New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.

翻译：深度学习通过利用射频（RF）数据中的模式来识别和认证设备，为无线网络安全带来了新能力。开集检测是深度学习的一个领域，用于识别部署过程中从新设备捕获的、未包含在训练集中的样本。以往的开集检测研究主要应用于独立同分布数据（如图像），而射频信号数据则构成一组独特挑战，因为数据以时间序列形式存在，样本间具有非线性时间依赖性。我们提出了一种基于卷积神经网络（CNN）长短期记忆（LSTM）模型隐藏状态值模式的新型开集检测方法。该方法在LoRa、无线WiFi和有线WiFi数据集上显著提升了精确率-召回率曲线下面积（AUPRC），从而可成功用于监控和控制无线设备的未经授权网络访问。