This paper reviews generative models for generating synthetic attack data in cybersecurity. Deep learning has become essential in cybersecurity due to its ability to process large datasets and detect malicious patterns. However, obtaining sufficient real-world data for training is challenging due to privacy and security concerns. Generative adversarial networks (GANs) have emerged as a promising solution for generating synthetic data, particularly in cybersecurity. While GANs are widely used, their effectiveness in generating realistic cyberattack data remains under investigation. The paper explores the capabilities of generative models, focusing on their data generation abilities and how they compare to discriminative models. It provides a comprehensive review of GANs, their architecture, and various methods for generating synthetic cyberattack data. The paper also assesses the value of synthetically generated attack data by conducting experiments with the NSL-KDD dataset. The study highlights the potential of synthetic data in improving the training of intrusion detection systems for real-world cyberattack mitigation. The paper discusses different modeling techniques, including generative and discriminative models, and their applications in cybersecurity. It also examines the advantages of generative models, such as their ability to learn underlying causal factors and use distributed representations to identify independent features. The paper concludes that generative models, particularly GANs, offer a promising approach for generating realistic synthetic attack data to enhance cybersecurity applications.This paper reviews generative models for generating synthetic attack data in cybersecurity. Deep learning has become essential in cybersecurity due to its ability to process large datasets and detect malicious patterns. However, obtaining sufficient real-world data for training is challenging due to privacy and security concerns. Generative adversarial networks (GANs) have emerged as a promising solution for generating synthetic data, particularly in cybersecurity. While GANs are widely used, their effectiveness in generating realistic cyberattack data remains under investigation. The paper explores the capabilities of generative models, focusing on their data generation abilities and how they compare to discriminative models. It provides a comprehensive review of GANs, their architecture, and various methods for generating synthetic cyberattack data. The paper also assesses the value of synthetically generated attack data by conducting experiments with the NSL-KDD dataset. The study highlights the potential of synthetic data in improving the training of intrusion detection systems for real-world cyberattack mitigation. The paper discusses different modeling techniques, including generative and discriminative models, and their applications in cybersecurity. It also examines the advantages of generative models, such as their ability to learn underlying causal factors and use distributed representations to identify independent features. The paper concludes that generative models, particularly GANs, offer a promising approach for generating realistic synthetic attack data to enhance cybersecurity applications.