Hybrid Ensemble Model for Real-Time Intrusion Detection in IoT Networks Using Machine Learning and Deep Learning Techniques - Airlangga, Gregorius (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2024)
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