  This study proposes the implementation of Deep Generative Adversarial
Networks (GANs) for augmenting the NSL-KDD dataset. The primary objective is to
enhance the efficacy of eXtreme Gradient Boosting (XGBoost) in the
classification of cyber-attacks on the NSL-KDD dataset. As a result, the method
proposed in this research achieved an accuracy of 99.53% using the XGBoost
model without data augmentation with GAN, and 99.78% with data augmentation
using GAN.
