Spiking Neural Network: a low power solution for physical layer authentication

TL;DR

This paper explores the use of energy-efficient Spiking Neural Networks for physical layer device authentication in wireless communication, demonstrating their ability to learn device fingerprints and defend against adversarial attacks.

Contribution

It introduces the application of SNNs for RF transmitter identification and proposes an autoencoder-based method to enhance their robustness against adversarial attacks.

Findings

SNNs can effectively learn RF device fingerprints.

SNNs are vulnerable to adversarial attacks.

Autoencoders can mitigate adversarial perturbations.

Abstract

Deep learning (DL) is a powerful tool that can solve complex problems, and thus, it seems natural to assume that DL can be used to enhance the security of wireless communication. However, deploying DL models to edge devices in wireless networks is challenging, as they require significant amounts of computing and power resources. Notably, Spiking Neural Networks (SNNs) are known to be efficient in terms of power consumption, meaning they can be an alternative platform for DL models for edge devices. In this study, we ask if SNNs can be used in physical layer authentication. Our evaluation suggests that SNNs can learn unique physical properties (i.e., `fingerprints') of RF transmitters and use them to identify individual devices. Furthermore, we find that SNNs are also vulnerable to adversarial attacks and that an autoencoder can be used clean out adversarial perturbations to harden SNNs against them.