Dataset for authentication and authorization using physical layer properties in indoor environment
: Ahmed, Kazi Istiaque; Tahir, Mohammad; Lau, Sian Lun; Habaebi, Mohamed Hadi; Ahad, Abdul; Pires, Ivan Miguel
Publisher: Elsevier
: 2024
: Data in Brief
: Data in Brief
: 110589
: 55
: 2352-3409
DOI: https://doi.org/10.1016/j.dib.2024.110589
: https://doi.org/10.1016/j.dib.2024.110589
: 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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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.
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