Abdurrahman Elmaghbub

Orcid: 0000-0003-3704-6056

According to our database1, Abdurrahman Elmaghbub authored at least 17 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security.
IEEE Wirel. Commun., April, 2024

2023
EPS: Distinguishable IQ Data Representation for Domain-Adaptation Learning of Device Fingerprints.
CoRR, 2023

On the Impact of the Hardware Warm-Up Time on Deep Learning-Based RF Fingerprinting.
CoRR, 2023

Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting.
IEEE Access, 2023

HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication.
Proceedings of the IEEE Symposium on Computers and Communications, 2023

ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation.
Proceedings of the IEEE International Conference on Communications, 2023

A Needle in a Haystack: Distinguishable Deep Neural Network Features for Domain-Agnostic Device Fingerprinting.
Proceedings of the IEEE Conference on Communications and Network Security, 2023

2022
Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes.
IEEE Netw., 2022

Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints.
CoRR, 2022

An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification.
Proceedings of the IEEE International Conference on Communications, 2022

ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G.
Proceedings of the IEEE Globecom 2022 Workshops, 2022

2021
Scalable spectrum database construction mechanisms for efficient wideband spectrum access management.
Phys. Commun., 2021

Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification.
IEEE Netw., 2021

LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability.
IEEE Access, 2021

Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case.
Proceedings of the IEEE Globecom 2021 Workshops, Madrid, Spain, December 7-11, 2021, 2021

2020
WideScan: Exploiting Out-of-Band Distortion for Device Classification Using Deep Learning.
Proceedings of the IEEE Global Communications Conference, 2020

2018
Distributed Wideband Sensing for Faded Dynamic Spectrum Access with Changing Occupancy.
Proceedings of the IEEE Global Communications Conference, 2018


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