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.

本文介绍一种基于卷积神经网络（CNN）和长短时记忆（LSTM）模型的开集检测方法，该方法有效地应用于 LoRa、无线 WiFi 和有线 WiFi 数据集，提高了 Area Under the Precision-Recall Curve，以便成功监控和控制无线设备的未授权网络访问。

HiNoVa:一种用于自动化射频设备认证的新型开放集检测方法