Physical Layer Authentication Based on Hierarchical Variational Auto-Encoder for Industrial Internet of Things

TL;DR

This paper introduces HVAE, a novel hierarchical variational auto-encoder-based physical layer authentication scheme for IIoT that performs well with limited training data and no prior attacker channel information.

Contribution

The paper proposes HVAE, a new channel impulse response-based PLA scheme that does not require prior attacker information and is effective in complex IIoT environments.

Findings

HVAE outperforms three comparison schemes in static and mobile scenarios.

HVAE maintains high authentication accuracy with limited training data.

The new objective function effectively models complex channel distributions.

Abstract

Recently, Physical Layer Authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication. In the complex environment, such as the Industrial Internet of Things (IIoT), machine learning (ML) is widely employed with PLA to extract and analyze complex channel characteristics for identity authentication. However, most PLA schemes for IIoT require attackers' prior channel information, leading to severe performance degradation when the source of the received signals is unknown in the training stage. Thus, a channel impulse response (CIR)-based PLA scheme named "Hierarchical Variational Auto-Encoder (HVAE)" for IIoT is proposed in this article, aiming at achieving high authentication performance without knowing attackers' prior channel information even when trained on a few data in the complex environment. HVAE consists of an Auto-Encoder (AE) module for CIR characteristics extraction and a Variational Auto-Encoder (VAE) module for improving the representation ability of the CIR characteristic and outputting the authentication results. Besides, a new objective function is constructed in which both the single-peak and the double-peak Gaussian distribution are taken into consideration in the VAE module. Moreover, the simulations are conducted under the static and mobile IIoT scenario, which verify the superiority of the proposed HVAE over three comparison PLA schemes even with a few training data.