection in Imbalanced Network Traffic
A hybrid framework that integrates SMOTE and CTGAN for minority-class augmentation, a Bidirectional Long Short-Term Memory network for supervised traffic classification, and a benign-trained Variational Autoencoder (VAE) for validating low-confidence predictions is proposed.
Felicia Maake, Justice Nkoana, V. Baloyi et al.
· Big Data and Cognitive Compu... · 0 citations