Authors : Abdulrahman Tunde Alabelewe; Rupert Waboke, William; Serestina Viriri; Adeyinka Samson; Obunike Arinze Ubadike; Omotayo Paul Ale

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5xjudmzf

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DOI : https://doi.org/10.38124/ijisrt/26apr311

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The Internet of Things (IoT) is becoming popular in recent times. This evolution has created more cybersecurity threats, particularly Distributed Denial of Service (DDoS) attacks. Several studies have achieved success in using single machine learning algorithms with impressive results. But DDoS attacks have become sophisticated and complex, and successful attacks have increased in recent times. Research has suggested the use of the ensemble learning technique, which promises to be more effective in mitigating these complex attacks. The study examines the vulnerabilities in IoT architectures that expose them to DDoS attacks, analyzes traditional machine learning approaches and their limitations, and evaluates the effectiveness of various ensemble learning methods, which include bagging, stacking, boosting, and voting techniques. The analysis reveals that ensemble techniques achieve superior detection accuracy and adaptability compared to standalone approaches while addressing the computational constraints inherent in IoT environments. This review contributes to the ongoing discussion about the development of effective cybersecurity solutions for increasingly complex and vulnerable IoT ecosystems.

Keywords : Internet of Things, DDoS Attacks, Ensemble Learning, Voting Classifier, Stacking Classifier, Machine Learning, Cybersecurity.

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