GAN-Based Artificial Noise Generation Against Eavesdropping for Wireless Secret Key Generation in Dynamic Indoor LiFi Networks

Abstract

This paper investigates the vulnerability of wireless secret key generation (WSKG) to eavesdropping by analyzing the channel impulse response (CIR) between a legitimate user and an eavesdropper under various scenarios in dynamic indoor light fidelity (LiFi) networks. These scenarios include (a) different user densities (2, 4, and 8 users), (b) fields of view (FoVs) of$30^\circ$,$60^\circ$, and$90^\circ$, and (c) various room layouts. Results show that higher user densities increase downlink CIR similarity, as users' movement traces become closer. For instance, with eight users and a$30^\circ$FoV, CIR similarity peaks at over 85%–95% during entering and exiting stages and remains no less than 40% during wandering. Consequently, an eavesdropper can generate a key with 27%–37% similarity to the legitimate user's key. To mitigate this threat, we propose a novel defense using a generative adversarial network (GAN) trained with crafted uplink CIRs. GANs model the complex statistical properties of legitimate CIRs and generate synthetic noise that mimics environmental and system characteristics without replicating real user CIRs. This prevents eavesdroppers from extracting useful information and avoids privacy concerns linked to handling actual CIR data. Furthermore, traditional reversed CIR methods are less effective in dynamic environments, where conditions change rapidly and are easier to reverse-engineer. Our GAN-generated noise, applied within the defense zone of the legitimate user, reduces CIR similarities from up to 95% to approximately 1%, effectively nullifying key leakage. These findings highlight the potential of GAN-based noise to significantly enhance WSKG security in dynamic indoor LiFi networks.