Machine Learning-Based Delay-Aware UAV Detection Over Encrypted Wi-Fi Traffic

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 estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of four types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method. Evaluation results show that our proposed method can identify the tested UAVs within 0.15-0.35s with high accuracy of 85.7-95.2%.