Intelligent Multi-Stage Feature Selection for Real-Time Intrusion Detection in IoV Networks: A Machine Learning Framework
Authors
Emediong Obot
Department of Computer Engineering, University of Uyo, Nigeria
Bliss Utibe-Abasi Stephen
TETFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Uyo, Nigeria
Okorafor Nwaji Godfrey
Federal University of Technology Ikot-Abasi, Akwa Ibom State, Nigeria
Sadiq Thomas
sadiqthomas@nileuniversity.edu.ng
Department of Computer Engineering, Nile University, Nigeria
Akpasam Joseph Ekanem
Department of Electrical and Electronic Engineering, Akwa Ibom State University, Nigeria
Philip Asuquo
Department of Computer Engineering, University of Uyo, Nigeria
DOI: https://doi.org/10.2478/fcds-2026-0014 | Journal eISSN: 2300-3405 (formerly 0867-6356) | Journal ISSN: 0867-6356
Language: English
Page range: 425 - 451
Submitted on: Feb 7, 2026
Accepted on: Aug 16, 2026
Published online: Oct 11, 2026
Published in print: Sep 1, 2026
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Emediong Obot, Bliss Utibe-Abasi Stephen, Okorafor Nwaji Godfrey, Sadiq Thomas, Akpasam Joseph Ekanem, Philip Asuquo, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.