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To combat severe out-of-distribution channel shifts in 6G non-terrestrial networks, this work establishes a distributed physical-layer authentication framework that integrates multi-domain physical fingerprints with causal meta-learning. Grounding adaptation in a structural causal model that couples Model-Agnostic Meta-Learning with Invariant Risk Minimization, the architecture enables rapid, few-shot policy transfer across non-stationary aerial environments. Evaluated across diverse deployment zones, the framework attains 92% accuracy and 96% AUC while substantially curbing backhaul overhead through an on-demand graph-attention localization trigger.
Extreme distribution shifts in non-terrestrial networks can be mastered with few-shot adaptation by enforcing causal invariance over distributed physical fingerprints, achieving 96% AUC in previously unseen deployment environments.
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.