Browsing by Author "Gomes, Filipe Manuel Moura"
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- Utilização da Tecnologia SDR para Autenticação de Dispositivos IoMT Baseada em RF FingerprintPublication . Gomes, Filipe Manuel Moura; Severino, Ricardo Augusto Rodrigues da SilvaThe increasing reliance on the Internet of Medical Things (IoMT) raises great concern in terms of cybersecurity, either at the device’s physical level or at the information and communication level. This is particularly important as these systems process very sensitive and private data, including personal health data from multiple patients such as real-time body measurements. Due to these concerns, cybersecurity mechanisms and strategies must be in place to protect these medical systems, defending them from compromising cyberattacks. Authentication is an essential cybersecurity technique for trustworthy IoMT communications. However, current authentication methods rely on upper-layer identity verification or key-based cryptography which can be inadequate to the heterogeneous Internet of Things (IoT) environments. In our current research we aim at using Radio Frequency Fingerprinting for IoMT device authentication in medical applications to improve the cybersecurity of such mechanisms. This technique allows the authentication of medical devices by their physical layer characteristics, i.e. of their emitted signal. This shall be accomplished through the use of software-defined technologies, specifically SoftwareDefined Radio (SDR) gateways to implement security solutions while addressing ecosystem heterogeneity, in conjunction with Artificial Intelligence (AI) Edge/Cloud support to scale and leverage Machine Learning (ML) strategies. This thesis focuses on the signal acquisition and feature extraction stages of the Radio Frequency Fingerprinting (RFF) process, relying upon SDR technology, which can effectively improve the feature extraction process in complex and challenging wireless environments, to support later highly accurate device authentication. This resulted in the creation of a dataset, which was unavailable before, to train and setup the RFF system. Finally, the thesis specifies the integration of the RFF system with the medical IoT gateway.