Unauthorized commercial drone activity around critical infrastructure motivates the development of trajectory-level anomaly detection. We present a rigorous benchmarking study of variational autoencoder methods for drone trajectory anomaly detection in a simulated nuclear facility protection scenario, evaluating six methods (bidirectional LSTM-VAE, unidirectional LSTM-VAE, fully connected VAE, standard autoencoder, One-Class SVM, Isolation Forest) on 2500 trajectories using identical raw features and training pipelines. Across five random seeds, all VAE variants achieve AUC-ROC of approximately 0.92 versus 0.73 to 0.80 for the non-VAE baselines, isolating variational regularization rather than bidirectionality or temporal encoding alone as the dominant performance driver in this domain. Building on this benchmark, we propose a domain-aware LSTM-VAE incorporating two facility-specific architectural elements: a polar coordinate input representation expressing trajectories relative to the protected facility and a distance-weighted reconstruction loss that allocates model capacity toward near-facility timesteps. The domain-aware variant achieves AUC-ROC of 0.962 ± 0.007 on the original test set and 0.973 ± 0.005 on an augmented hard anomalies test set, a 3 to 4 percentage-point improvement over generic VAE methods at no additional parameter cost. A bootstrap evaluation under 99:1 class imbalance confirms that the domain-aware variant maintains its precision advantage at low false positive rate operating points.

Networks of fixed, low-cost dose-rate sensors monitor radiological facilities, but the trustworthiness of their reports rests on heuristics: existing trust schemes score nodes by mutual voting or behavioral statistics, which fail against an adversary who falsifies data consistently with the physics the network can observe. This paper presents PRAHARI, a trust management framework in which an unmanned aerial system carrying a calibrated scintillator chain hovers at each node as a mobile metrological anchor. Trust evidence is a statistically principled compatibility test between two uncertainty-quantified measurements with an empirically verified false-alarm rate, feeding a multi-channel Beta reputation engine with wall-clock forgetting, endurance-constrained verification scheduling, and trust-gated dose mapping. Across 9640 paired Monte Carlo trials on a shielded-facility twin with randomized attack classes, PRAHARI is compared against three published network-internal trust methods, a physics-model residual detector, and an uncertainty-blind aerial band. Only aerial-reference evidence discriminates scaling and physics-consistent falsifiers (class AUC 0.80 and 0.75), while network-internal methods stay near chance (0.48 to 0.60), an advantage persisting across facility variants and up to half the adversary’s shielding knowledge, at a false-accusation rate one order of magnitude below every alternative of comparable ranking power. Sensitivity, ablation, reference-bias, and drift studies bound the claims.