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AuthorRathore H.
AuthorFu C.
AuthorMohamed A.
AuthorAl-Ali A.
AuthorDu X.
AuthorGuizani M.
AuthorYu Z.
Available date2022-04-21T08:58:26Z
Publication Date2020
Publication NameNeural Computing and Applications
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/s00521-018-3819-0
URIhttp://hdl.handle.net/10576/30098
AbstractInternet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patients health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients. 2018, The Natural Computing Applications Forum.
SponsorQatar Foundation;Qatar National Research Fund
Languageen
PublisherSpringer
SubjectAnomaly detection
Authentication
Health care
Personnel testing
Remote patient monitoring
Implantable medical devices
Legendre approximation
Machine learning approaches
Medical Devices
Multi layer perceptron
Wireless communication channels
Wireless communications
Wireless medical devices
Deep learning
TitleMulti-layer security scheme for implantable medical devices
TypeArticle
Pagination4347-4360
Issue Number9
Volume Number32


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