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

doi:10.61132/prosemnasproit.v2i2.104

Authors

Muhammad Ilham Mansis Universitas Dinamika Bangsa
Riza Pahlevi Universitas Dinamika Bangsa
Ronald Naibaho Universitas Dinamika Bangsa
Eko Arip Winanto Universitas Dinamika Bangsa

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.104

Keywords:

Bayesian Optimization, Deteksi Intrusi, Internet of Things, Mirai, Recurrent Neural Network

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.

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