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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
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Computer Science & IT

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Improving Internet of Things Cyber Attack Detection with Information Gain and Decision Tree Alvin Mufidha Ahmad; Fauzi Adi Rafrastara
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/p2c33t87

Abstract

The rapid proliferation of Internet of Things (IoT) devices within digital ecosystems has enhanced efficiency and availability but has also expanded the attack surface for cyber threats. This study aims to improve intrusion detection accuracy in IoT environments by addressing two key challenges: class imbalance and high feature dimensionality. Random Undersampling (RUS) is employed to mitigate data imbalance in the CIC IoT 2023 dataset, while feature selection is performed using the filter-based Information Gain method. A decision tree classifier is implemented and validated using k-fold cross-validation to ensure result reliability. Experimental results demonstrate that the proposed approach achieves an accuracy of 88.7%, outperforming a wrapper-based method, which attained 87.3%. These findings confirm that an appropriately designed filter-based feature selection strategy can effectively enhance the performance of intrusion detection systems for IoT security.
Enhancing Feature-Efficient Network Intrusion Detection Using Gradient Boosting and Chi-Square Selection on NSL-KDD Soares, Gilardinho Javiere Oscoraldo Pedrosa; Fauzi Adi Rafrastara; Ramadhan Rakhmat Sani
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15650

Abstract

This study examines the growing complexity of cyber threats that increasingly challenge the effectiveness of traditional Network Intrusion Detection Systems (NIDS). Modern attacks, particularly zero-day intrusions, require detection approaches capable of handling high-dimensional network traffic data. However, existing studies rarely examine the trade-off between feature efficiency and generalization performance in boosting-based NIDS under controlled feature-reduction strategies. Moreover, the role of statistical feature selection in mitigating overfitting in classical boosting models remains underexplored. This study evaluates the performance of NIDS by combining boosting ensemble algorithms, namely AdaBoost, Gradient Boosting, and XGBoost, with filter-based feature selection methods, including Information Gain, Chi-Square, and ReliefF. The NSL-KDD dataset is used as the primary benchmark, with Min–Max normalization applied during preprocessing to ensure numerical feature consistency. Model development is conducted using Orange Data Mining, and performance is assessed through 10-fold cross-validation. Experimental results show that Gradient Boosting achieves the highest baseline accuracy among the evaluated models. Further performance improvements are obtained through feature selection, with the Chi-Square method yielding the best result at 81.2% accuracy using 19 selected features. Information Gain also enhances performance, achieving 80.8% accuracy with 13 features, while ReliefF provides comparatively lower gains. These findings demonstrate that effective feature reduction improves generalization performance, reduces computational complexity, and mitigates overfitting. Overall, the proposed combination of Gradient Boosting and statistical feature selection provides a feature-efficient, generalizable intrusion detection strategy for modern network environments.
Co-Authors Alvin Mufidha Ahmad Ramadhan Rakhmat Sani Soares, Gilardinho Javiere Oscoraldo Pedrosa