scholarly journals Device Authentication Codes based on RF Fingerprinting using Deep Learning

2021 ◽  
Vol 8 (29) ◽  
pp. 172305
Author(s):  
Joshua Bassey ◽  
Xiangfang Li ◽  
Lijun Qian
Keyword(s):  
Deep Learning ◽  
Authentication Codes ◽  
Rf Fingerprinting

Deep Learning for RF Fingerprinting: A Massive Experimental Study

2020 ◽  
Vol 3 (1) ◽  
pp. 50-57 ◽  
Author(s):  
Tong Jian ◽  
Bruno Costa Rendon ◽  
Emmanuel Ojuba ◽  
Nasim Soltani ◽  
Zifeng Wang ◽  
...  
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Rf Fingerprinting

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals Deep learning based RF fingerprinting for device identification and wireless security

2018 ◽  
Vol 54 (24) ◽  
pp. 1405-1407 ◽  
Author(s):  
Qingyang Wu ◽  
Carlos Feres ◽  
Daniel Kuzmenko ◽  
Ding Zhi ◽  
Zhou Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wireless Security ◽  
Device Identification ◽  
Rf Fingerprinting

Deep Learning Methods for Device Authentication Using RF Fingerprinting

2021 ◽  
Author(s):  
Da Huang ◽  
Akram Al-Hourani ◽  
Kandeepan Sithamparanathan ◽  
Wayne S. T. Rowe ◽  
Luc Bulot ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Methods ◽  
Rf Fingerprinting

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

2020 ◽  
Author(s):  
Hao Gu ◽  
Guangwei Qing ◽  
Yu Wang ◽  
Sheng Hong ◽  
Guan Gui ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Superior Performance ◽  
Aviation Management ◽  
Fast Running ◽  
Rf Fingerprinting ◽  
High Recognition Accuracy ◽  
Complex Valued

<div>Drones-aided ubiquitous applications play more and more important roles in our daily life. Accurate recognition of drones is required in aviation management due to their potential risks and even disasters.</div><div>Radio frequency (RF) fingerprinting-based recognition technology based on deep learning is considered as one of the effective approaches to extract hidden abstract features from RF data of drones. Existing deep learning-based methods are either a high computational burden or low accuracy.</div><div>In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones.</div><div>Compared with existing recognition methods, the DC-CNN method has the advantages of high recognition accuracy, fast running time and small network complexity.</div><div>Nine algorithm models and two datasets are used to represent the superior performance of our system.</div><div>Experimental results show that our proposed DC-CNN can achieve recognition accuracy of 99.5\% and 74.1\% respectively on 4 and 8 classes of RF drone datasets.</div>

Intrusion Detection for IoT Devices based on RF Fingerprinting using Deep Learning

2019 ◽  
Author(s):  
Joshua Bassey ◽  
Damilola Adesina ◽  
Xiangfang Li ◽  
Lijun Qian ◽  
Alexander Aved ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Rf Fingerprinting ◽  
Iot Devices

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

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