Anomaly Detection in Unmanned Aerial Vehicle Telemetry Using Automated Machine Learning

Reliable analysis of UAV telemetry data is critical for mission safety, especially as drones are increasingly deployed in complex and high-risk environments. These data streams often include anomalies arising from sensor faults, environmental disruptions, or cyber-physical attacks, making robust anomaly detection essential. This study introduces an unsupervised anomaly detection framework designed specifically for high-frequency UAV telemetry. It combines domain-driven feature engineering with an AutoML-based optimization pipeline that enables automated model selection and hyperparameter tuning. The framework integrates four unsupervised algorithms—Local Outlier Factor, Isolation Forest, One-Class SVM, and Elliptic Envelope—ensuring adaptability to the dynamic nature of UAV operations. Evaluated on a real-world dataset of 127,000 samples from 48 UAV missions, the system uses expert-labeled anomaly segments solely for validation to preserve the integrity of unsupervised learning. Among all methods, Local Outlier Factor yielded the best results with 0.920 accuracy, 0.880 precision, 0.850 recall, and 0.860 F1-score. Scalable and low-latency, the proposed solution is well-suited for real-time deployment. By bridging theoretical advances with operational needs, this work contributes to safer and more resilient aerial robotic systems.