Data augmentation techniques show potential in various domains, yet their application to enhance robustness in wireless anomaly detection remains underexplored. Wireless datasets often suffer from anomaly scarcity and class imbalance, hindering the training of reliable detection models. This work introduces GANSec, a novel conditional Generative Adversarial Networks (GAN) framework specifically designed to augment wireless time-series data. We investigate different neural network architectures (MLP, LSTM, CNN) and two conditional training objectives (Embedded Conditional, Classification Oriented) within GANSec, evaluating the framework using real-world 5G measurements for jamming anomaly detection. For evaluation, we train the downstream anomaly detector exclusively on GANSec-generated data and test its performance in a cross-scenario setting. Our evaluation demonstrates that models trained this way significantly outperform those trained on original or baseline augmentation data when tested under unseen network conditions. Specifically, our approach achieved up to 92.13% accuracy on the unseen dataset (i.e., data collected from a different distribution reflecting network conditions distinct from the training set), compared to 78% for models trained on raw data and 83.33% for the best-performing baseline, exhibiting substantially enhanced robustness and generalization.