Enhanced DDoS Attack Detection in IoT Environments Using Voting and Stacking Ensemble Learning: Implementation and Performance Analysis

Authors

  • Abdulrahman Tunde Alabelewe Department of Informatics, Kaduna State University, Kaduna, Nigeria & Department of Cyber Security, Airforce Institute of Technology, Kaduna, Nigeria Author
  • Muhammad Aminu Ahmad Department of Secure Computing, Kaduna State University, Kaduna, Nigeria Author
  • Ahmed Abubakar Aliyu Department of Secure Computing, Kaduna State University, Kaduna, Nigeria Author
  • Mohammed Ibrahim Department of Cyber Security, Nigerian Defence Academy, Kaduna, Nigeria Author
  • Abubakar Muazu Ahmed Department of Secure Computing, Kaduna State University, Kaduna, Nigeria Author
  • Saadatu Abdulkadir Department of Secure Computing, Kaduna State University, Kaduna, Nigeria Author

DOI:

https://doi.org/10.56919/usci.2542.017

Keywords:

Internet of Things, DDoS attacks, ensemble learning, machine learning, voting classifier, stacking classifier, IoT Security

Abstract

Due to the increase in the adoption of Internet of Things (IoT) devices, there has been a significant increase in Distributed Denial of Service (DDoS) attack. This is because IoT devices have introduced significant security vulnerabilities, thereby increasing the attack surface. This paper aims to present an enhanced DDoS Attack detection in an IoT environment using an ensemble learning approach. This was achieved by implementing the voting and stacking classifiers that combine four supervised learning algorithms: Random Forest, Decision Trees, Logistic Regression, and K-Nearest Neighbors. Using the comprehensive CIC-IoT2023 dataset, the results of the test conducted indicate outstanding performance, with the voting classifier achieving 99.39% accuracy (190.9872ms inference time, 32 false positives) and the stacking classifier reaching 99.40% accuracy (224.9587ms inference time, 89 false positives), a 5-fold stratified cross-validation was conducted which validated the models' robustness as a significant improvement on previous study conducted in this area.

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Published

2025-06-30

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Vol. 4 No. 2 (2025)

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How to Cite

Alabelewe, A. T., Ahmad, M. A., Aliyu, A. A., Ibrahim, M., Ahmed, A. M., & Abdulkadir, S. (2025). Enhanced DDoS Attack Detection in IoT Environments Using Voting and Stacking Ensemble Learning: Implementation and Performance Analysis. UMYU Scientifica, 4(2), 142-157. https://doi.org/10.56919/usci.2542.017

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