More is better: Data augmentation for channel-resilient RF fingerprinting
RF fingerprinting involves identifying characteristic transmitter-imposed variations within a
wireless signal. Deep neural networks (DNNs) that do not rely on handcrafting features have …
wireless signal. Deep neural networks (DNNs) that do not rely on handcrafting features have …
Deep learning for RF fingerprinting: A massive experimental study
RF fingerprinting is a key security mechanism that allows device identification by learning
unchanging, hardware-based characteristics of the transmitter. In this article, we …
unchanging, hardware-based characteristics of the transmitter. In this article, we …
Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting
RF fingerprinting leverages circuit-level variability of transmitters to identify them using
signals they send. Signals used for identification are impacted by a wireless channel and …
signals they send. Signals used for identification are impacted by a wireless channel and …
DeepRadioID: Real-time channel-resilient optimization of deep learning-based radio fingerprinting algorithms
Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by
leveraging the unique hardware-level imperfections imposed on the received wireless …
leveraging the unique hardware-level imperfections imposed on the received wireless …
Analysis of augmentation methods for RF fingerprinting under impaired channels
Cyber-physical systems such as autonomous vehicle networks are considered to be critical
infrastructures in various applications. However, their mission critical deployment makes …
infrastructures in various applications. However, their mission critical deployment makes …
A robust RF fingerprinting approach using multisampling convolutional neural network
With the increasing popularity of the Internet of Things (IoT), device identification, and
authentication has become a critical security issue. Recently, radio frequency (RF) …
authentication has become a critical security issue. Recently, radio frequency (RF) …
Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting
Radio fingerprinting uniquely identifies wireless devices by leveraging tiny hardware-level
imperfections inevitably present in off-the-shelf radio circuitry. This way, devices can be …
imperfections inevitably present in off-the-shelf radio circuitry. This way, devices can be …
A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges
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 …
support disruptive applications such as extended reality (XR), augmented/virtual reality …
Chameleons' oblivion: Complex-valued deep neural networks for protocol-agnostic RF device fingerprinting
Prior work has demonstrated techniques for fingerprinting devices based on their network
traffic or transmitted signals, which use software artifacts or characteristics of the underlying …
traffic or transmitted signals, which use software artifacts or characteristics of the underlying …
Deep-learning-aided RF fingerprinting for NFC security
Given the ever increasing use of near field communication (NFC), the security of this system
is becoming increasingly important. Recently, radio frequency (RF) fingerprinting, where the …
is becoming increasingly important. Recently, radio frequency (RF) fingerprinting, where the …