Generative adversarial network framework based security enhancement in free-space optical networks

Free-space optical (FSO) communication is a promising key technology for large bandwidth, high data rate and cost effective data transmission. However, FSO systems experiences crucial challenges under atmospheric turbulence, pointing errors and eavesdropping threats. The proposed machine learning framework uses generative adversarial networks (GANs) for eavesdropping threats and malicious intrusions to improve the security. The GAN based framework influences a generative model to simulate attacks, such as eavesdropping and jamming, whereas the adversarial model learns to identify and mitigate these threats in real time. By continuously adapting these strategies, the GAN framework enhances the robustness of the FSO communication link. Experimental results show that the proposed framework minimizes interception threats.