Hybrid Ensemble Learning Network Security System To Improve Anomaly Detection Performance
DOI:
https://doi.org/10.29407/noe.v8i02.25617Keywords:
Anomaly Detection, Cyber Attack, Cyber Security, Hybrid Ensemble, Network Intrusion Detection SystemAbstract
Cyber attacks such as zero-day attacks and APTs are serious challenges for network intrusion detection systems, especially those that still rely on signature-based methods. This study aims to design a hybrid ensemble learning-based network anomaly detection system by combining the Isolation Forest, K-Means, and Random Forest algorithms using the majority voting method. The research process includes data preprocessing, training, and model evaluation using the CSE-CIC-IDS2018 public dataset. Evaluation is carried out using accuracy, precision, recall, F1-score, and AUC metrics. The results show that this hybrid approach improves detection accuracy by up to 99.9% and significantly reduces false positives compared to a single approach. The proposed system is proven to be more adaptive and efficient in identifying various cyber attack patterns, as well as contributing to the development of more reliable network security technology.
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Copyright (c) 2025 Rony Heri Irawan, Nico Adi Saputra, Umi Mahdiyah
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