Dataset for authentication and authorization using physical layer properties in indoor environment
Kazi Istiaque Ahmed,
Mohammad Tahir,
Sian Lun Lau,
Mohamed Hadi Habaebi,
Abdul Ahad,
Ivan Miguel Pires
Affiliations
Kazi Istiaque Ahmed
Department of Computing and Information Systems, Sunway University, Petaling Jaya, 47500 Selangor, Malaysia
Mohammad Tahir
Department of Computing, University of Turku, FI-20014 Turun Yliopisto, Finland; Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India; Corresponding author at: Department of Computing, University of Turku, FI-20014 Turun Yliopisto, Finland.
Sian Lun Lau
School of Engineering and Technology Sunway University No 5, Jalan Universiti, Bandar Sunway 47500 Selangor Darul Ehsan, Malaysia; Corresponding author at: School of Engineering and Technology, Sunway University No 5, Jalan Universiti, Bandar Sunway 47500 Selangor Darul Ehsan, Malaysia.
Mohamed Hadi Habaebi
IoT & Wireless Communication Protocols Lab, Department of Electrical and Computer Engineering, International Islamic University Malaysia, Jalan Gombak, 53100 Selangor, Malaysia
Abdul Ahad
School of Software, Northwestern Polytechnical University, Xian, Shaanxi, PR China; Department of Electronics and Communication Engineering, Istanbul Technical University, Turkey
Ivan Miguel Pires
Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal
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.
