Enhancing UAV Communication Security: Multi-Label Anomaly Detection Using Machine Learning in Imbalanced Data Environments

Abstract

Unmanned Aerial Vehicle (UAV) communication networks are increasingly vulnerable to cyber threats, including spoofing, jamming, malware, and distributed denial-of-service (DDoS) attacks. Effective anomaly detection is crucial to maintaining network integrity and operational security. This study evaluates multiple machines learning models, including Support Vector Machines, Logistic Regression, XGBoost, Gradient Boosting, and Random Forest, to detect anomalies in UAV communication networks. A real-world dataset containing 44,016 instances of network telemetry and security indicators was utilized, with each instance labeled for multiple potential anomalies. Experimental results reveal a significant class imbalance, where models achieve high accuracy (92%) but fail to detect minority class anomalies, yielding near-zero recall scores for critical cyber threats. The study highlights the limitations of traditional classifiers in imbalanced multi-label classification tasks and emphasizes the need for advanced techniques such as Synthetic Minority Over-sampling (SMOTE), cost-sensitive learning, and deep learning-based anomaly detection. The findings suggest that conventional machine learning approaches alone are insufficient for reliable anomaly detection in UAV networks, necessitating hybrid solutions that integrate multiple detection paradigms. Future work should explore adaptive ensemble learning methods and deep anomaly detection frameworks to improve recall and precision for rare cybersecurity threats.