Drone Wireless Charging Profiling and Fingerpriting

This thesis describes the work done during my internship period, carried out within the University of Padua in the SPRITZ Research Group, an acronym that stands for Security and Privacy Research Group, of the Department of Mathematics, under the guidance of professor Alessandro Brighente. The work was divided into three parts: the study of the literature on UAV security with Threath Models and Scenarios, Wireless Charging Protocols (Qi) and their areas of use, taking over a Codebase of a temporarily stopped project, and finally the implementation of codes dedicated to introduce a model with the intention of fingerprinting, profiling, to be used with ad hoc Machine Learning algorithms: this study assesses the feasibility of profiling and fingerprinting drone firmware and executed operations by analyzing the current flow in various charging states. The findings reveal a distinct correlation between different software on board the drone and current behavior, which can be easily distinguished using various machine learning algorithms, the results demonstrate the possibility to accurately identify both the firmware and communication protocol of a drone. This research, and the ones prior, serve as a foundation for exploring the security and privacy of wireless power transfer in drone technology, with implications for both novel attack vectors and defense strategies.