Transfer learning for radio frequency machine learning: a taxonomy and survey
LJ Wong, AJ Michaels - Sensors, 2022 - mdpi.com
Transfer learning is a pervasive technology in computer vision and natural language
processing fields, yielding exponential performance improvements by leveraging prior …
processing fields, yielding exponential performance improvements by leveraging prior …
Machine learning for the detection and identification of Internet of Things devices: A survey
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …
variety of emerging services and applications. However, the presence of rogue IoT devices …
Radio frequency fingerprint identification for Internet of Things: A survey
Radio frequency fingerprint (RFF) identification is a promising technique for identifying
Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF …
Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF …
Evaluating adversarial evasion attacks in the context of wireless communications
B Flowers, RM Buehrer… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Recent advancements in radio frequency machine learning (RFML) have demonstrated the
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
Groundwork for neural network-based specific emitter identification authentication for IoT
JM McGinthy, LJ Wong… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
Trust is a prominent concern with the continued expansion of the Internet of Things (IoT). As
new devices enter the market, device security must be a design pillar. In order to trust these …
new devices enter the market, device security must be a design pillar. In order to trust these …
Dilated causal convolutional model for RF fingerprinting
J Robinson, S Kuzdeba, J Stankowicz… - 2020 10th Annual …, 2020 - ieeexplore.ieee.org
We design a network to classify individual wireless devices based on their radio frequency
(RF) fingerprints imparted on transmitted signals. The network combines a stack of dilated …
(RF) fingerprints imparted on transmitted signals. The network combines a stack of dilated …
Identification of OFDM-based radios under rayleigh fading using RF-DNA and deep learning
The Internet of Things (IoT) is here and has permeated every aspect of our lives. A disturbing
fact is that the majority of all IoT devices employ weak or no encryption at all. This coupled …
fact is that the majority of all IoT devices employ weak or no encryption at all. This coupled …
Improving rf-dna fingerprinting performance in an indoor multipath environment using semi-supervised learning
MKM Fadul, DR Reising, LP Weerasena… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Internet of Things (IoT) deployments are expected to reach 75.4 billion by 2025. Roughly
70% of all IoT devices employ weak or no encryption, thus putting them and their connected …
70% of all IoT devices employ weak or no encryption, thus putting them and their connected …
On the limitations of targeted adversarial evasion attacks against deep learning enabled modulation recognition
Wireless communications has greatly benefited in recent years from advances in machine
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
Transfer learning with radio frequency signals
S Kuzdeba, J Robinson… - 2021 IEEE 18th Annual …, 2021 - ieeexplore.ieee.org
Transfer learning has allowed for more widespread adaptation and expanded use of deep
learning models in fields such as computer vision and speech recognition. The radio …
learning models in fields such as computer vision and speech recognition. The radio …