A1 Refereed data article in a scientific journal
Dataset for authentication and authorization using physical layer properties in indoor environment
Authors: Ahmed, Kazi Istiaque; Tahir, Mohammad; Lau, Sian Lun; Habaebi, Mohamed Hadi; Ahad, Abdul; Pires, Ivan Miguel
Publisher: Elsevier
Publication year: 2024
Journal: Data in Brief
Journal name in source: Data in Brief
Article number: 110589
Volume: 55
eISSN: 2352-3409
DOI: https://doi.org/10.1016/j.dib.2024.110589
Web address : https://doi.org/10.1016/j.dib.2024.110589
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/456848606
The proliferation landscape of the Internet of Things (IoT) has accentuated the critical role of Authentication and Authorization (AA) mechanisms in securing interconnected devices. There is a lack of relevant datasets that can aid in building appropriate machine learning enabled security solutions focusing on authentication and authorization using physical layer characteristics. In this context, our research presents a novel dataset derived from real-world scenarios, utilizing Zigbee Zolertia Z1 nodes to capture physical layer properties in indoor environments. The dataset encompasses crucial parameters such as Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Device Internal Temperature, Device Battery Level, and more, providing a comprehensive foundation for advancing Machine learning enabled AA in IoT ecosystems.
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Funding information in the publication:
This work is funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020.
