HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication
This addresses the problem of unauthorized network access in wireless security, offering a domain-specific solution for RF data, though it appears incremental as it adapts existing deep learning techniques to a new data type.
The paper tackles open-set detection for RF device authentication by introducing a CNN-LSTM based method that leverages hidden state patterns, achieving significant improvements in Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets.
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.