Citation

Ullah, Saeed and Wu, Junsheng and Lin, Zhijun and Kamal, Mian Muhammad and Mostafa, Hala and Sheraz, Muhammad and Chuah, Teong Chee (2025) Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

The proliferation of Internet of Things (IoT) devices has created unprecedented cybersecurity vulnerabilities, with botnets emerging as a critical threat to network infrastructure. This study focuses on traditional machine learning and deep learning approaches, proposes a novel ensemble framework to address these issues, integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Random Forest (RF), and Logistic Regression (LR) via a weighted soft-voting mechanism. Our approach introduces a Quantile Uniform transformation to reduce feature skewness, a multi-layered feature selection method to enhance discriminative power, an individual performance of deep learning–traditional machine learning and a hybrid models (ensemble models) for robust detection. Evaluated on BOT-IOT, CICIOT2023, and IOT23 datasets, the framework achieves 100% accuracy on BOT-IOT, 99.2% on CICIOT2023, and 91.5% on IOT23, outperforming state-ofthe-art models by up to 6.2%. These contributions advance IoT security by enabling scalable, highperformance detection adaptable to diverse network scenarios, with practical optimizations for realworld deployment.

Item Type:	Article
Uncontrolled Keywords:	IoT, cyber-attacks, botnet, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 01:48
Last Modified:	04 Oct 2025 15:04
URII:	http://shdl.mmu.edu.my/id/eprint/14534

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