Identifying Drone Operator by Deep Learning and Ensemble Learning of IMU and Control Data

Abstract

Drone flight controls and ground stations are known to be vulnerable to attacks. Besides posing a threat to integrity and confidentiality of drone data, their vulnerabilities endanger safety. Onboard continuous authentication is a vital countermeasure to hijacking attempts. Motivated by the success of Machine Learning (ML) techniques in the field of behavioral biometrics, this paper investigates the use of sensor readings generated onboard drones and of control data reaching them from the ground to feed an onboard ML model continuously authenticating pilots. We analyze fifteen inertial measurement units (IMU) and four radio control signals obtained from the drone's onboard sensors or coming from its remote controller, to identify the controlling pilot. We investigate three sequence classification schemes. In the first scheme, raw sensor sequences are directly fed to a deep Long/Short-Term Memory (LSTM) learner. In the second scheme, frequency-domain features are extracted from the data sequences and interpreted by an ensemble of random trees. In the third scheme, instantaneous sensor readings are classified using the same ensemble learning technique as in the second scheme, yet a final decision fusion method is adopted to provide a sequence-based decision. We compare the three schemes in terms of accuracy, complexity, and delay. The winning scheme is validated and tested against an unseen intruder scenario. Our tests show that an LSTM model trained with data from 19 users is able to identify the operating user at a 97% accuracy, while it can identify an unknown intruder at an average accuracy of 73%.