Enhancing IoT Security Through User Categorization and Aberrant Behavior Detection Using RBAC and Machine Learning

field_of_study

Blank

university

Al-Azhar University – Gaza

authors

Alshawwa Izzeddin A.O.; Bin Yahaya N.A.; mahmoud A.Y.

authors_full_names

Alshawwa Izzeddin, A.O. (59498480800); Bin Yahaya, Nor Adnan (59498822000); mahmoud, Ahmed Y. (35113538500)

authors_id

59498480800; 59498822000; 35113538500

year

2024

source_title

International Journal of Advanced Computer Science and Applications

article_title

Enhancing IoT Security Through User Categorization and Aberrant Behavior Detection Using RBAC and Machine Learning

volume

15

issue

12

page_start

638

page_end

647

publisher

Science and Information Organization

affiliations

University Malaysia of Computer Science and Engineering (UNIMY), Malaysia; Faculty of Engineering and Information Technology, Al-Azhar University-Gaza, Palestine

authors_with_affiliations

Alshawwa Izzeddin A.O., University Malaysia of Computer Science and Engineering (UNIMY), Malaysia; Bin Yahaya N.A., University Malaysia of Computer Science and Engineering (UNIMY), Malaysia; mahmoud A.Y., Faculty of Engineering and Information Technology, Al-Azhar University-Gaza, Palestine

cited_by

0

doi

10.14569/IJACSA.2024.0151265

author_keywords

aberrant user behavior; classification; IF classification methods; IoT user dataset and user categorization; LOF; Machine learning; Role-Based Access Control (RBAC); SVM

index_keywords

Access control; Classification (of information); Contrastive Learning; Support vector machines; Aberrant user behavior; Classification methods; Forest classification; Internet of thing user dataset and user categorization; Isolation forest classification method; Local Outlier Factor; Machine-learning; Role-based Access Control; Support vectors machine; User behaviors; Adversarial machine learning