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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Penelitian Pendidikan IPA (JPPIPA) Dinamisia: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Archive: Jurnal Pengabdian Kepada Masyarakat J-COSCIS : Journal of Computer Science Community Service CONSEN: Indonesian Journal of Community Services and Engagement JURNAL KARYA ILMIAH MULTIDISIPLIN INOVTEK Polbeng - Seri Informatika
Loneli Costaner
Fakultas Ilmu Komputer, Universitas Lancang Kuning
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Optimizing Random Forest for IoT Cyberattack Detection using SMOTE: A Study on CIC-IoT2023 Dataset Guntoro, Guntoro; Lisnawita, Lisnawita; Costaner, Loneli
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5382

Abstract

The growing number of Internet of Things devices has led to an increased risk of complex and diverse cyberattacks. However, a significant challenge in this domain is the imbalanced class distribution in most Internet of Things datasets, cautilizing classification algorithms to be biased towards the majority class, hindering effective threat detection. This study addresses this issue by leveraging the Random Forest algorithm optimised by the Synthetic Minority Oversampling Technique. This research aims to develop an effective model for detecting cyberattacks in Internet of Things environments by resolving class imbalance issues inside of the CIC-IoT2023 dataset. The methodology involves several stages, comprising data preprocessing and applying Synthetic Minority Oversampling Technique for data balancing. The balanced dataset was then used to train a Random Forest model, by its performance evaluated utilizing accuracy, precision, recall, F1-score, and Cohen's Kappa metrics. The results demonstrate the model's effectiveness, achieving an accuracy of 99.01%, an F1-score of 98.96%, and a Cohen's Kappa of 98.92%. This marks a notable improvement in performance, particularly in detecting minority classes, compared to the model trained devoid of Synthetic Minority Oversampling Technique, that struggled to identify several less common attack types. The outcomes suggest that combining Random Forest by Synthetic Minority Oversampling Technique can significantly enhance the development of intrusion detection systems by improving detection accuracy for all 33 attack types and reducing the risks associated by undetected threats. In conclusion, this study advances Internet of Things cybersecurity by presenting an effective and efficient method for addressing data imbalance in attack detection. Future research should focus on evaluating the model's robustness utilizing more complex datasets and enhancing its performance for real-time deployment on resource-constrained Internet of Things Devices.
Co-Authors Abdullah Abdullah Ahmad Zamsuri, Ahmad Alfarasy, Febrizal Alyauma Hajjah Antonius Fernando Bayu Febriadi, Bayu Chan, Andhi Darmanta Sukrianto Deny Jollyta Dwi Oktarina Guntoro Guntoro Guntoro Guntoro Guntoro Guntoro, Guntoro Gusrianty Gusrianty Gustientiedina Hadi Dwi Putra Hutri Rizkiyah Alda Johan Johan Johar, Olivia Anggie Lisnawita Lisnawita Lisnawita Lisnawita Lisnawita Lucky Lhaura Van FC, Lucky Lhaura Mariza Devega Mhd. Arief Hasan, Mhd. Arief Musfawati Mustika Mustika Pandu Pratama Putra, Pandu Pratama Samsudin, Samsudin Sutejo Sutejo Wahyu Joni Kurniawan Wenni Syafitri, Wenni Wenny Syafitri Yitro Amazihono Yogi Yunefri, Yogi Yuhelmi Yuhelmi