LSTM-VAE for Temporal Anomaly Detection in Drone Trajectory Analysis: A Comparative Study for Critical Infrastructure Protection

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.