Dataset for authentication and authorization using physical layer properties in indoor environment

Autor:	Kazi Istiaque Ahmed, Mohammad Tahir, Sian Lun Lau, Mohamed Hadi Habaebi, Abdul Ahad, Ivan Miguel Pires
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	RSSI LQI Authentication Authorization Physical layer Machine learning Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Zdroj:	Data in Brief, Vol 55, Iss , Pp 110589- (2024)
Druh dokumentu:	article
ISSN:	2352-3409 58972404
DOI:	10.1016/j.dib.2024.110589
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f980a589724040a7859d3f6d5d689ba9 Zobrazit plný text záznamu View record in DOAJ