  The consumer UAV (unmanned aerial vehicle) market has grown significantly
over the past few years. Despite its huge potential in spurring economic growth
by supporting various applications, the increase of consumer UAVs poses
potential risks to public security and personal privacy. To minimize the risks,
efficiently detecting and identifying invading UAVs is in urgent need for both
invasion detection and forensics purposes. Given the fact that consumer UAVs
are usually used in a civilian environment, existing physical detection methods
(such as radar, vision, and sound) may become ineffective in many scenarios.
Aiming to complement the existing physical detection mechanisms, we propose a
machine learning-based framework for fast UAV identification over encrypted
Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use
Wi-Fi links for control and video streaming. The proposed framework extracts
features derived only from packet size and inter-arrival time of encrypted
Wi-Fi traffic, and can efficiently detect UAVs and identify their operation
modes. In order to reduce the online identification time, our framework adopts
a re-weighted $\ell_1$-norm regularization, which considers the number of
samples and computation cost of different features. This framework jointly
optimizes feature selection and prediction performance in a unified objective
function. To tackle the packet inter-arrival time uncertainty when optimizing
the trade-off between the detection accuracy and delay, we utilize Maximum
Likelihood Estimation (MLE) method to estimate the packet inter-arrival time.
We collect a large number of real-world Wi-Fi data traffic of eight types of
consumer UAVs and conduct extensive evaluation on the performance of our
proposed method.
