*Result*: A hybrid XGBoost-SVM ensemble framework for robust cyber-attack detection in the internet of medical things (IoMT).
Title:
A hybrid XGBoost-SVM ensemble framework for robust cyber-attack detection in the internet of medical things (IoMT).
Authors:
Abdelhaq M; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia., Palanisamy S; Department of Electronics and Communication Engineering, Alliance School of Applied Engineering, Alliance University, Bengaluru, India. skcommn2@gmail.com., Gopinath M; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. gopitamil23@gmail.com., Manasa VGS; Department of CSE, Siddhartha Academy of Higher Education, Vijayawada, India., Ram MA; Department of CSE, Siddhartha Academy of Higher Education, Vijayawada, India., Md SK; Department of CSE, Siddhartha Academy of Higher Education, Vijayawada, India.
Source:
Scientific reports [Sci Rep] 2026 Jan 31; Vol. 16 (1), pp. 6855. Date of Electronic Publication: 2026 Jan 31.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
References:
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Hady, A. A., Ghubaish, A., Salman, T., Unal, D. & Jain, R. "Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study. IEEE Access 8, 106576–106584. https://doi.org/10.1109/ACCESS.2020.3000421 (2020). (PMID: 10.1109/ACCESS.2020.3000421)
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Lokshina I., Lanting C. (2019) A Qualitative Evaluation of IoT-Driven eHealth: Knowledge Management, Business Models and Opportunities, Deployment and Evolution. In: Kryvinska N., Greguš M. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-94117-2_2.
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Thamilarasu, G., Odesile, A. & Hoang, A. An Intrusion Detection System for Internet of Medical Things. IEEE Access 8, 181560–181576 (2020). (PMID: 10.1109/ACCESS.2020.3026260)
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Liu, Y., Dong, X., Zio, E., & Cui, Y. (2025). Active Resilient Secure Control for Heterogeneous Swarm Systems Under Malicious Cyber-Attacks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–10. https://doi.org/10.1109/TSMC.2025.3580940.
Chen, P., Song, Y. & Xia, Y. Adaptively Diagnosing System Faults in Microservice Architecture: An Autonomous Predictive Model Construction Framework. Future Generation Comput. Syst. https://doi.org/10.1016/j.future.2025.108256 (2025). (PMID: 10.1016/j.future.2025.108256)
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Palanisamy, S. et al. Towards precision in IoT-based healthcare systems: a hybrid optimized framework for big data classification. J Big Data 12, 190. https://doi.org/10.1186/s40537-025-01243-1 (2025). (PMID: 10.1186/s40537-025-01243-1)
Sun, Q., Jian, X., Han, C. & Li, Y. An Improved Opportunistic Localization Algorithm Using LEO Signals Based on PSODC. IEEE Trans. Instrum. Meas. 74, 1–10. https://doi.org/10.1109/TIM.2025.3593550 (2025). (PMID: 10.1109/TIM.2025.3593550)
Palanisamy, S. N. Achieving Secured Medical Network (SMN) through Stateless Mechanism and SkeyM in Medical-Internet of Things (M-IoT). J. Eng. Appl. Sci. 71, 128. https://doi.org/10.1186/s44147-024-00460-4 (2024). (PMID: 10.1186/s44147-024-00460-4)
Nandy, S., Adhikari, M., Khan, M. A., Menon, V. G. & Verma, S. An Intrusion Detection Mechanism for Secured IoMT framework based on Swarm-Neural Network. IEEE J. Biomed. Health Inform. 26(5), 1969–1976. https://doi.org/10.1109/JBHI.2021.3101686 (2021). (PMID: 10.1109/JBHI.2021.3101686)
P. Radoglou-Grammatikis, P. Sarigiannidis, G. Efstathopoulos, T. Lagkas, G. Fragulis and A. Sarigiannidis, "A Self-Learning Approach for Detecting Intrusions in Healthcare Systems," ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1–6, https://doi.org/10.1109/ICC42927.2021.9500354 .
Saheed, Y. K. & Arowolo, M. O. Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms. IEEE Access 9, 161546–161554. https://doi.org/10.1109/ACCESS.2021.3128837 (2021). (PMID: 10.1109/ACCESS.2021.3128837)
Manimurugan, S. et al. Effective Attack Detection in Internet of Medical Things Smart Environment Using a Deep Belief Neural Network. IEEE Access 8, 77396–77404. https://doi.org/10.1109/ACCESS.2020.2986013 (2020). (PMID: 10.1109/ACCESS.2020.2986013)
T. Saba, "Intrusion Detection in Smart City Hospitals using Ensemble Classifiers," 2020 13th International Conference on Developments in eSystems Engineering (DeSE), 2020, pp. 418–422, https://doi.org/10.1109/DeSE51703.2020.9450247 .
Kumar, P., Gupta, G. P. & Tripathi, R. An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Comput. Commun. 166, 110–124. https://doi.org/10.1016/j.comcom.2020.12.003 (2021). (PMID: 10.1016/j.comcom.2020.12.003)
Swarna Priya, R. M. et al. An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput. Commun. 160, 139–149. https://doi.org/10.1016/j.comcom.2020.05.048 (2020). (PMID: 10.1016/j.comcom.2020.05.048)
Liaqat, S., Akhunzada, A., Shaikh, F. S., Giannetsos, A. & Jan, M. A. SDN orchestration to combat evolving cyber threats in Internet of Medical Things (IoMT). Comput. Commun. 160, 697–705. https://doi.org/10.1016/j.comcom.2020.07.006 (2020). (PMID: 10.1016/j.comcom.2020.07.006)
Khan, S. & Akhunzada, A. A hybrid DL-driven intelligent SDN-enabled malware detection framework for Internet of Medical Things (IoMT). Comput. Commun. 170, 209–216. https://doi.org/10.1016/j.comcom.2021.01.013 (2021). (PMID: 10.1016/j.comcom.2021.01.013)
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Grant Information:
PNURSP2026R97 Princess Nourah Bint Abdulrahman University
Contributed Indexing:
Keywords: Cyber attacks; IoMT; Machine learning; SVM; Soft voting; XGBoost
Entry Date(s):
Date Created: 20260131 Date Completed: 20260218 Latest Revision: 20260221
Update Code:
20260221
PubMed Central ID:
PMC12916823
DOI:
10.1038/s41598-026-37832-0
PMID:
41620472
Database:
MEDLINE
*Further Information*
*Today, the rise of the Internet of Medical Things (IoMT) has evolved into a highly valued global market worth billions of dollars. However, this growth has also created many opportunities for massive and advanced attack scenarios due to the vast number of devices and their interconnected communication networks. Based on recent reports, it is observed that during the Covid-19 pandemic, the necessity of the IoMT ecosystem has increased significantly. On the other hand, attackers and intruders aim to impair data integrity and patient safety with the prevalence of sophisticated cyber attacks including Man in the Middle (MITM) attacks like spoofing and data injection. In this research work, WUSTL-EHMS-2020 dataset is utilized to demonstrate a robust IoMT cyberattack detection method based on machine learning and the efficiency of the proposed model is validated by employing TON-IoT and CICIDS 2017 datasets. We offer an ensemble approach that employs Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classifiers to address the exclusive challenges in IoMT security. By utilizing the complementing advantages of SVM's decision boundary precision and XGBoost's gradient-based optimization, our model outperforms baseline techniques with a superior detection accuracy of 98.04% with WUSTL-EHMS-2020 dataset. In addition, the practicality of the proposed model is examined by considering peculiar features of IoMT like resource restrictions, medical device communication diversity, and healthcare data privacy by comparing with IoMT and IoT datasets by exposing the patterns of cyberattack in dynamic IoMT environment. Hence, this research will be considered as the pioneer for developing reliable IoMT security solutions by adapting trustworthy and scalability.
(© 2026. The Author(s).)*
*Declarations. Competing interests: The authors declare no competing interests.*