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This study proposes the implementation of Deep Generative AdversarialNetworks (GANs) for augmenting the NSL-KDD dataset. The primary objective is toenhance the efficacy of eXtreme Gradient Boosting (XGBoost) in theclassification of cyber-attacks on the NSL-KDD dataset. As a result, the methodproposed in this research achieved an accuracy of 99.53% using the XGBoostmodel without data augmentation with GAN, and 99.78% with data augmentationusing GAN.

Analisis Eksploratif Dan Augmentasi Data NSL-KDD Menggunakan Deep  Generative Adversarial Networks Untuk Meningkatkan Performa Algoritma Extreme  Gradient Boosting Dalam Klasifikasi Jenis Serangan Siber