The improvement of IoT security solutions is nowadays more current and urgent because of the large masses of vulnerabilities, cyber attacks, data theft, and other threats related to the use of IoT devices. Most IoT datasets are imbalanced, as benign traffic dominates while malicious traffic is scarce. Additionally, IoT traffic is sensitive and rarely available for public research. Generating realistic synthetic data is essential for overcoming these limitations. Our study focuses on Deep Learning models for network intrusion detection by implementing Tabular Generative Adversarial Networks (TGAN) to address class imbalance. GANs help by increasing the proportion of rare malware samples, improving model training and detection accuracy. In this paper, we rely on the UNSW-NB15 and NSL-KDD datasets to address the issue of imbalanced classes. We propose a new approach that we called FS-TGAN, which is based on feature selection methods and TGAN model for samples generating. The time is a crucial parameter for security tools such as IDS and antivirus, where a lot of data must be analyzed to look for malware, anomalies, or anything suspicious that might be trying to penetrate the system. For this purpose, we reduced the number of features to eliminate redundant features or those that are highly correlated. The results show that TGAN performs well. We achieved 99.03% of accuracy with the UNSW-NB15 dataset, demonstrating a significantly reduced error rate by learning to provide new unseen data that share the training set’s statistics, with a detection time of 0.230 ms per traffic set.