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PENERAPAN METODE EXTREME GRADIENT BOOSTING UNTUK DETEKSI MULTI-CLASSIFICATION SERANGAN SIBER

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In the developing digital era, cybersecurity threats are increasing. One of the solutions commonly used in securing networks is the Network Intrusion Detection System (NIDS). To improve the performance of NIDS, this study applies the Machine Learning (ML) method, namely the Extreme Gradient Boosting (XGBoost) method, because it is considered to have high performance and its ability to handle complex and imbalanced data. Therefore, this study aims to evaluate the performance of the XGBoost algorithm in detecting various types of cyber attacks using five benchmark datasets commonly used in intrusion detection systems, namely NSL-KDD, UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and ToN-IoT. In this study, XGBoost is used as the main algorithm to detect and classify various types of cyber attacks in the form of multi-class classification. Therefore, in this study has stages of pre-processing, feature selection, and hyperparameter tuning optimization. Also, several main evaluation metrics are used such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC.

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Detail Information

Series Title

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Call Number

T1842022025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, 95 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.807

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Keamanan Data
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEW

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