Analisis Eksploratif Dan Augmentasi Data NSL-KDD Menggunakan Deep Generative Adversarial Networks Untuk Meningkatkan Performa Algoritma Extreme Gradient Boosting Dalam Klasifikasi Jenis Serangan Siber
This work addresses cyber-attack classification for cybersecurity applications, but it is incremental as it applies an existing method (GANs) to a specific dataset.
The study tackled the problem of improving cyber-attack classification on the NSL-KDD dataset by using Deep Generative Adversarial Networks (GANs) for data augmentation, resulting in an accuracy increase from 99.53% to 99.78% with XGBoost.
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.