# Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations

arXiv: https://arxiv.org/abs/2005.00954 · HTML: https://mlsanity.com/paper/2005.00954/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 16 of 16)

- [A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication](https://mlsanity.com/paper/2108.04436/) (2021)
- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [Open Set RF Fingerprinting using Generative Outlier Augmentation](https://mlsanity.com/paper/2108.13099/) (2021)
- [Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication](https://mlsanity.com/paper/2210.12494/) (2022)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [Reservoir-Based Distributed Machine Learning for Edge Operation](https://mlsanity.com/paper/2104.00751/) (2021)
- [HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication](https://mlsanity.com/paper/2305.09594/) (2023)
- [Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors](https://mlsanity.com/paper/2106.16087/) (2021)
- [Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval](https://mlsanity.com/paper/2111.02584/) (2021)
- [Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible](https://mlsanity.com/paper/2501.04401/) (2025)
- [Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting](https://mlsanity.com/paper/2505.00881/) (2025)
- [Spiking Neural Network: a low power solution for physical layer authentication](https://mlsanity.com/paper/2505.12647/) (2025)
- [Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models](https://mlsanity.com/paper/2602.03035/) (2026)
- [Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting](https://mlsanity.com/paper/2604.12834/) (2026)
- [Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting](https://mlsanity.com/paper/2605.30364/) (2026)
- [Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting](https://mlsanity.com/paper/2606.12718/) (2026)

## Abstract (arXiv)

Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, the majority of the work has focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this paper, we consider the problem of recognizing authorized transmitters and rejecting new transmitters. To address this problem, we adapt the most prominent approaches from the open set recognition and anomaly detection literature to the problem. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The robustness of the RF authorization with respect to temporal changes in fingerprints is also considered in the evaluation. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.