Enhancing attack detection in IoT through integration of weighted emphasis formula with XGBoost

Al Amien, Januar and Ab Ghani, Hadhrami and Md Saleh, Nurul Izrin (2025) Enhancing attack detection in IoT through integration of weighted emphasis formula with XGBoost. Indonesian Journal Of Electrical Engineering And Computer Science, 38 (1). pp. 641-648. ISSN 2502-4752

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Abstract

This research addresses the challenge of detecting attacks in the internet of things (IoT) environment, where minority classes often go unnoticed due to the dominance of majority classes. The primary objective is to introduce and integrate the imbalance ratio formula (IRF) into the XGBoost algorithm, aiming to provide greater emphasis on minority classes and ensure the model's focus on attack detection, particularly in binary and multiclass scenarios. Experimental validation using the IoTID20 dataset demonstrates the significant enhancement in attack detection accuracy achieved by integrating IRF into XGBoost. This enhancement contributes to the consistent improvement in distinguishing attacks from normal traffic, thereby resulting in a more reliable attack detection system in complex IoT environments. Moreover, the implementation of IRF enhances the robustness of the XGBoost model, enabling effective handling of imbalanced datasets commonly encountered in IoT security applications. This approach advances intrusion detection systems by addressing the challenge of class imbalance, leading to more accurate and efficient detection of malicious activities in IoT networks. The practical implications of these findings include the enhancement of cybersecurity measures in IoT deployments, potentially mitigating the risks associated with cyber threats in interconnected smart environments.

Item Type:	Article
Uncontrolled Keywords:	Imbalanced ratio, Internet of things, Intrusion detection system, IoTID20 dataset, Machine learning, Weighted XGBoost
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	21 Jul 2026 03:29
Last Modified:	21 Jul 2026 03:29
URI:	http://eprints.utem.edu.my/id/eprint/30061
Statistic Details:	View Download Statistic

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