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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence
Muhammad Mamman Kontagora
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Materials Science & Nanotechnology

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Machine Learning Algorithms for Anomaly Detection in IoT Networks – A Review Muhammad Mamman Kontagora; Bartholomew Idoko
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.
Co-Authors Bartholomew Idoko