A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

Darren Seifert,Hassan Reza

doi:10.3390/computers5040027

Abstract

This paper surveys the available system architectures for cyber-physical systems. Several candidate architectures are examined using a series of essential qualities for cyber-physical systems for healthcare. Next, diagrams detailing the expected functionality of infusion pumps in two of the architectures are analyzed. The STRIDE Threat Model is then used to decompose each to determine possible security issues and how they can be addressed. Finally, a comparison of the major security issues in each architecture is presented to help determine which is most adaptable to meet the security needs of cyber-physical systems in healthcare.

Highlights

Recent advances in embedded systems design, communication protocols, sensor technology, and mobile computing are enabling the development of a new class of system that integrates cyber space and our physical environment. While those working in the area of embedded system design are focused on designing computational models for specific applications, those working on these cyber-physical systems (CPS) are focused on establishing communications models that can reliably integrate time and feedback control into the model [1]
One area where CPS could potentially provide a much needed solution is in healthcare
In order to ensure an an end-to-end ofthe thesecurity security issues presented by each architecture, threat modeling process originally developed by Microsoft was utilized

Summary

Introduction

Recent advances in embedded systems design, communication protocols, sensor technology, and mobile computing are enabling the development of a new class of system that integrates cyber space and our physical environment. While those working in the area of embedded system design are focused on designing computational models for specific applications, those working on these cyber-physical systems (CPS) are focused on establishing communications models that can reliably integrate time and feedback control into the model [1]. Applications in aircraft and vehicle control systems, factory automation, weather forecasting, and deep sea drilling have already been identified [2].

Methods

Findings

Discussion

Conclusion