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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Intelligent Engineering
Nurhidayati Nurhidayati
Institut Ahmad Dahlan Probolinggo
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Improving Random Forest Performance for Botnet Attack Detection in IoT Big Data Using Remove Frequent Values Filter Imam Marzuki; Mas Ahmad Baihaqi; Hartawan Abdillah; Dwi Iryaning Handayani; Nurhidayati Nurhidayati
International Journal of Electrical and Intelligent Engineering Vol 1, No 1 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i1.34533

Abstract

This research aims to enhance the performance of the Random Forest algorithm in classifying big data within the Internet of Things (IoT) domain, specifically for detecting botnet attacks. The study utilizes the N-BaIoT dataset, comprising 150,000 instances of IoT network traffic categorized into normal and anomalous (botnet) data. To optimize classification outcomes, a preprocessing technique—the “remove frequent values” filter—is applied to reduce redundancy and improve computational efficiency. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that this filter improves classification accuracy from 99.976% to 99.998%, with precision, recall, and F1-score all reaching 1.000. Cross-validation was conducted to ensure the robustness of these results. These findings suggest that even lightweight preprocessing techniques can significantly enhance machine learning performance in IoT big data classification tasks. 
Co-Authors Dwi Iryaning Handayani Hartawan Abdillah Imam Marzuki Mas Ahmad Baihaqi