Dark Drones: Can They Be Automatically Detected and Mitigated?
Isaiah Henry-Simpson, Hortencia Mendoza, Anjie Shen, Xuecheng Wang, Yinzhi Cao, Lanier A. Watkins · 2025
The increasing prevalence of drones operating without radio frequency (RF) emissions-referred to as Dark Drones-poses a significant threat to national security and critical infrastructure. These platforms are hard to detect due to suppressed RF signatures and potential for malicious payloads. While existing C-UAS use visual, acoustic, or fused modalities, a critical gap remains in automated detection and mitigation of Dark Drones. To address this challenge, we conducted a systematic inves-tigation of commonly available hobbyist and commercial drones, examining their onboard sensors such as GPS, infrared, ultrasonic, and optical systems. We then identified and empirically validated multiple pathways for detecting, identifying, and mitigating these RF -silent drones. Building on these insights, we developed and evaluated a prototype system, the Automated Dark Drone Countering Tool (AD-DeCT). This tool is capable of automatically detecting, classifying, and mitigating Dark Drones without relying on RF emissions. While the current prototype has certain lim-itations, our findings demonstrate the technical feasibility of a comprehensive automated countermeasure approach against this emerging class of aerial threats.