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Multi-layer security scheme for implantable medical devices

  • Heena Rathore
  • Chenglong Fu
  • Amr Mohamed
  • Abdulla Al-Ali
  • Xiaojiang Du
  • Mohsen Guizani
  • Zhengtao Yu
Cognitive Computing for Intelligent Application and Service

Abstract

Internet 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 patient’s 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.

Keywords

Multi-layer security Deep learning Medical devices Authentication 

Notes

Acknowledgements

This publication was made possible by NPRP Grant #8-408-2-172 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Authors would like to thank Dr Abhay Samant for his valuable feedback and corrections in the paper.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Hiller MeasuremntsAustinUSA
  2. 2.Temple UniversityPhiladelphiaUSA
  3. 3.Qatar UniversityDohaQatar
  4. 4.University of IdahoMoscowUSA
  5. 5.Kunming University of Science and TechnologyXishan, KunmingChina

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