Anomaly detection in drone activities: Data collection and unsupervised machine learning modeling
Date
2025
Authors
Chen, Zhuo
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Abstract
As Internet of Things (IoT) devices, drones are among the most popular unmanned aerial vehicles (UAVs), equipped with multiple sensors, cameras, and communication systems. These features expose them to potential vulnerabilities exploitable by hackers. making it crucial to explore these vulnerabilities and implement effective anomaly detection while operating UAVs. This study investigates a DJI Edu Tello drone to comprehensively assess its vulnerabilities and develop anomaly detection mechanisms using different unsupervised machine learning techniques. Two types of data were collected: benign data from legitimate actions and attack data comprising nine types of attacks. Feature extraction and engineering were performed based on scripts from the Canadian Institute for Cybersecurity (CIC), which were modified to suit the specific needs of this project. The modifications aimed to improve the robustness of the detector by removing and modifying existing features and introducing new measurements to represent the captured packets. The anomaly detector was formulated after comparing three unsupervised machine learning algorithms: Isolation Forest, Local Outlier Factor (LOF), and Elliptic Envelope, through extensive performance evaluations and analyses. The study demonstrated the effectiveness of these algorithms in detecting anomalies and enhancing the security of drones. The findings also highlight the critical role of robust feature engineering and careful algorithm selection in developing a reliable anomaly detection system for UAVs.
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Keywords
unsupervised machine learning, anomaly detection, cybersecurity