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.

Siber is the buff, blue-eyed and bald principal of the Buyonic Insurance Agency in Austin, Texas. This insurance android is more evidence that the future of mechanized businesses has arrived, with robots marching out of computer backrooms and off assembly lines right onto the frontlines of the service economy. Recent studies have suggested that a quarter of insurance jobs could be replaced by robots over the next decade. Buyonic is not one of the virtual or online agencies vying for business on the cloud today. Rather, it's an old-fashioned, Main Street brick-and-mortar retail shop where customers actually show up in person.