The consumer unmanned aerial vehicle (UAV) 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. 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 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 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. Evaluation results show that our proposed method can detect and identify tested UAVs within 0.15-0.35s with high accuracy of 85.7-95.2%. The UAV detection range is within the physical sensing range of 70m and 40m in the line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively. The operation mode of UAVs can be identified with high accuracy of 88.5-98.2%.

中文翻译：

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加密Wi-Fi流量上基于机器学习的延迟感知无人机检测和操作模式识别

过去几年中，消费者无人驾驶飞机（UAV）市场已显着增长。尽管通过支持各种应用在刺激经济增长方面具有巨大潜力，但消费者无人机的增加却对公共安全和个人隐私构成潜在风险。为了最小化风险，迫切需要有效地检测和识别入侵的无人机，以用于入侵检测和法医目的。为了补充现有的物理检测机制，我们提出了一种基于机器学习的框架，用于通过加密的Wi-Fi通信快速识别UAV。观察到的动机是，许多消费类无人机使用Wi-Fi链接进行控制和视频流传输。提出的框架提取的特征仅来自数据包大小和加密Wi-Fi流量的到达间隔时间，并可以有效地检测无人机并确定其运行模式。为了减少在线识别时间，我们的框架采用了重新加权ℓ _1个 -norm正则化，它考虑样本数和不同特征的计算成本。该框架在统一的目标函数中共同优化了特征选择和预测性能。为了在优化检测精度和延迟之间的权衡时解决数据包到达时间的不确定性，我们利用最大似然估计（MLE）方法来估计数据包到达时间。我们收集了八种类型的消费者无人机的大量现实世界Wi-Fi数据流量，并对我们提出的方法的性能进行了广泛的评估。评估结果表明，我们提出的方法可以在0.15-0.35s内检测和识别经过测试的无人机，其准确度为85.7-95.2％。在视线（LoS）和非视线（NLoS）场景中，无人机的检测范围分别在70m和40m的物理感应范围内。无人机的工作模式可以识别为88.5-98.2％的高精度。