Unsupervised Machine Learning for Intrusion Detection of Cyber-Attacks in Unmanned Aerial Vehicles
Open Access DepositedThis paper presents a reliable and extensible ensemble framework for unsupervised anomaly detection in Unmanned Aerial Vehicles (UAVs). This framework aims at detecting cyber-attacks without relying on labeled data for training. Using the IEEE UAV Attack Dataset, the system integrates diverse models and techniques like Autoencoders, Isolation Forests, One-Class SVMs, Principal Component Analysis and pretrained Transformer-based detectors over multi-sensor, time-aligned telemetry. In the ensemble, model contributions are adaptively weighted via a hybrid scheme combining supervised trust (F1-score) with unsupervised reliability metrics (kurtosis, entropy, Median Absolute Deviation). The resulting weights are fused using a data-driven coefficient α, derived through Spearman rank correlation between supervised and unsupervised scores. This research uses MOMENT a pretrained Transformer introduced by Goswami et al (2024) for gating consensus mechanism. MOMENT refines final decisions using temporal smoothing and inter-model agreement measured by Jaccard similarity. Unlike prior single-detector methods, the proposed architecture is modular, sensor-agnostic, and resilient to sensor dropout. Experiments show improved anomaly detection accuracy, reduced false positives, and enhanced generalization across spoofing, jamming, and evolving threats without requiring threshold tuning.
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