Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice
- URL: http://arxiv.org/abs/2506.09807v1
- Date: Wed, 11 Jun 2025 14:47:05 GMT
- Title: Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice
- Authors: Junqing Zhang, Francesco Ardizzon, Mattia Piana, Guanxiong Shen, Stefano Tomasin,
- Abstract summary: Physical layer-based device fingerprinting is an emerging device authentication for wireless security.<n>This article focuses on hardware impairment-based identity authentication and channel features-based authentication.
- Score: 12.512982702508669
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication approaches are cryptography-based, which, however, are usually computationally expensive and not adequate in the Internet of Things (IoT), where devices tend to be low-cost and with limited resources. This paper provides a comprehensive survey of physical layer-based device fingerprinting, which is an emerging device authentication for wireless security. In particular, this article focuses on hardware impairment-based identity authentication and channel features-based authentication. They are passive techniques that are readily applicable to legacy IoT devices. Their intrinsic hardware and channel features, algorithm design methodologies, application scenarios, and key research questions are extensively reviewed here. The remaining research challenges are discussed, and future work is suggested that can further enhance the physical layer-based device fingerprinting.
Related papers
- Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge.<n>We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic.<n>This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
arXiv Detail & Related papers (2025-04-22T09:40:05Z) - To See or Not to See -- Fingerprinting Devices in Adversarial Environments Amid Advanced Machine Learning [0.725130576615102]
Device fingerprinting is often employed to authenticate devices, detect adversaries, and identify eavesdroppers in an environment.<n>This requires the ability to discern between legitimate and malicious devices.<n>We propose a generic, yet simplified, model for device fingerprinting.
arXiv Detail & Related papers (2025-04-11T05:40:47Z) - ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Deepfake Media Forensics: State of the Art and Challenges Ahead [51.33414186878676]
AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity.
Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques.
This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
arXiv Detail & Related papers (2024-08-01T08:57:47Z) - Communication Traffic Characteristics Reveal an IoT Devices Identity [0.0]
This paper proposes a machine learning-based device fingerprinting (DFP) model for identifying network-connected IoT devices.
Experimental results have shown that the proposed DFP method achieves over 98% in classifying individual IoT devices.
arXiv Detail & Related papers (2024-02-25T18:58:09Z) - Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security [7.8344795632171325]
Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly.
To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality.
This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework.
arXiv Detail & Related papers (2024-02-08T00:23:42Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
This paper provides a classification of IoT malware.
Major targets and used exploits for attacks are identified and referred to the specific malware.
The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
arXiv Detail & Related papers (2023-12-01T16:10:43Z) - Internet of Things: Digital Footprints Carry A Device Identity [0.0]
Device fingerprinting (DFP) model is able to distinguish between Internet of Things (IoT) and non-IoT devices.
Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints.
arXiv Detail & Related papers (2023-01-01T02:18:02Z) - Task-Oriented Communications for NextG: End-to-End Deep Learning and AI
Security Aspects [78.84264189471936]
NextG communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications.
Wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.
Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB.
arXiv Detail & Related papers (2022-12-19T17:54:36Z) - CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an
In-Vehicle CAN Bus Based on Deep Features of Voltage Signals [48.813942331065206]
We propose a security hardening system for in-vehicle networks.
The proposed system includes two mechanisms that process deep features extracted from voltage signals measured on the CAN bus.
arXiv Detail & Related papers (2021-06-15T06:12:33Z) - Machine Learning for the Detection and Identification of Internet of
Things (IoT) Devices: A Survey [16.3730669259576]
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications.
The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones.
We classify the IoT device identification and detection into four categories: device-specific pattern recognition, Deep Learning enabled device identification, unsupervised device identification, and abnormal device detection.
arXiv Detail & Related papers (2021-01-25T15:51:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.