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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JOURNAL V-TECH (VISION TECHNOLOGY) JURNAL RISET AKUNTANSI JAMBI Quantitative Economics and Management Studies Journal of Trends Economics and Accounting Research Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Ilmiah Manajemen dan Kewirausahaan PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Modem : Jurnal Informatika dan Sains Teknologi Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
NAIBAHO, RONALD
Universitas Dinamika Bangsa
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences

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Deteksi Serangan Mirai Pada IoT Menggunakan Recurrent Neural Network (RNN) dengan Optimasi Hyperparameter Berbasis Bayesian Optimization Muhammad Ilham Mansis; Riza Pahlevi; Ronald Naibaho; Eko Arip Winanto
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.104

Abstract

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.
Co-Authors agus Nugroho Ahmad Husaein Ahmad Husaein Alfon Tuhoni Zebua Asyhadi, Ahmad Bambang Sukoco Bayu Perdana Beni Irawan Charles Sitindaon Chintia Aprilia Chintia Aprilia Dodi Sandra Eddy Suratno Effiyaldi Effiyaldi, Effiyaldi Eko Arip Winanto Febri Annisa Feranika, Ayu Firman syah, Firman Gultom, Josua Alex Gunardi Gunardi Harahap, Daniel Haposan Hendri Hendri Jasmir , Jasmir Jasmir, Jasmir Johni Paul Karolus Pasaribu Josua Alexander Gultom Khairuldi Laura Prasasti Mardiana.R Meisak, Despita Melani Kadar Miranda Miranda Muhammad Ilham Mansis Napitupulu, Abidan Nasution, Andi Saputra Nurhadi Nurhadi Nurhadi Pandiangan, Saut Maruli Tua Pareza Alam Jusia Pasaribu, Jhon Paul Carollus Purnama, Benni R. Zaevan Khazafi Putra Riki Bayu Andhika Riza Pahlefi Rohaini, Eni Saud Maruli Panjaitan Selfi Kumara Hati Senni Velawati SIBURIAN, MANANDA TURE SILITONGA, MANAOR Sinaga, Ardian Rahmat Irawan Sinurat, Windo Sitohang, Andar Sitohang, Oloan Suratno, Eddy Suwanto Vira Aryati Aryati Winanto, Eko Arif Yovi Pratama