A Comparative Analysis of Ensemble‐Based Machine Learning Approaches With Explainable <scp>AI</scp> for Multi‐Class Intrusion Detection in Drone Networks

ABSTRACT The growing integration of drones into civilian, commercial, and defense sectors introduces significant cybersecurity concerns, particularly with the increased risk of network‐based intrusions targeting drone communication protocols. Detecting and classifying these intrusions is inherently challenging due to the dynamic nature of drone traffic and the presence of multiple sophisticated attack vectors such as spoofing, injection, replay, and man‐in‐the‐middle (MITM) attacks. This research aims to develop a robust and interpretable intrusion detection framework tailored for drone networks, with a focus on handling multi‐class classification and model explainability. Initially, the ISOT Drone Anomaly Detection Dataset was used for model training, followed by validation on the UAVIDS‐2025 dataset to assess generalizability. We present a comparative analysis of ensemble‐based machine learning models trained…