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In this paper, we propose a machine learning-based framework for fast UAV (unmanned aerial vehicle)detection and 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 statistical features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can quickly identify UAV types. In order to reduce the online identification time, our framework adopts a re-weighted ℓ 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 interarrival time uncertainty when optimizing the trade-off between the identification accuracy and delay, we utilize maximum likelihood estimation (MLE)method to …