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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence Open Access DRIVERset Kwaghe International Journal of Sciences and Technology Open Access DRIVERset Kwaghe International Journal of Engineering and Information Technology
Idoko, Bartholomew
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences

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Journal : Open Access DRIVERset

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Machine Learning Algorithms for Anomaly Detection in IoT Networks – A Review Kontagora, Muhammad Mamman; Idoko, Bartholomew
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 1 No 2 (2024): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v1i2.4014

Abstract

Internet of Things (IoT) wide applications has significantly increased the need for robust anomaly detection to safeguard against countless security breaches. This paper presents a review that examines the effectiveness of hybrid solutions incorporating supervised and unsupervised machine learning models for enhancing IoT security. The review consolidates insights from a range of studies employing models such as Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), and Gaussian Mixture Models (GMM). It integrates the findings of diverse research, emphasizing improvements in terms of detection accuracy and computational demands. The study delineates challenges in the field to evaluate the efficacy of hybrid techniques and their potential for immediate IoT security applications. Moreover, future research directions encompass the exploration of new algorithms and the integration of these approaches within dynamic IoT data streams.
Detection of Malware Attacks in Medical Mechatronics Distribution System Using Support Vector Machine Idoko, Bartholomew; Isah, Okoro Denis; Agada, Sampson; Olofu, Samuel Owoicho
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 2 No 3 (2025): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v2i3.7231

Abstract

This study examines the cybersecurity challenges and solutions associated with medical mechatronics devices, which increasingly operate autonomously through advances in robotics, artificial intelligence (AI), and machine-to-machine communication. As the number of interconnected devices grows—from smart grids and home appliances to medical equipment and sensor–actuator testers—ensuring secure and trustworthy communication becomes critical. A sustainable defensive strategy for medical mechatronics requires robust systems capable of malware analysis and detection, informed by an understanding of cyber-attack stages such as reconnaissance, weaponization, delivery, exploitation, installation, and command and control. Traditional malware detection systems struggle with obfuscated malware, making AI and machine learning (ML) more effective tools for accurate detection and classification. This research proposes the use of a Support Vector Machine (SVM) model with a novel metric to enhance malware detection in medical mechatronics devices, thereby strengthening confidentiality, integrity, availability, and digital trust. The proposed SVM-based approach was compared with established SVM algorithms using a real dataset from medical mechatronics distribution systems across federal medical centers in Nigeria. Findings demonstrate the potential of the model to improve malware detection accuracy and compliance with digital sovereignty standards, offering practical insights for enhancing cybersecurity in critical healthcare technologies.
Co-Authors Agada, Sampson Bossah, Chika Patricia Isah, Okoro Denis Kontagora, Muhammad Mamman Nwankwo, Kenneth Olofu, Samuel Owoicho