no code implementations • 6 Mar 2024 • Jun Chen, Weng-Keen Wong, Bechir Hamdaoui
When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs.
no code implementations • 13 Jun 2023 • Bechir Hamdaoui, Nora Basha, Kathiravetpillai Sivanesan
Deep learning-enabled device fingerprinting has proven efficient in enabling automated identification and authentication of transmitting devices.
no code implementations • 16 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.
no code implementations • 29 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.
no code implementations • 14 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.
no code implementations • 31 Aug 2022 • Bechir Hamdaoui, Abdurrahman Elmaghbub
Finally, we experimentally study and analyze the sensitivity of LoRa RF fingerprinting to various network setting changes.
no code implementations • 20 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.
no code implementations • 6 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.
no code implementations • 8 Sep 2021 • Nora Basha, Bechir Hamdaoui
We also show that for Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to $40\%$ when the models are trained and tested over the same channel, and by up to $60\%$ when the models are tested on a channel that is different from that used for training.
no code implementations • 2 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.