Advancing Adversarial Detection of Anomalous Wi-Fi Activity Through Residual Generative Adversarial Networks: A Novel Approach for Robust Wireless Network Security

This study aims to address the growing need for robust wireless network security by detecting anomalous Wi-Fi activity through a Residual Generative Adversarial Network (Res-GAN). Traditional methods for detecting Wi-Fi anomalies often struggle to distinguish subtle network behaviors, leading to undetected threats. In this research, the authors develop a novel Res-GAN architecture that combines generative adversarial techniques with residual learning to model and identify anomalous patterns within Wi-Fi traffic data accurately. The model is trained on labeled Wi-Fi signal datasets, where the generator learns to create realistic Wi-Fi activity while the discriminator discerns genuine from anomalous signals. Results demonstrate that Res-GAN achieves high detection accuracy and improves robustness against adversarial attempts to evade detection. The findings suggest that integrating residual connections within GAN architectures can significantly enhance the detection of Wi-Fi anomalies, presenting a scalable solution for real-time network security applications in wireless communication systems.