Oyelakin A. M.

Work place: Department of Computer Science, College of Information and Communication Technology, Crescent University, Abeokuta, Nigeria

E-mail: moruff.oyelakin@cuab.edu.ng

Website: https://orcid.org/0000-0003-2844-4837

Research Interests:

Biography

Oyelakin A. M. is an academic, IT professional and technical author.He obtained National Diploma (Distinction Classification), B.Sc., M.Sc and PhD.in Computer Science. After graduation, he worked for some years in the IT industry in different capacities and later became a lecturer in the university on full-time basis. He has published over forty-six peer reviewed papers in journals and conference proceedings. His current areas of research interest are: Computer Networks, Cyber Security, Machine Learning, Intelligent Systems and Object Detection.

Author Articles
Novel Machine Learning Approaches for Identifying Attacks in IoT-based Smart Home Environment

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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