Low-cost UAV detection via WiFi traffic analysis and machine learning

In recent years, unmanned aerial vehicles (UAVs) have experienced remarkable advancements. However, their growing utilization brings potential security threats to the public, particularly in private and sensitive locales. To address these hazards, this paper introduces a low-cost, three-stage UAV detection framework for monitoring invading UAVs in vulnerable zones, devised through an investigation of the Chinese UAV market. Various scenarios were examined to evaluate the framework, and it was implemented on a portable board. Experiments demonstrate that the framework can detect invading UAVs at an early stage, even in stealthy mode, indicating potential for deployment in portable surveillance systems for UAV-restricted regions.

Longtao Bi, Zi-Xin Xu, Ling Yang