# Device Fingerprinting in Wireless Networks: Challenges and Opportunities

arXiv: https://arxiv.org/abs/1501.01367 · HTML: https://mlsanity.com/paper/1501.01367/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2015.

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth](https://mlsanity.com/paper/2209.03142/) (2022)
- [Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth](https://mlsanity.com/paper/2210.02897/) (2022)
- [The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting](https://mlsanity.com/paper/2105.03671/) (2021)
- [Semi-Supervised RF Fingerprinting with Consistency-Based Regularization](https://mlsanity.com/paper/2304.14795/) (2023)
- [Device identification using optimized digital footprints](https://mlsanity.com/paper/2212.04354/) (2022)
- [DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning](https://mlsanity.com/paper/2204.07614/) (2022)
- [Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels](https://mlsanity.com/paper/2205.08426/) (2022)
- [Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices](https://mlsanity.com/paper/2303.13538/) (2023)
- [Machine Learning Methods for Device Identification Using Wireless Fingerprinting](https://mlsanity.com/paper/2211.01963/) (2022)
- [Internet of Things: Digital Footprints Carry A Device Identity](https://mlsanity.com/paper/2301.00328/) (2023)
- [Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting](https://mlsanity.com/paper/2505.00881/) (2025)
- [Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models](https://mlsanity.com/paper/2602.03035/) (2026)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)

## Abstract (arXiv)

Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented. In particular, we aim to provide a detailed treatment on developing novel wireless security solutions using device fingerprinting techniques. The objectives are three-fold: (i) to introduce a comprehensive taxonomy of wireless features that can be used in fingerprinting, (ii) to provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches, and (iii) to identify key open research problems in the area of device fingerprinting and feature extraction, as applied to wireless security.