Enhancement of Secure Hospital Healthcare Monitoring System Based–Software Defined Network (SDN) with Machine Learning - Enhancement of Secure Hospital Healthcare Monitoring System

Aug 2, 2026

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Handling delicate healthcare knowledge demands strong security to block unauthorized use of sensitive patient data. Software-defined networks (SDNs) are increasingly adopted for efficient resource management and improved network control, but they also enlarge the attack surface through diverse cyber threats. The study proposes a link between SDN technology and machine-learning-based attacks in healthcare, and builds an SDN-oriented framework to model connectivity between “nano” in-body networks and medical providers. It evaluates MLCAH defenses across different ML approaches and attack types by measuring algorithmic efficiency and trade-offs, supporting more reliable protection for clinical systems.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

SDN adoption and benefits in healthcare

Security, privacy, and attack growth

Insider threats and SDN limitations

Toward nano-network and ML-based protection

Abstract

INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION [journal homepage : www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv) Enhancement of Secure Hospital Healthcare Monitoring System Based–Software Defined Network (SDN) with Machine Learning Sarah Shihab Ahmed a,*, Huda Rashid Shakirb a The First Rusafa Directorate, Ministry of Education, Iraq b The Third Karkh Directorate, Ministry of Education, Iraq Corresponding author:*[sarahshihab1989@uomustansiriyah.edu.iq](sarahshihab1989@uomustansiriyah.edu.iq) Abstract—Handling delicate and crucial knowledge by healthcare providers requires security measures to prevent unapproved use. Software-defined networks (SDNs) are extensively used in medical facilities to ensure resource efficiency, security, and superior network management and management. Despite these advantages, SDNs present a significant threat from various assaults due to the sensitivity of patient information. Our work's primary goal is to propose a global connection between SDN technology and machine learningbased assaults in healthcare. This paper aims to draw attention to a few relevant options. Additionally, we give a framework using software-defined network principles that illustrate linkages between a collection of people, each of whom has a Nano network residing within their bodies, and medical providers via the local network of a medical center. In health care, the initiative is sometimes called an issue of machine learning assault systems and amenities. The current possibilities for machine learning cyberattacks on the medical industry are quite promising. It is also highly well-liked because of its capacity to identify and assess. From a single instrument to the enormous amounts of data gathered, this evolution radically changes how we approach medicine. This work uses a range of ML approaches and attacks to test MLCAH (Machine Learning-based Cyber Attacks Healthcare). For every combination of machine learning methods and assaults, an efficiency assessment highlights the benefits and drawbacks of different algorithms for defending against a specific assault. Keywords—Software defined networking; medical networks; network security; healthcare systems. Manuscript received 16 Dec. 2024; revised 11 Feb. 2024; accepted 21 May 2024. Date of publication 31 Dec. 2024. International Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License. I. INTRODUCTION SDNs have seen widespread application in various industries in recent years, primarily due to their benefits as dependable network technologies that enable network management and control by decoupling the planes that handle data and control. The structure of SDN offers further details on the state of the network, from the controllers to its programs, compared with conventional networks, which provide application comprehension [1]. Considering the rapid development of information and communication technologies (ICT) in recent years, medical facilities have started to use various standard technology, apps, and processes used by businesses in other industries. This was to be predicted, given that medical devices that are linked or linked online can improve the administration of assets, interactions, and digital medical records, as well as other necessities that save costs. Healthcare organizations are anticipated to spend a lot on networking technology in the coming decades, even though costs for medical supplies are anticipated to decline by 15 to 30%[2]. Additionally, due to the stringent regulations of the medical field, security and the privacy of user knowledge are both issues that are the most important in most instances of computer systems, along with the security of structures and machines. The most recent McAfee report must emphasize that connected healthcare equipment could uncover security flaws in the healthcare industry's endeavor to combine

FAQs

Why are security measures necessary for healthcare monitoring systems?

Healthcare providers handle highly sensitive patient information, so security controls are required to prevent unauthorized access and misuse of delicate knowledge.

What role do SDNs play in medical facilities according to the paper?

SDNs are used to support efficient resource management and superior network control by decoupling data and control planes, which can improve network visibility through controllers and applications.

How does the paper connect SDN with machine learning cyberattacks?

It proposes a framework that models the linkage between SDN technology and machine-learning-based attacks, using ML approaches and attack scenarios to test MLCAH and assess defensive efficiency.

Aug 2, 2026

Upload by Riley