International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 13, Issue 1 (January 2026), Pages: 1-12

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 Original Research Paper

Cyberattack detection and prevention framework for the healthcare sector using machine learning techniques

 Author(s): 

 Ahmad Alshammari 1, *, Ali Alqarni 2

 Affiliation(s):

  1Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
  2Department of Computer Science, College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0009-0000-2051-2757

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2026.01.001

 Abstract

This paper presents a complete machine-learning framework for detecting and preventing cyberattacks in the healthcare sector. Because healthcare systems are highly vulnerable and data breaches can cause serious harm, the study seeks to address gaps in current solutions by developing an end-to-end model. Using a design science research approach, the framework includes five connected stages: data collection and preprocessing, data cleaning and feature selection, model training and evaluation, implementation and deployment, and continuous monitoring and improvement. The paper argues that this comprehensive approach, supported by comparisons with existing studies and an empirical analysis, offers a more effective and sustainable solution for healthcare cybersecurity than models that focus only on specific types of attacks.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Cybersecurity, Healthcare systems, Machine learning, Attack detection, Framework design

 Article history

 Received 27 July 2025, Received in revised form 23 November 2025, Accepted 8 December 2025

 Acknowledgment

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA, for funding this research work through the project number “NBU-FFR-2025-2990-08. “The authors are thankful to the Deanship of Graduate Studies and Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alshammari A and Alqarni A (2026). Cyberattack detection and prevention framework for the healthcare sector using machine learning techniques. International Journal of Advanced and Applied Sciences, 13(1): 1-12

  Permanent Link to this page

 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5

 Tables

  Table 1  Table 2  Table 3  Table 4

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