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Related Experiment Videos

Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction.

Kazi Istiaque Ahmed1, Mohammad Tahir1, Mohamed Hadi Habaebi2

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

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary

Securing Internet of Things (IoT) networks is vital. This study reviews machine learning-based authentication and authorization schemes to enhance IoT security against evolving threats.

Keywords:
Internet of ThingsIoTauthenticationauthorizationmachine learningsecurity

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning Applications

Background:

  • Internet of Things (IoT) adoption faces significant security challenges, particularly in critical infrastructure.
  • Existing authentication and authorization (AA) methods are insufficient for large-scale, resource-constrained IoT environments.
  • Security is a major national and global priority for IoT networks like power grids and water systems.

Purpose of the Study:

  • To review recent advances in authentication and authorization techniques for IoT networks.
  • To present a taxonomy of IoT AA schemes, emphasizing machine learning-based approaches.
  • To analyze security threats, challenges, and criteria for resilient IoT AA implementations.

Main Methods:

  • Comprehensive literature review of IoT authentication and authorization techniques.
  • Development of a taxonomy categorizing AA schemes, with a focus on machine learning.
  • Analysis of security threats, challenges, and resilience criteria for IoT AA.

Main Results:

  • Identified limitations of current AA schemes in addressing IoT scale and device constraints.
  • Categorized and analyzed various machine learning-based AA schemes for IoT.
  • Evaluated criteria for enhancing AA resiliency in IoT security.

Conclusions:

  • Machine learning offers promising solutions for improving IoT authentication and authorization.
  • Addressing open issues and future research directions is crucial for secure IoT communication.
  • A robust taxonomy aids in understanding and developing advanced IoT security strategies.