Incremental learning for drone pilot identification

With the maturity of Unmanned Aerial Vehicle (UAV) technology and the development of Industrial Internet of Things, drones have become indispensible part of intelligent transportation system. Due to the absence of an effective identification scheme, most commercial drones suffer from impersonation attacks during the flight procedure. Some pioneer works already try to validate the pilot's legal status at the beginning and during the flight time. However, the off-the-shelf pilot identification scheme can not adapt to the dynamic pilot membership management for lacking of extensibility. To address this challenge, we propose an incremental learning-based drone pilot identification scheme to protect drones from impersonation attacks. By utilizing the pilot temporal operational behavioral traits, the proposed identification scheme could validate pilot legal status and dynamically adapt newly registered pilots into a well-constructed identification scheme for dynamic pilot membership management. After systemic experiments, the proposed scheme could achieve the best average identification accuracy with 95.71% on P450 and 94.23% on S500. With the increasing number of registered pilots, the proposed scheme still maintains high identification performance for the newly added and the previously registered pilots. Thanks to the minimal system overhead, this identification scheme demonstrates the high potential to protect drones from impersonation attacks.