Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jun 4:55:110589.
doi: 10.1016/j.dib.2024.110589. eCollection 2024 Aug.

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

Kazi Istiaque Ahmed  1 , Mohammad Tahir  2   3 , Sian Lun Lau  4 , Mohamed Hadi Habaebi  5 , Abdul Ahad  6   7 , Ivan Miguel Pires  8
Affiliations

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

Kazi Istiaque Ahmed et al. Data Brief. .

Abstract

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.

Keywords: Authentication; Authorization; Internet of things.; LQI; Machine learning; Physical layer; RSSI; Security.

PubMed Disclaimer

Figures

Fig. 1:
Fig. 1
Lab Setup for Data Collection (Horizontal).
Fig. 2:
Fig. 2
Lab Setup for Data Collection (Vertical).
Fig. 3:
Fig. 3
Data Representation of Client-X1.
Fig. 4:
Fig. 4
Data Representation of Client-X2.
Fig. 5:
Fig. 5
Data Representation of Client-X3.
See this image and copyright information in PMC

References

    1. Moradbeikie A., Zare M., Keshavarz A., Lopes S.I. RSSI-based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity. Data Br. 2024;53 doi: 10.1016/j.dib.2024.110120. - DOI - PMC - PubMed
    1. Faris M., Mahmud M.N., Salleh M.F.M., Alnoor A. Wireless sensor network security: a recent review based on state-of-the-art works. Int. J. Eng. Bus Manag. 2023;15:1–29. doi: 10.1177/18479790231157220. - DOI
    1. Janssen T., Koppert A., Berkvens R., Weyn M. A Survey on IoT positioning leveraging LPWAN, GNSS, and LEO-PNT. IEEE Internet Things J. 2023;10:11135–11159. doi: 10.1109/JIOT.2023.3243207. - DOI
    1. Almomani I., Al-Kasasbeh B., Al-Akhras M. WSN-DS: a dataset for intrusion detection systems in wireless sensor networks. J. Sens. 2016;2016 doi: 10.1155/2016/4731953. - DOI
    1. Istiaque Ahmed K., Tahir M., Hadi Habaebi M., Lun Lau S., Ahad A. Machine learning for authentication and authorization in iot: taxonomy, challenges and future research direction. Sensors. 2021;21:1–34. doi: 10.3390/s21155122. - DOI - PMC - PubMed

LinkOut - more resources