Cyber-Security of Neural Networks in Medical Devices

2020 ◽  
pp. 289-297
Author(s):  
Uwe Becker
Keyword(s):  
Neural Networks ◽  
Medical Devices ◽  
Cyber Security

scholarly journals Cyber security for Medical Devices Using Block chain

2019 ◽  
Vol 9 (4) ◽  
Author(s):  
Dr. Karen C. Benson ◽  
Dr. Lorraine Jonassen ◽  
Dr. Binh Tran
Keyword(s):  
Medical Devices ◽  
Cyber Security ◽  
Block Chain

scholarly journals A Case Study of Quantizing Convolutional Neural Networks for Fast Disease Diagnosis on Portable Medical Devices

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 219
Author(s):  
Mukhammed Garifulla ◽  
Juncheol Shin ◽  
Chanho Kim ◽  
Won Hwa Kim ◽  
Hye Jung Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Medical Devices ◽  
Energy Efficient ◽  
High Performance ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Mobile System ◽  
Performance Computing

Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for healthcare professionals. Using the CNN on portable medical devices can enable a handy and accurate disease diagnosis. Unfortunately, however, the CNNs require high-performance computing resources as they involve a significant amount of computation to process big data. Thus, they are limited to being used on portable medical devices with limited computing resources. This paper discusses the network quantization techniques that reduce the size of CNN models and enable fast CNN inference with an energy-efficient CNN accelerator integrated into recent mobile processors. With extensive experiments, we show that the quantization technique reduces inference time by 97% on the mobile system integrating a CNN acceleration engine.

scholarly journals Design of Hack-Resistant Diabetes Devices and Disclosure of Their Cyber Safety

2016 ◽  
Vol 11 (2) ◽  
pp. 198-202 ◽  
Author(s):  
Jonathan Sackner-Bernstein
Keyword(s):  
Risk Assessment ◽  
Operating Systems ◽  
Medical Devices ◽  
Cyber Security ◽  
State Of The Art ◽  
Security Testing ◽  
Cyber Safety ◽  
Regulatory Bodies ◽  
Recognition And Response ◽  
Inform Decision Making

Background: The focus of the medical device industry and regulatory bodies on cyber security parallels that in other industries, primarily on risk assessment and user education as well as the recognition and response to infiltration. However, transparency of the safety of marketed devices is lacking and developers are not embracing optimal design practices with new devices. Achieving cyber safe diabetes devices: To improve understanding of cyber safety by clinicians and patients, and inform decision making on use practices of medical devices requires disclosure by device manufacturers of the results of their cyber security testing. Furthermore, developers should immediately shift their design processes to deliver better cyber safety, exemplified by use of state of the art encryption, secure operating systems, and memory protections from malware.

Death by Hacking

2019 ◽  
pp. 1500-1516 ◽  
Author(s):  
Penelope Wang
Keyword(s):  
Medical Devices ◽  
Cyber Security ◽  
Critical Infrastructure ◽  
Power Grids ◽  
The Internet ◽  
Physical Injury ◽  
Traffic Light ◽  
Insulin Pumps ◽  
Cyber Threat

Innovation and technological advancements have seen many devices and systems being linked up on to the Internet. Such devices and systems include personal medical devices like insulin pumps and pacemakers, cars, as well as critical infrastructure like power grids and traffic light systems. However, recent research by cyber security experts has revealed that these critical devices and systems are highly vulnerable to being hacked into and manipulated. Should such an attack be carried out successfully by bad actors, like violent extremists, this could result in physical injury or even death. Hence, this chapter aims to bring awareness on the kinetic cyber threat by highlighting various forms of kinetic cyber, and the vulnerabilities that make these devices and systems susceptible. In addition, this chapter introduces the motivations and characteristics of violent extremists who might engage in kinetic cyber, and ends off by proposing some recommended directions to counter this threat.

Cyber Security in Health

2020 ◽  
pp. 313-329 ◽  
Author(s):  
Bruno J. Santos ◽  
Rachel P. Tabacow ◽  
Marcelo Barboza ◽  
Tarcisio F. Leão ◽  
Eduardo G. P. Bock
Keyword(s):  
Medical Devices ◽  
Virtual Worlds ◽  
Cyber Security ◽  
New Technology ◽  
Physical World ◽  
Artificial Organs ◽  
Healthcare Organizations ◽  
Digital Era ◽  
Iot Devices ◽  
Security Incidents

Cyber security in Healthcare is a growing concern. Since it has been a proliferation of IoT devices, data breaches from the healthcare industry are increasing the concern about how cyber security can protect data from connected medical devices. Recent years have seen numerous hacking and IT security incidents. Many healthcare organizations are facing problems to defend their networks from cybercriminals. In the current digital era, the physical world has a cyber-representation. Both the real and virtual worlds are connected in areas, such as informatics and manufacturing. Health 4.0 (H4.0) refers to a group of initiatives aiming to improve medical care for patients, hospitals, researchers, and medical device suppliers. Increasing collaboration in terms of medical equipment, artificial organs, and biosensors is a way to facilitate H4.0. As a result, cyber security budgets have increased, new technology has been purchased, and healthcare organizations are improving at blocking attacks and keeping their networks secure.

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