Generative AI for Physical-Layer Authentication

TL;DR

This work tackles the challenge of secure and lightweight authentication in next-generation wireless networks by leveraging Generative AI to enhance Physical-Layer Authentication (PLA). It develops a three-stage taxonomy—fingerprint collection, model training, and performance optimization—and proposes a channel-extrapolation based PLA framework to operate in dynamic environments, validated by a Generative Diffusion Model (GDM) case study. The results demonstrate that GAI-based PLA can improve fingerprint estimation, robustness to noise, and open-set multiuser authentication, while reducing labeling needs and enabling efficient deployment. The paper also outlines a forward-looking roadmap covering reliable fingerprint design, lightweight architectures, generalization, security, and industrialization to enable practical adoption of GAI-enhanced PLA in 6G and beyond.

Abstract

Recently, Artificial Intelligence (AI)-driven Physical-Layer Authentication (PLA), which focuses on achieving endogenous security and intelligent identity authentication, has attracted considerable interest. When compared with Discriminative AI (DAI), Generative AI (GAI) offers several advantages, such as fingerprint augmentation, reconstruction, and denoising. Inspired by these innovations, this paper provides a systematic exploration of GAI's integration with PLA. We commence with a concise review of identity authentication techniques and GAI models. Then, we contrast the limitations of DAI with the potential of GAI in addressing PLA challenges. Specifically, we introduce a structured taxonomy for GAI-enhanced PLA methodologies, encompassing three key stages: fingerprint collection, model training, and performance optimization within the PLA pipeline. Furthermore, we propose a novel PLA framework based on GAI and channel extrapolation for dynamic environments. To demonstrate GAI's efficacy in enhancing PLA robustness, we implement a case study using the Generative Diffusion Model (GDM). Finally, we outline potential future research directions for GAI-based PLA.

Figures (4)

• Figure 1: Illustration of GAI-enhanced methods for PLA, where Part (A) denotes representative GAI models, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE), Generative Diffusion Model (GDM), and Transformer, Part (B) denotes GAI for fingerprint collection phase, including fingerprint estimation, fingerprint augmentation, fingerprint filling, and fingerprint transmission, Part (C) denotes GAI for PLA model training, including attack detection, multiuser authentication, and channel knowledge map, and Part (D) denotes GAI for PLA performance optimization, including flexibility for eliminated legitimate users, defense against intelligent attacks, and adaptation for different scenarios.
• Figure 2: Illustration of the proposed GAI-enhanced PLA framework for dynamic environments, where Alice is a legitimate device that requires identity authentication performed at Bob, Jack is a collaborative device, and Eve is a spoofing attacker impersonating Alice. Part (A) denotes the offline training step including channel fingerprint pre-processing and joint distribution learning, and Part (B) denotes the online authentication step including dynamic fingerprint prediction and identity authentication.
• Figure 3: Illustration of GDMs used for CSI fingerprint generation and prediction. To preserve the phase integrity of complex CSI fingerprints, we compute the dynamic ranges of the real and imaginary components as the differences between their maxima and minima, and then use the larger of these two ranges as a common normalization factor to linearly map both the real and imaginary values into the interval $[-1,1]$. Part (A) denotes the training process of the GDM-based fingerprint generation, including the forward and denoising processes, and Part (B) denotes the visual denoising process for DeepMIMO "O1" outdoor and "I3" indoor CSI fingerprint datasets.
• Figure 4: Comparison results between the proposed GDM-based PLA scheme and four baseline schemes. The proposed scheme utilizes GDMs with a cross-attention mechanism to generate Alice's fingerprint at time $T$ based on Jack's fingerprint at time $T$, and compare it with the fingerprint to be authenticated at time $T$. Baseline Scheme 1: The VAE model with a cross-attention mechanism, is used to predict Alice's fingerprint at time $T$ and compare it with the fingerprint to be authenticated at time $T$. Baseline Scheme 2: The typical time series model, Long Short-Term Memory (LSTM) model, is used to predict Alice's fingerprint at time $T$ and compare it with the fingerprint to be authenticated at time $T$. Baseline Scheme 3: The fingerprint to be authenticated at time $T$ is compared with Jack's fingerprint at time $T$. Baseline Scheme 4:The Gated Recurrent Unit (GRU) model, is used to predict Alice's fingerprint at time $T$ and compare it with the fingerprint to be authenticated at time $T$. Part (A) uses $F_1=(2P_{tl}P_{ta})/(P_{tl}+P_{ta}) \in[0,1]$ to evaluate the authentication performance, where $P_{tl}$ and $P_{ta}$ respectively represent correct authentication rate and false alarm rate meng2023physical. A larger $F_1$ means better authentication performance. Part (B) employs authentication error rate $R_e\in[0,1]$ as the metric, and a smaller $R_e$ means better authentication performance.