Adversarial deep learning for network anomaly detection utilizes generative adversarial networks (GANs) to enhance network security. By training on both genuine and adversarial data, it improves anomaly detection's resilience to advanced cyber threats. This paper explores the application of adversarial deep learning, emphasizing its importance in safeguarding sensitive data and maintaining operational continuity. It reviews relevant studies and presents a high-level methodology for GAN-based anomaly detection. The mathematical expressions highlight GAN training objectives, anomaly detection thresholding, and the F1-score as a performance metric. This paper provides an overview of the application of adversarial deep learning in network anomaly detection, highlighting its potential in addressing the challenges posed by modern cyber threats.