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Future Internet
Volume 17
Issue 11
10.3390/fi17110529
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest

by
Brou Médard Kouassi
1,
Abou Bakary Ballo
1,2,
Kacoutchy Jean Ayikpa
3,4,*,
Diarra Mamadou
1,3 and
Minfonga Zié Jérôme Coulibaly
1
1
Laboratoire de Mécanique et Informatique, Université Félix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire
2
Laboratoire de Mathématiques et Informatique, Université Péléforo Gon Coulibaly, Korhogo BP 1328, Côte d’Ivoire
3
Laboratoire Imagerie et Vision Artificielle (ImViA), Université Bourgogne Europe, 21000 Dijon, France
4
Unité de Recherche et d’Expertise Numérique (UREN), Université Virtuelle de Côte d’Ivoire, Abidjan 28 BP 536, Côte d’Ivoire
*
Author to whom correspondence should be addressed.
Future Internet 2025, 17(11), 529; https://doi.org/10.3390/fi17110529 (registering DOI)
Submission received: 13 September 2025 / Revised: 12 November 2025 / Accepted: 17 November 2025 / Published: 19 November 2025
(This article belongs to the Special Issue Machine Learning and Internet of Things in Industry 4.0)
Versions Notes

Abstract

The rapid growth of the Internet of Things (IoT) has created vast networks of interconnected devices that are increasingly exposed to cyberattacks. Ensuring the security of such distributed systems requires efficient and adaptive intrusion detection mechanisms. However, conventional methods face limitations in processing large and complex feature spaces. To address this issue, this study proposes an optimized intrusion detection approach based on Top-K feature selection combined with ensemble learning models, evaluated on the CICIoMT2024 dataset. Three algorithms, XGBoost, LightGBM, and Random Forest, were trained and tested on IoT datasets using three feature configurations: Top-10, Top-15, and the complete feature set. The results show that the Random Forest model provides the best balance between accuracy and computational efficiency, achieving 91.7% accuracy and an F1-score of 93% with the Top-10 subset while reducing processing time by 35%. These findings demonstrate that the Top-K selection strategy enhances the interpretability and performance of IDSs in IoT environments. Future work will extend this framework to real-time adaptive detection and edge computing integration for large-scale IoT deployments.
Keywords: intrusion detection system (IDS); feature selection; Top-K features; classification algorithms; machine learning intrusion detection system (IDS); feature selection; Top-K features; classification algorithms; machine learning

Share and Cite

MDPI and ACS Style

Kouassi, B.M.; Ballo, A.B.; Ayikpa, K.J.; Mamadou, D.; Coulibaly, M.Z.J. Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest. Future Internet 2025, 17, 529. https://doi.org/10.3390/fi17110529

AMA Style

Kouassi BM, Ballo AB, Ayikpa KJ, Mamadou D, Coulibaly MZJ. Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest. Future Internet. 2025; 17(11):529. https://doi.org/10.3390/fi17110529

Chicago/Turabian Style

Kouassi, Brou Médard, Abou Bakary Ballo, Kacoutchy Jean Ayikpa, Diarra Mamadou, and Minfonga Zié Jérôme Coulibaly. 2025. "Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest" Future Internet 17, no. 11: 529. https://doi.org/10.3390/fi17110529

APA Style

Kouassi, B. M., Ballo, A. B., Ayikpa, K. J., Mamadou, D., & Coulibaly, M. Z. J. (2025). Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest. Future Internet, 17(11), 529. https://doi.org/10.3390/fi17110529

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