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All Journal Scientific Journal of Informatics Jurnal Teknik Informatika (JUTIF)
Pradana, Fadli Dony
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Computer Science & IT

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Journal : Jurnal Teknik Informatika (JUTIF)

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Stacked Random Forest-LightGBM for Web Attack Classification Pradana, Fadli Dony; Farikhin, Farikhin; Warsito , Budi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4950

Abstract

The rapid expansion of web services in the digital era has intensified exposure to increasingly complex and imbalanced cyber threats. This study proposes a stacking hybrid ensemble framework for web attack classification, integrating Random Forest as the base learner and LightGBM as the meta-learner, enhanced by the SMOTE technique for data balancing. The Web Attack subset of the CICIDS-2017 dataset serves as a case study, with a focus on detecting minority attacks such as SQL Injection, XSS, and Brute Force. The preprocessing pipeline includes data cleaning, removal of irrelevant features, normalization, extreme value imputation, and ANOVA F-test-based feature selection. Evaluation results indicate that the proposed model outperforms baseline models in both multiclass classification (98.7% accuracy, 0.634 macro F1-score) and binary classification (99.41% accuracy, 99.47% F1-score), while maintaining high sensitivity to minority classes. These results contribute to informatics and cybersecurity scholarship through a generalizable stacking baseline and well-specified evaluation procedures for web-attack detection, facilitating replicability, fair comparison, and dataset-agnostic insights.
Co-Authors Budi Prasetiyo, Budi Hakim, M. Faris Al Juwanda, Farikhin M. Faris Al Hakim S.Pd. M Kes I Ketut Sudiana . Warsito , Budi