Securing Wireless Infusion Pumps In Healthcare Delivery Organizations

O'Brien, Gavin W.; Edwards, Sallie; Littlefield, Kevin; McNab, Neil; Wang, Sue; Zheng, Kangmin

doi:10.6028/nist.sp.1800-8

Cited by 11 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current TOF-Cuff architecture considered in this paper includes an Ethernet output, but an optional Wi-Fi output should be available for the new device. In this case some guidance for securing wireless medical devices, such as [24], should be considered. • The clear diagrams with stakeholder viewpoints are very helpful for the understanding of all stakeholders.…”

Section: Discussion and Learned Lessonsmentioning

confidence: 99%

Model-Based System, Safety and Security Co-Engineering Method and Toolchain for Medical Devices Design

Sango

Godot

González

et al. 2019

2019 Design of Medical Devices Conference

View full text Add to dashboard Cite

The increasing complexity of the medical regulatory environment and the inherent complexity of medical devices, especially due to the increased use of connected devices and embedded control software, impose adoption of new methods and tools for the system design, safety and security analyses. In this paper, we propose a method and an associated toolchain to couple model-based system engineering and safety/security analyses at the design phase of medical devices. The method is compliant with ANSI/AAMI/ISO TIR57 safety and security guidance, and compatible with INCOSE Biomedical-Healthcare Model-Based Systems Engineering works. The toolchain is based on a system architecture modelling tool and supports medical device domain specific reference architecture, as well as tools for safety and security risk analyses. The proposed method and toolchain are illustrated by considering a RGB’s TOF-CUFF monitor device analyzed in the scope of the AQUAS project as a medical device use case.

show abstract

Section: Discussion and Learned Lessonsmentioning

confidence: 99%

Model-Based System, Safety and Security Co-Engineering Method and Toolchain for Medical Devices Design

Sango

Godot

González

et al. 2019

2019 Design of Medical Devices Conference

View full text Add to dashboard Cite

show abstract

“…Medical devices, such as IPs, may not allow for the addition of third‐party software, such as antivirus or antimalware programs, because the protection could negatively impact the medical device's ability to operate effectively (O'Brien et al., 2018). Because of this, it is imperative that hospitals apply the medical device's patching and updates from the manufacturer as soon as possible (Forescout Research Labs, 2020; O'Brien et al., 2018) since a compromised medical device may lead to more severe risks (Somasundaram & Thirugnanam, 2021) and thus higher losses. Therefore, the vulnerabilities of interconnected medical devices in a hospital network pose serious cyber risks.…”

Section: Medical Devices Of a Hospital Their Connections And Cybersec...mentioning

confidence: 99%

“…Typically, once a contagion infects one component of the network, the other connected components may be compromised (Kunreuther & Heal, 2003). And so, the integration of new endpoint devices such as medical components with outdated, legacy, or unsupported operating systems increases the cyber risk of healthcare networks (Coventry & Branley, 2018; He et al., 2021; Kruse et al., 2017; O'Brien et al., 2018). To account for this, we investigate how a compromised medical component due to low cybersecurity protection can jeopardize the overall cybersecurity profile of the hospital.…”

Section: Numerical Implicationsmentioning

confidence: 99%

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach

2023

View full text Add to dashboard Cite

Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient‐monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph‐theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyber risk.

show abstract

“…The National Institute of Standards and Technology (NIST) has release documentation designed to assist medical providers with securing their devices on an enterprise level network. SP 1800-8 focuses on wireless infusion pumps and lists the multiple security guidelines designed to help secure these devices [50]. While written specifically for wireless infusion pumps, the guidelines are applicable throughout the entire medical implant device ecosystem.…”

Section: Known Cybersecurity Vulnerabilities Acknowledged By the Governing Authoritymentioning

confidence: 99%

Medical Internet of Things: A Survey of the Current Threat and Vulnerability Landscape

McGowan

Sittig

Andel

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The Internet of things (IoT) is a system that utilizes the Internet to facilitate communication between sensors and devices. Given the ubiquitous nature of IoT devices, it is seemingly inevitable that IoT would be used as a conduit to transform healthcare. One such medical IoT (mIoT) device that is revolutionizing healthcare is the medical implant device. These mIoT implant devices which control insulin pumps, cardioverter defibrillators and bone growth stimulators have redefined the way patient data is accessed, and healthcare is delivered. These implant devices are a double-edged sword. While they allow for the effective and efficient noninvasive treatment of patients, this external communication makes the medical implants vulnerable to cyberattacks synonymous with IoT devices. As a result, privacy and security vulnerabilities have surfaced as pronounced challenges for mIoT devices. This work summarizes and synthesizes the inherent vulnerabilities associated with mIoT devices and the implications regarding patient safety.

show abstract

Securing Wireless Infusion Pumps In Healthcare Delivery Organizations

Cited by 11 publications

References 0 publications

Model-Based System, Safety and Security Co-Engineering Method and Toolchain for Medical Devices Design

Model-Based System, Safety and Security Co-Engineering Method and Toolchain for Medical Devices Design

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach

Medical Internet of Things: A Survey of the Current Threat and Vulnerability Landscape

Contact Info

Product

Resources

About