Search Results for author: Abdurrahman Elmaghbub

Found 8 papers, 1 papers with code

HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication

no code implementations16 May 2023 Luke Puppo, Weng-Keen Wong, Bechir Hamdaoui, Abdurrahman Elmaghbub

New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices.

Time Series

ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation

no code implementations29 Jan 2023 Abdurrahman Elmaghbub, Bechir Hamdaoui, Weng-Keen Wong

Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation.

Disentanglement Domain Adaptation

Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints

no code implementations14 Nov 2022 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

1 code implementation13 Oct 2022 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

no code implementations31 Aug 2022 Bechir Hamdaoui, Abdurrahman Elmaghbub

Finally, we experimentally study and analyze the sensitivity of LoRa RF fingerprinting to various network setting changes.

An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification

no code implementations20 Feb 2022 Jun Chen, Weng-Keen Wong, Bechir Hamdaoui, Abdurrahman Elmaghbub, Kathiravetpillai Sivanesan, Richard Dorrance, Lily L. Yang

We perform a deep dive into understanding the impact of (i) the input representation/type and (ii) the architectural layer of the neural network.

Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case

no code implementations6 Jan 2022 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 enforcement, automated network device authentication, and unauthorized network access monitoring and control.

Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification

no code implementations2 Mar 2021 Bechir Hamdaoui, Abdurrahman Elmaghbub, Seifeddine Mejri

We then present novel feature design approaches that exploit the distinct structures of the RF communication signals and the spectrum emissions caused by transmitter hardware impairments to custom-make DNN models suitable for classifying wireless devices using RF signal data.

Classification General Classification

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