Work place: Department of Computer Science, Oyo State College of Education, Lanlate, Nigeria
E-mail: adegbolaia@oyscoel.edu.ng
Website: https://orcid.org/0000-0002-0268-1992
Research Interests:
Biography
Adegbola I. A. holds a Bachelor’s degree in Computer Science from the University of Ilorin (2006), a Master’s degree in Computer Science from the University of Ibadan (2010), and a Ph.D. in Computer Science (2020), focusing on Spambot Detection using content classification. He is currently the Head of the Department of Computer Science at Oyo State College of Education, Lanlate, Nigeria.He has to his credit several peer reviewed articles in journals and conference proceedings. His research interests include IoT, Artificial Intelligence, Cybersecurity, and Community Informatics.
By Oyelakin A. M. Sanni S. A. Adegbola I. A. Salau-Ibrahim T. T. Bakare-Busari Z. M. Saka B. A.
DOI: https://doi.org/10.5815/ijwmt.2025.02.04, Pub. Date: 8 Apr. 2025
Attackers keep launching different attacks on computer networks. Signature-based and Machine Learning (ML)-based techniques have been used to build models for promptly identifying these attacks in networks. However, ML-based approaches are more popular than their counterparts because of their ability to detect zero-day attacks. In the Internet of Things (IoT), devices are interconnected and this called for the need to guide such networks against intrusions. This study aims at building effective ML models from a recently released IoT-based Smart Home dataset. The study revealed patterns and characteristics of the IoT dataset, pre-processed it and then selected discriminant features using Binary Bat Algorithm (BBA). The pre-processing of the Smart Home IoT dataset for the study was carried out based on the issues identified during the exploratory analyses. The experimental evaluation carried out revealed that all the learning algorithms achieved promising classification results. For instance, Decision Trees recorded 98.60% accuracy, KNN produced 99.60% accuracy while Random Forest (RF) and AdaBoost-based models recorded 100.00% and 99.91% respectively. In all other metrics, RF-based attack classification model slightly recorded the best results. The study concluded that the EDA, innovative data pre-processing, BBA-based feature selection improved the classification performances of the ML approaches used in this study.
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