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DOI: 10.14569/IJACSA.2020.0110243
PDF

Machine Learning based Access Control Framework for the Internet of Things

Author 1: Aissam Outchakoucht
Author 2: Anas Abou El Kalam
Author 3: Hamza Es-Samaali
Author 4: Siham Benhadou

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The main challenge facing the Internet of Things (IoT) in general, and IoT security in particular, is that humans have never handled such a huge amount of nodes and quantity of data. Fortunately, it turns out that Machine Learning (ML) systems are very effective in the presence of these two elements. However, can IoT devices support ML techniques? In this paper, we investigated this issue and proposed a twofold contribution: a thorough study of the IoT paradigm and its intersections with ML from a security perspective; then, we actually proposed a holistic ML-based framework for access control, which is the defense head of recent IT systems. In addition to learning techniques, this second pillar was based on the organization and attribute concepts to avoid role explosion problems and applied to a smart city case study to prove its effectiveness.

Keywords: Access control; internet of things; machine learning; security; smart city

Aissam Outchakoucht, Anas Abou El Kalam, Hamza Es-Samaali and Siham Benhadou, “Machine Learning based Access Control Framework for the Internet of Things” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110243

@article{Outchakoucht2020,
title = {Machine Learning based Access Control Framework for the Internet of Things},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110243},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110243},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Aissam Outchakoucht and Anas Abou El Kalam and Hamza Es-Samaali and Siham Benhadou}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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