This article presents a systematic mapping study and review of the application of Generative Machine Learning Models (GMLMs) to Intrusion Detection Systems (IDSs). The study explores how GMLMs can address challenges in IDSs, such as the lack of quality training data and high false-positive rates. It provides a systematic mapping study of research at the intersection of GMLMs and IDSs, along with a detailed review of the current state of research and directions for future work. The study identifies three main application areas of GMLMs in IDSs: (1) GMLMs for assisting with penetration testing, (2) GMLMs for supplementing IDS datasets, and (3) GMLMs as IDSs. The review discusses various GMLMs, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, and their applications in intrusion detection. It also analyzes evaluation metrics used in these applications and highlights the challenges and opportunities in using GMLMs for intrusion detection. The study finds that GMLMs can be used to generate synthetic data for penetration testing, improve IDS performance by supplementing datasets, and serve as IDSs themselves. However, challenges remain, including the lack of standardized evaluation metrics and the need for realistic synthetic data. The review also discusses the ethical implications of using GMLMs in penetration testing and the potential for future research in this area. The study concludes that GMLMs have significant potential in improving intrusion detection systems, but further research is needed to address the challenges and ensure the effectiveness and ethical use of these models.This article presents a systematic mapping study and review of the application of Generative Machine Learning Models (GMLMs) to Intrusion Detection Systems (IDSs). The study explores how GMLMs can address challenges in IDSs, such as the lack of quality training data and high false-positive rates. It provides a systematic mapping study of research at the intersection of GMLMs and IDSs, along with a detailed review of the current state of research and directions for future work. The study identifies three main application areas of GMLMs in IDSs: (1) GMLMs for assisting with penetration testing, (2) GMLMs for supplementing IDS datasets, and (3) GMLMs as IDSs. The review discusses various GMLMs, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, and their applications in intrusion detection. It also analyzes evaluation metrics used in these applications and highlights the challenges and opportunities in using GMLMs for intrusion detection. The study finds that GMLMs can be used to generate synthetic data for penetration testing, improve IDS performance by supplementing datasets, and serve as IDSs themselves. However, challenges remain, including the lack of standardized evaluation metrics and the need for realistic synthetic data. The review also discusses the ethical implications of using GMLMs in penetration testing and the potential for future research in this area. The study concludes that GMLMs have significant potential in improving intrusion detection systems, but further research is needed to address the challenges and ensure the effectiveness and ethical use of these models.