scholarly journals HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems

Author(s):  
AKM Iqtidar Newaz ◽  
Amit Kumar Sikder ◽  
Mohammad Ashiqur Rahman ◽  
A. Selcuk Uluagac
Author(s):  
Akm Iqtidar Newaz ◽  
Nur Imtiazul Haque ◽  
Amit Kumar Sikder ◽  
Mohammad Ashiqur Rahman ◽  
A. Selcuk Uluagac

Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.


2020 ◽  
Vol 6 (3) ◽  
pp. 599-603
Author(s):  
Michael Friebe

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.


2021 ◽  
Author(s):  
Michael Enbibel

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.


The advancement of information and communications technology has changed an IoMT-enabled healthcare system. The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) that focuses on smart healthcare (medical) device connectivity. While the Internet of Medical Things (IoMT) communication environment facilitates and supports our daily health activities, it also has drawbacks such as password guessing, replay, impersonation, remote hijacking, privileged insider, denial of service (DoS), and man-in-the-middle attacks, as well as malware attacks. Malware botnets cause assaults on the system's data and other resources, compromising its authenticity, availability, confidentiality and, integrity. In the event of such an attack, crucial IoMT communication data may be exposed, altered, or even unavailable to authorised users. As a result, malware protection for the IoMT environment becomes critical. In this paper, we provide several forms of malware attacks and their consequences. We also go through security, privacy, and different IoMT malware detection schemes


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