Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

Mohammed Tawfik

doi:10.1371/journal.pone.0304082

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Open Access

Peer-reviewed

Research Article

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

Mohammed Tawfik

Roles Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: kmkhol01@gmail.com

Affiliation Faculty of Computer and Information Technology, Sana’a University, Sana’a, Yemen

https://orcid.org/0000-0002-1227-387X

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Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

Mohammed Tawfik

Published: August 1, 2024
https://doi.org/10.1371/journal.pone.0304082

About the Authors

Mohammed Tawfik

Roles Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: kmkhol01@gmail.com

Affiliation Faculty of Computer and Information Technology, Sana’a University, Sana’a, Yemen

https://orcid.org/0000-0002-1227-387X

Competing Interests

The authors have declared that no competing interests exist.

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