XGBoostModel Optimization Using PCAfor Classification of Cyber Attacks on The Internet ofThings

Ramadan, Afrijal Rizqi, Hariyadi, M. Amin ORCID: https://orcid.org/0000-0001-9327-7604 and Almais, Agung Teguh Wibowo ORCID: https://orcid.org/0000-0001-7770-1954 (2025) XGBoostModel Optimization Using PCAfor Classification of Cyber Attacks on The Internet ofThings. International Journal of Advances in Data and Information Systems, 6 (3). pp. 850-862. ISSN 2721-3056

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Abstract

The rapid expansion of the Internet of Things (IoT) ecosystem has increased its susceptibility to cyberattacks, creating a critical need for reliable Intrusion Detection Systems (IDS). However, IDS performance is often hindered by severe class imbalance, high-dimensional features, and similarities among attack behaviors. This study proposes an optimized XGBoost model enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) and Principal Component Analysis (PCA) to address these challenges. A systematic grid-search procedure was employed to ensure transparency, reproducibility, and optimal hyperparameter selection. The original imbalance ratio of approximately 1:27 was successfully normalized to nearly 1:1 through SMOTE. The Gotham dataset used in this study consists of roughly 350,000 IoT traffic records across eight attack categories. Five data-splitting scenarios (50:50 to 90:10) were evaluated using stratified hold-out validation supported by k-fold cross-validation. he optimized model achieved 99.68% accuracy, while extremely high AUC values approaching 1.0 were carefully validated to eliminate potential data leakage.Naive Bayes,Logistic Regression, Support Vector Machine, and Deep Neural Network were included as baseline comparisons. The results demonstrate that combining SMOTE and PCA significantly improves model stability and generalization on imbalanced IoT traffic, confirming the effectiveness of the proposed XGBSP method.

Item Type:	Journal Article
Keywords:	Cyber Attack, Internet of Things,Smart City.XGBoost, Principal Component Analysis, SMOTE
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified 08 INFORMATION AND COMPUTING SCIENCES > 0805 Distributed Computing > 080503 Networking and Communications
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Mokhamad Amin Hariyadi
Date Deposited:	13 Jul 2026 09:22

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