This paper reviews the use of generative models, particularly generative adversarial networks (GANs), in generating synthetic attack data for cybersecurity. The authors highlight the challenges in obtaining large datasets for training deep learning models and the need for synthetic data due to privacy concerns. GANs are praised for their ability to generate diverse and realistic synthetic data, but their efficacy in generating realistic cyberattack data remains a subject of investigation. The paper explores the capabilities of generative models, provides an overview of GANs, and reviews various methods for generating synthetic cyberattack data. It also assesses the quality of synthetic attack data through experiments with the NSL-KDD dataset, focusing on the characteristics of DoS attacks and the effectiveness of GAN-generated data in improving intrusion detection systems. The paper concludes by discussing the potential of synthetic data in enhancing deep learning models for robust cybersecurity applications.This paper reviews the use of generative models, particularly generative adversarial networks (GANs), in generating synthetic attack data for cybersecurity. The authors highlight the challenges in obtaining large datasets for training deep learning models and the need for synthetic data due to privacy concerns. GANs are praised for their ability to generate diverse and realistic synthetic data, but their efficacy in generating realistic cyberattack data remains a subject of investigation. The paper explores the capabilities of generative models, provides an overview of GANs, and reviews various methods for generating synthetic cyberattack data. It also assesses the quality of synthetic attack data through experiments with the NSL-KDD dataset, focusing on the characteristics of DoS attacks and the effectiveness of GAN-generated data in improving intrusion detection systems. The paper concludes by discussing the potential of synthetic data in enhancing deep learning models for robust cybersecurity applications.