Enhancing UAV Reliability and Security: A Machine Learning-Based Framework for Anomaly Detection and Classification

Unmanned Aerial Vehicles have become integral to sectors like defense, agriculture, logistics, and surveillance due to their versatility and autonomy. However, their widespread adoption has introduced challenges, particularly concerning operational anomalies and cyber-security threats. Anomalies, including hardware failures, sensor malfunctions, and cyber intrusions, can critically affect Unmanned Aerial Vehicles reliability, leading to mission failures, financial losses, and data breaches. This paper presents a machine learning (ML)-based approach to enhance Unmanned Aerial Vehicles operations, focusing on anomaly detection and security. The proposed system leverages self-supervised learning alongside advanced ML models such as Support Vector Machines, Recurrent Neural Networks, and autoencoders to identify and classify anomalies. The methodology employs a two-stage approach: first, detecting anomalies using self-supervised learning models trained on extensive Unmanned Aerial Vehicles datasets, and second, classifying the anomalies into hardware, software, or cyber attack related issues. This ensures precise identification and mitigation of operational threats in real time. Additionally, the system addresses cyber-security concerns, including risks like unauthorized control and data breaches. By analyzing cyber-physical datasets, the proposed method detects and mitigates digital vulnerabilities, ensuring secure Unmanned Aerial Vehicles operations. Experimental results demonstrate significant improvements in metrics like accuracy, precision, and F1 score when employing self-supervised learning for anomaly detection. The findings highlight the potential of integrating ML techniques to enhance Unmanned Aerial Vehicles reliability, security, and autonomy. This work paves the way for safer and more efficient Unmanned Aerial Vehicles operations across diverse applications, addressing the critical need for robust anomaly and intrusion detection systems in contemporary Unmanned Aerial Vehicles ecosystems.