43 citations · 79 across the 13 of their papers we have counts for
5 papers · 1 filter
Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints
Bechir Hamdaoui, Abdurrahman Elmaghbub
Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices…
ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G
Ahmed Benfaid, Nadia Adem, Abdurrahman Elmaghbub
Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention.…
Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes
Bechir Hamdaoui, Abdurrahman Elmaghbub
Deep-learning-based device fingerprinting has recently been recognized as a key enabler for automated network access authentication. Its robustness to impersonation attacks due to…
An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification
Jun Chen, Weng-Keen Wong, Bechir Hamdaoui +4
Recent deep neural network-based device classification studies show that complex-valued neural networks (CVNNs) yield higher classification accuracy than real-valued neural network…
Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case
Abdurrahman Elmaghbub, Bechir Hamdaoui
Deep learning-based RF fingerprinting has recently been recognized as a potential solution for enabling newly emerging wireless network applications, such as spectrum access policy…