# Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning

arXiv: https://arxiv.org/abs/2609.09511 · HTML: https://mlsanity.com/paper/2609.09511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023)
- [Physical Layer Authentication and Security Design in the Machine Learning Era](https://mlsanity.com/paper/2305.09748/) (2023)
- [Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints](https://mlsanity.com/paper/2504.04829/) (2025)
- [Physical Layer Challenge-Response Authentication between Ambient Backscatter Devices](https://mlsanity.com/paper/2506.18767/) (2025)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices](https://mlsanity.com/paper/2603.19972/) (2026)
- [APEG: Adaptive Physical Layer Authentication with Channel Extrapolation and Generative AI](https://mlsanity.com/paper/2603.21923/) (2026)
- [Impact of Phase Noise and Power Amplifier Non-Linearities on Downlink Cell-Free Massive MIMO-OFDM Systems](https://mlsanity.com/paper/2507.21635/) (2025)
- [Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario](https://mlsanity.com/paper/2512.16648/) (2025)
- [Causal Neighbourhood Learning for Invariant Graph Representations](https://mlsanity.com/paper/2602.17934/) (2026)

## Abstract (arXiv)

Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade conventional learning methods. Existing PLA schemes often rely on single features or generalize poorly to unseen environments. This paper proposes a secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a causal meta-learning framework for distributed PLA (DPLA) in NTNs. First, we design a multi-feature fingerprint that combines spatial, angular, combiner, subspace, and Doppler-delay features. The fingerprint is adaptive and distributed, as it fuses heterogeneous physical-layer features and measurements from multiple aerial nodes. Second, we formulate a structural causal model (SCM) to capture the relations among design choices, environmental factors, extracted features, and authentication outcomes. Third, we develop a model-agnostic meta-learning (MAML) strategy with invariant risk minimization (IRM) and causal consistency regularization for fast adaptation to unseen NTN environments with few labeled samples. Fourth, we propose a two-stage authentication scheme that performs local recognition and activates time-difference-of-arrival (TDOA) localization with a graph attention (GAT) network only when needed, which reduces backhaul overhead. Simulations show that SAFA-MZ achieves 92% accuracy and 96% AUC, outperforming centralized deep learning and single-feature baselines across diverse environments.