Drone Anomaly Detection: Dataset and Unsupervised Machine Learning

Drone Anomaly Detection: Dataset and Unsupervised Machine Learning

연구 분야: Artificial Intelligence

학회: International Symposium on Foundations and Practice of Security

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 components expose drones to many critical vulnerabilities, which raise the need to implement effective threat detection while operating them. This study investigates a wifi-based drone to comprehensively assess its vulnerabilities and develop anomaly detection mechanisms using different unsupervised and incremental supervised machine learning techniques. Two types of data were collected: benign data from legitimate actions and attack data comprising nine 9 distinct types of attacks, with an additional 5 sub-categories, totaling 14 types. 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 work. 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. In addition to evaluating these three algorithms, an incremental supervised ML model, specifically the adaptive random forest, was also explored. 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.