LATIDIA · Ciberseguridad

Conjunto de datos y plataforma de pruebas experimentales para la identificación de dispositivos Bluetooth mediante aprendizaje profundo.

arXiv:2611.00332v1 Anuncio Tipo: cruzado Resumen: Las redes inalámbricas habilitadas para IA han surgido como componentes vitales de los sistemas de comunicación inalámbrica de próxima generación. Entre las diversas arquitecturas inalámbricas, Bluetooth Low Ene

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arXiv:2611.00332v1 Announce Type: cross Abstract: AI-enabled wireless networks have emerged as vital components of future-generation wireless communication systems. Among various wireless architectures, Bluetooth Low Energy (BLE) networks have attracted significant research interest due to their widespread adoption and low cost. In these networks, deep learning (DL) models can be integrated directly into the physical-layer infrastructure to perform diverse tasks, including environment sensing, localization, radio frequency fingerprinting (RFFP), and signal denoising. While highly effective under their original training configurations, these DL models often fail significantly when deployed in unseen environments---such as different rooms, varying temperature conditions, or altered carrier frequencies. Addressing this limitation requires rigorous benchmarking of models and algorithms against