Sensor Data-Driven UAV Anomaly Detection using Deep Learning Approach

Julio Galvan, Ashok Raja, Yanyan Li, Jiawei Yuan

Abstract: Thanks to the high mobility and rich sensing capabilities of unmanned aerial vehicles (UAVs), or drones, they are increasingly leveraged to perform a series of military and civilian tasks today. Meanwhile, UAVs are also facing various security and safety concerns raised by both external attacks and internal hardware/software failures. Therefore, detecting the abnormal status of a UAV is a critical task to protect it against malicious adversaries and prevent potential crashes. In this paper, we propose an anomaly detection system for UAVs by monitoring and analyzing their sensor data in real-time using deep learning approaches. The proposed system leverages the convolutional neural network (CNN) to extract and learn features automatically from raw sensor data and then process them to support anomaly detection. We construct a data set of UAV IMU sensor data using our UAV cybersecurity simulation platform to support the training of our CNN model. Different deep learning models are also evaluated and compared in this paper. We validate the performance of the proposed detection system using extensive experimental evaluation, which demonstrates that our system achieves high detection accuracy under different conditions.