A Robust Ensemble Learning for DDoS Attack Classification on the Internet of Medical Things

Nisrina Nurhaliza; Sudianto Sudianto; Pradana Ananda Raharja

doi:10.18196/jrc.v7i1.26416

Akses terbuka Jurnal SINTA 1

A Robust Ensemble Learning for DDoS Attack Classification on the Internet of Medical Things

Journal of Robotics and Control (JRC) · Vol. 7 No. 1 (2026) · hlm. 3308–3321

DOI: 10.18196/jrc.v7i1.26416
ISSN: 2715-5072, 2715-5056
Terbit: 2026-01-31
Penerbit: Universitas Muhammadiyah Yogyakarta
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

Distributed Denial of Service (DDoS) attacks pose significant threats to the Internet of Medical Things (IoMT), potentially disrupting critical healthcare services. A significant challenge in detecting these attacks is the high imbalance in network traffic data, which can bias classification models. This study introduces a hybrid approach that integrates the XGBoost algorithm with Principal Component Analysis (PCA) and undersampling to address class imbalance and enhance detection performance. Three configurations were tested: (1) XGBoost + PCA + Undersampling, (2) XGBoost + PCA, and (3) XGBoost + Undersampling. The model was evaluated using a publicly available multiclass DDoS dataset under an 80:20 training-testing split. The XGBoost + Undersampling method achieved the highest performance, with accuracy, precision, recall, and F1-score of 99.98%. Despite these results, potential limitations—such as data loss due to undersampling and excluding cross-validation or external testing. Thus, the proposed ensemble learning technique has proven to robustly improve the performance of DDoS attack detection in unbalanced dataset conditions

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Telkom University
Universitas Muhammadiyah Malang

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Rekan penulis	Organisasi	Artikel bersama
Dasril Aldo	Telkom University	1
Ahmad Fauzi	Universitas Muhammadiyah Malang	1
Ajeng Dyah Kurniawati	Telkom University	1
Dedy Agung Prabowo	Telkom University	1
Wahyu Andi Saputra	Telkom University	1
Feri Yasin	Telkom University	1
Satya Helfi Agustianto	Telkom University	1
Farhan Aryo Pangestu	Telkom University	1
Gilang Sulaeman	Telkom University	1
Bunga Laelatul Muna	Telkom University	1
Muhammad Lulu Latif Usman	Telkom University	1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">A Robust Ensemble Learning for DDoS Attack Classification on the Internet of Medical Things</dc:title>
	<dc:creator>Nisrina Nurhaliza </dc:creator>
	<dc:creator>Sudianto Sudianto</dc:creator>
	<dc:creator>Pradana Ananda Raharja </dc:creator>
	<dc:subject xml:lang="en-US">DDoS detection</dc:subject>
	<dc:subject xml:lang="en-US">ensemble learning</dc:subject>
	<dc:subject xml:lang="en-US">class imbalance</dc:subject>
	<dc:subject xml:lang="en-US">IoMT security</dc:subject>
	<dc:subject xml:lang="en-US">XGBoost</dc:subject>
	<dc:description xml:lang="en-US">Distributed Denial of Service (DDoS) attacks pose significant threats to the Internet of Medical Things (IoMT), potentially disrupting critical healthcare services. A significant challenge in detecting these attacks is the high imbalance in network traffic data, which can bias classification models. This study introduces a hybrid approach that integrates the XGBoost algorithm with Principal Component Analysis (PCA) and undersampling to address class imbalance and enhance detection performance. Three configurations were tested: (1) XGBoost + PCA + Undersampling, (2) XGBoost + PCA, and (3) XGBoost + Undersampling. The model was evaluated using a publicly available multiclass DDoS dataset under an 80:20 training-testing split. The XGBoost + Undersampling method achieved the highest performance, with accuracy, precision, recall, and F1-score of 99.98%. Despite these results, potential limitations—such as data loss due to undersampling and excluding cross-validation or external testing. Thus, the proposed ensemble learning technique has proven to robustly improve the performance of DDoS attack detection in unbalanced dataset conditions</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2026-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/26416</dc:identifier>
	<dc:identifier>10.18196/jrc.v7i1.26416</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 7 No. 1 (2026); 3308-3321</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/26416/12267</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Nisrina Nurhaliza , Sudianto Sudianto, Pradana Ananda Raharja </dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>