With the increased use of digital services post-COVID, security breaches have become more prevalent, emphasizing the need for robust security measures. Intrusion Detection and Prevention Systems (IDPSs) using deep learning techniques, particularly GANs, can be used to improve detection accuracy and response speed. The objective of this study is to enhance IDPS using GANs and deep learning techniques to improve detection precision and response time. By integrating GANs with SDN, the study aims to address the challenges posed by cyber threats and enhance network security. This paper also explores the applications of Generative Adversarial Networks (GANs) in Software-Defined Networking (SDN)-based IDPS. The study presents a comprehensive analysis of the effectiveness of deep learning methods, particularly GANs, in improving IDPS. It demonstrates how GANs can be used to detect anomalies and potential risks, enabling organizations to proactively protect their assets and clients. The study emphasizes how crucial it is to comprehend how SDNs affect network security, even though using SDN in IDPS presents certain difficulties, such as guaranteeing the SDN controller’s security.