This study tackles the urgent issue of network safety in today’s ever-changing cybersecurity scene. Regular intrusion detection systems (IDS) often can’t keep up with the quick progress of cyberthreats. To make IDS stronger against these attacks, this research brings in a new way by adding generative adversarial networks (GANs) to the detection setup. Using the KDD Cup 1999 dataset, a common standard in this field, the team does thorough data prep and digging. They build a complex GAN structure with a generator and discriminator letting the system learn to spot both real and fake patterns in network traffic. They check how well this GAN-based IDS works by testing it on the dataset looking at key measures like F1-score, precision, accuracy, and recall at different decision points. The study helps the field by showing how GANs can be used in network safety and stresses the need to think outside the box to beef up intrusion detection systems. What they found paves the way to make cybersecurity better in the future highlighting the need to have defense systems that can adapt to new sneaky tactics.