Transmitter Fingerprinting for VLC Systems via Deep Feature Separation Network

Abstract

Visible light communication (VLC) is a promising technology with a high data rate that can supplement radio frequency communication. Although VLC systems have a natural advantage of high security due to the line-of-sight light propagation characteristic, they are still vulnerable when facing an open environment. Device fingerprinting is a technique that is widely viewed to detect transmitter impersonation attack in radio frequency (RF) based wireless systems. In this paper, we introduce the fingerprinting technique to discriminate illegal transmitting devices in VLC systems. We first investigate the hardware imperfections of the VLC transmitter, which can provide a unique device ID. Then we implement a feature separation network for transmitter fingerprinting (TF-FSN) and design a two-stage training strategy to obtain a stable classifier. Finally, we experimentally demonstrate the feasibility and performance of the proposed method. The results show that the accuracy of identification and verification is 92.65% and 98%, respectively. Moreover, our method is robust over different distances and a wide range of signal-to-noise ratios.

Keywords

• Physical layer security
• visible light communication
• transmitter fingerprinting
• deep learning