FoSDeT: a new hybrid machine learning model for accurate and fast detection of IoT botnet
Irfan Syamsuddin, David Al-Dabass · Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu · 2025
Purpose. This study is aimed at introducing a new hybrid machine learning model to enhance the accuracy and speed in detecting botnet attacks in Internet of Things networks. The new model is derived from an integration of decision tree algorithm and feature selection algorithms to produce a novel hybrid machine learning for better performance in IoT botnet detection. Methodology. The study adopts a six steps research methodology. It consists of dataset collection, dataset preprocessing, applying machine learning, comparing feature selection algorithms, combining both machine learning and feature selection algorithms, and finally comparing the results. Findings. A novel hybrid machine learning (ML) model called FoSDeT has been obtained as a result of combination of decision tree algorithm and feature selection algorithm called Forward Selection which shows a significant improvement in IoT botnet detection in comparison to standard decision tree model. Originality. The paper proposes a simple yet powerful hybrid approach which integrates Decision Tree algorithm with two pre-defined feature selection algorithms namely, Forward Selection and Backward Elimination. The new hybrid model called FoSDeT shows a significant enhancement in terms of IoT botnet detection. Practical value. The hybrid model obtained from this study might be used by IT security practitioners in developing real intrusion detection system for defending IoT networks from botnet attacks.