Data Augmentation for Intrusion Detection Using VAE-WGAN-GP

A recent study by Yahui Wang and Zhiyong Zhang addresses the challenge of class imbalance in intrusion detection systems, particularly where attack data is scarce. The authors propose a novel data augmentation model that combines Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) with Gradient Penalty (GP). This model effectively generates synthetic minority class data to balance datasets, which enhances the performance of multi-class intrusion detection classifiers. The study demonstrates significant improvements when applied to both traditional internet datasets and industrial control system networks. The findings are crucial for organizations relying on deep learning for cybersecurity, as they highlight a method to mitigate the risks associated with imbalanced datasets. The research was published in the Academic Journal of Computing & Information Science on May 17, 2026.

Key Points: • The study introduces a VAE-WGAN-GP model for data augmentation in intrusion detection. • Class imbalance in datasets can lead to increased classification errors for attack data. • The proposed method shows significant improvements in detection accuracy across multiple classifiers.