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Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning.

Ahmad Alzahrani¹, Mohammed Alshehri², Rayed AlGhamdi¹

¹Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Healthcare (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces an improved wireless medical cyber-physical system (IWMCPS) using machine learning to enhance security for patient data. The IWMCPS framework effectively detects and classifies cyber threats, ensuring data confidentiality and system reliability in healthcare.

Keywords:

attacks classification data machine learning medical cyber-physical systems security schemes

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Area of Science:

Cyber-physical systems
Machine Learning
Healthcare Security

Background:

Medical cyber-physical systems (MCPS) leverage IoT sensors and machine intelligence for patient data management.
Wireless MCPS are crucial in healthcare, but vulnerable to cyber threats impacting patient privacy and data integrity.
Existing systems face challenges in reliability, security, and transparency due to device heterogeneity.

Purpose of the Study:

To propose an improved wireless medical cyber-physical system (IWMCPS) for enhanced security and reliability.
To develop a machine learning-based framework for detecting and classifying cyber threats in MCPS.
To present a patient-centric architecture ensuring secure data exchange and privacy.

Main Methods:

Developed an IWMCPS framework with three core components: communication/monitoring, computation/safety, and resource management.
Employed deep neural networks for attack detection and classification within the IWMCPS.
Evaluated the system using real patient data against attacks like data modification, DoS, and data injection.

Main Results:

The proposed IWMCPS achieved a high detection accuracy of 92% for cyber threats.
The system demonstrated a low computational time of 13 seconds with minimal error analysis.
The patient-centric architecture effectively controlled data exchange accessibility via end-user smartphones.

Conclusions:

The IWMCPS offers a robust solution to mitigate cyber-physical threats in healthcare.
Machine learning, particularly deep neural networks, is effective for securing wireless medical systems.
The developed framework enhances patient data protection, reliability, and privacy in MCPS.