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

arXiv: https://arxiv.org/abs/2305.09594 · HTML: https://mlsanity.com/paper/2305.09594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 10 of 10)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods](https://mlsanity.com/paper/2203.02486/) (2022)
- [Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations](https://mlsanity.com/paper/2005.00954/) (2020)
- [Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case](https://mlsanity.com/paper/2201.02213/) (2022)
- [Deep Learning Approaches for Open Set Wireless Transmitter Authorization](https://mlsanity.com/paper/2002.07777/) (2020)
- [Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication](https://mlsanity.com/paper/2209.00786/) (2022)
- [Open Set RF Fingerprinting using Generative Outlier Augmentation](https://mlsanity.com/paper/2108.13099/) (2021)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift](https://mlsanity.com/paper/2403.04036/) (2024)

## Abstract (arXiv)

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.