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All Journal KALBISIANA Jurnal Sains, Bisnis dan Teknologi
Nurdin Andi Baso Daeng Marewa
Universitas Kalbis
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences

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Pengembangan Model Machine Learning untuk Deteksi Serangan Siber Nurdin Andi Baso Daeng Marewa; Muhammad Adrinta Abdurrazzaq
KALBISIANA Jurnal Sains, Bisnis dan Teknologi Vol. 12 No. 3 (2026): Kalbisiana
Publisher : UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/ferek244

Abstract

This study proposes a deep learning-based Intrusion Detection System (IDS) by combining Convolutional Neural Network (CNN) and Random Forest (RF) to detect network attacks. The CICIDS2018 dataset is used as training data to recognize various types of attacks such as DDoS and Brute Force. CNN acts as a feature extractor, while RF is used for classification. This system is implemented as a web application with an interactive interface for ease of use. Test results show that the hybrid CNN-RF model achieves high accuracy (91%) and is superior to both the single CNN model and CNN-XGBoost. This approach improves attack detection accuracy and provides an adaptive and efficient solution for network security.
Co-Authors Muhammad Adrinta Abdurrazzaq