Fifth generation (5G) network and beyond envision 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 the 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. 

https://www.techrxiv.org/articles/preprint/A_Comprehensive_Survey_on_Radio_Frequency_RF_Fingerprinting_Traditional_Approaches_Deep_Learning_and_Open_Challenges/17711444/3/files/36593202.pdf

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

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.

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

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. 

https://www.techrxiv.org/articles/preprint/A_Comprehensive_Survey_on_Radio_Frequency_RF_Fingerprinting_Traditional_Approaches_Deep_Learning_and_Open_Challenges/17711444/2/files/32416001.pdf

Network security has always had an issue with secure authentication and identification. In the current mixed device network of today, the number of nodes on a network has expanded but these nodes are often unmanaged from a network security perspective. The solution proposed requires a paradigm shift, a recognition of what has already happened, identity is for sale across the internet. That identity is the users’ network ID, their behavior, and even their behavior in using the networks. Secondly a majority of the devices on the Internet have been fingerprinted. Use of device fingerprinting can help secure a network if properly understood and properly executed. The research into this area suggests a solution. Which is the use of device fingerprints including clock skews to identify the devices and a dualauthentication process targeted at authenticating the device and the user. Not only authenticating the identity presented but also combining them into a unified entity so failure to authenticate part of the entity means the whole is denied access to the network and its resources.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=6975&context=etd

Device fingerprinting identification and authentication: A two-fold use in multi-factor access control schemes

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Device fingerprinting identification and authentication: A two-fold use in multi-factor access control schemes