The Use of SNN for Ultralow-Power RF Fingerprinting Identification With Attention Mechanisms in VDES-SAT

Abstract

VHF Data Exchange System (VDES) is explored as a navigational safety guarantor for ships worldwide. As the number of jammers and spoofers targeting VDES is on the rise, its security authentication emerges as an urgent issue. Radio frequency fingerprinting identification (RFFI) as a noncryptographic physical layer security solution can effectively defend against increasingly sophisticated attacks. Nevertheless, the power consumption of frequent RFFI is not negligible for VDES satellite components with energy constraints. To this end, spiking neural networks (SNNs) are first employed to build an ultra-low power RF fingerprinting identification (RFFI) system for VDES, where the optimized spiking neurons can yield high accuracy with very few spikes. Moreover, multiple RF fingerprinting features are fused to highly integrate the information of the VDES signal in different dimensions, and the attention mechanism is utilized to further improve RFFI performance. Numerical results show that our SNN-based RFFI system can yield identification accuracy up to 92:59% at a signal-to-noise ratio (SNR) of 25 dB on VDES datasets, and reduces power consumption by 64% compared to artificial neural networks (ANNs) of comparable accuracy on 45 nm CMOS process.