Hybrid Ensemble Model for Real-Time Intrusion Detection in IoT Networks Using Machine Learning and Deep Learning Techniques

Gregorius Airlangga(1*),

(1) Universitas Katolik Indonesia Atma Jaya, Indonesia
(*) Corresponding Author

Abstract


The rapid growth of the Internet of Things (IoT) has introduced new security challenges, as IoT devices are increasingly vulnerable to sophisticated cyberattacks. This study proposes a hybrid ensemble model combining classical machine learning algorithms (Random Forest, Gradient Boosting) with deep learning (Multi-Layer Perceptron) to improve the detection of malicious activities in IoT networks. The model leverages the RT-IoT2022 dataset, which includes diverse attack patterns such as DDoS, Brute-Force SSH, and Nmap scanning. The integration of these models using a Voting Classifier achieves superior performance by exploiting the strengths of each individual model. Evaluation results demonstrate that the hybrid model outperforms its individual components, achieving an accuracy of 99.80%, precision of 99.80%, recall of 99.80%, and F1-score of 99.80%. The proposed system demonstrates strong generalization across both frequent and rare attack types, making it well-suited for real-world IoT environments where high accuracy and low false-positive rates are critical. This study contributes to the development of robust and scalable intrusion detection systems that can adapt to evolving threats in real-time

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DOI: https://doi.org/10.30645/kesatria.v5i4.523

DOI (PDF): https://doi.org/10.30645/kesatria.v5i4.523.g518

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