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

Kazi Istiaque Ahmed1, Mohammad Tahir2,3, Sian Lun Lau4

  • 1Department of Computing and Information Systems, Sunway University, Petaling Jaya, 47500 Selangor, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a new dataset for Internet of Things (IoT) security, focusing on authentication and authorization (AA) using physical layer data. This resource aids in developing machine learning security solutions for IoT devices.

Keywords:
AuthenticationAuthorizationInternet of things.LQIMachine learningPhysical layerRSSISecurity

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Area of Science:

  • Cybersecurity
  • Computer Networks
  • Machine Learning

Background:

  • The Internet of Things (IoT) expansion necessitates robust security, particularly for Authentication and Authorization (AA).
  • Existing security solutions often lack comprehensive datasets for developing effective machine learning-based AA mechanisms.
  • Physical layer characteristics offer unique identifiers for device authentication.

Purpose of the Study:

  • To present a novel dataset for enhancing machine learning-enabled Authentication and Authorization (AA) in IoT ecosystems.
  • To address the gap in real-world datasets for IoT physical layer security research.
  • To facilitate the development of advanced security solutions leveraging device-specific physical attributes.

Main Methods:

  • Collected data from real-world scenarios using Zigbee Zolertia Z1 nodes in indoor environments.
  • Captured physical layer parameters including Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), device internal temperature, and battery level.
  • Structured the data to provide a comprehensive foundation for machine learning model training.

Main Results:

  • A novel, comprehensive dataset of physical layer characteristics for IoT devices was created.
  • The dataset includes diverse parameters crucial for distinguishing and authenticating IoT devices.
  • The findings provide a valuable resource for researchers in IoT security and machine learning.

Conclusions:

  • The developed dataset is essential for advancing machine learning-enabled Authentication and Authorization (AA) in the Internet of Things (IoT).
  • Utilizing physical layer characteristics offers a promising avenue for secure and reliable IoT device identification.
  • This research contributes to building more resilient and secure IoT ecosystems.