Profiling drones via wireless power transfer side channel

Flying drones have become increasingly popular in recent years, both for recreational purposes and more functional tasks such as carrying packages or recording footage. With the rise of unmanned and autonomous drones, automated charging solutions, such as wireless charging pads, have also become more popular in the field. However, the privacy and security implications of public or unsecured charging pads for flying drones, or vice versa the use of said charging pads by unknown and untrusted devices, are not well explored and clear. In this work, we present a novel side channel that exploits the current flow in the wireless charging circuit to infer the drone's operational capabilities and internal state. We focus on the Qi wireless charging standard, in use in many different types of electronic devices such as smartphones, and apply it to the CrazyFlie drone platform, a highly customizable and research-oriented design which supports wireless charging via the Qi standard by default. Our aim is to evaluate our capabilities in profiling the drone's payloads and operations strictly from measuring the current flow in various charging states. We measure the correlation between the current consumed by the drone's payloads and the effect recorded on the sensors injected into the current delivery circuit, focusing on detecting both the battery level and immediate current consumption. Finally, we evaluate the extent of information leakage of the drone's internal operations based on the transient power consumption as recorded by our charging station.