HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication

TL;DR

This paper presents HiNoVa, a new open-set detection method utilizing CNN-LSTM models to improve RF device authentication by identifying unseen devices in wireless networks.

Contribution

The paper introduces a novel open-set detection approach based on hidden state patterns in CNN-LSTM models tailored for RF data, addressing unique time series challenges.

Findings

Significant improvement in Area Under the Precision-Recall Curve across datasets

Effective detection of unauthorized wireless devices

Enhanced security in RF device authentication

Abstract

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.