Malicious UAV detection using various modalities

Abstract

Unmanned Aerial Vehicles, also known as drones, have seen increasing interest in recent years. This surge of interest is based on technological advancements, enhanced performance, affordability, and their large array of applications. Despite their utility in various applications, drones could also be used for malicious intent. The increasing concern regarding malicious drones triggers the use of various technologies and countermeasures, including drone detection and counter-drone systems. In this paper, we raise concerns about the malicious use of drone systems by providing a brief description of the major security threats. We also present the architecture of Unmanned Aerial Systems, drone types and their communication methods. We provide an in-depth overview of various drone detection techniques including vision-based, radio-frequency-based, and audio-based techniques, and discuss recent studies that address this issue using machine learning and deep learning models. We highlight the importance of using a hybrid approach for more accurate results.