HiNoVa: A Novel Open-Set Detection Method for Automating RF Device
  Authentication
Luke Puppo Weng-Keen Wong Bechir Hamdaoui Abdurrahman Elmaghbub
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.