E-Argus: Drones Detection by Side-Channel Signatures via Electromagnetic Radiation

Abstract

The increasing misuse of commercial drones for illicit activities poses significant challenges in their detection and identification. Existing methods, such as acoustic-based, radio frequency-based, and computer vision approaches, face limitations due to factors like miniaturization, stealth, and background noise. In this paper, we propose E-Argus, a system that leverages the electromagnetic radiation (EMR) emitted by the memory of drones. It is a basic fact that, with all types of drones, the implementation of arbitrary behavior must be digested in the built-in memory, and electromagnetic radiation is thus generated. Specifically, the memory clock drives the switching regulator causing current fluctuations that generate EMR signals at the clock frequency. E-Argus combines the relationship between the flight pattern of the drone and the memory EMR signal, analyzes the unique side-channel signatures, and utilizes advanced neural network-based identification; E-Argus can accurately detect and identify various types of illegal drones. We designed a system prototype based on USRP B210 and conducted experiments in a wide range of scenarios. The evaluation shows that E-Argus has low latency, high accuracy, and robustness in real environments.