Multi-layer security scheme for implantable medical devices

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


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.


Multi-layer security Deep learning Medical devices Authentication 



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.


  1. 1.
    Frost and Sullivan, Internet of Medical Things, Forecast to 2021, market report. Accessed 19 June 2018
  2. 2.
  3. 3.
    Halperin D et al (2008) Pacemakers and implantable cardiac defibrillators: software radio attacks and zero-power defenses. In: IEEE symposium on security and privacy, SP, pp 129–142Google Scholar
  4. 4.
    Rathore H (2017) A review of security challenges, attacks and resolutions for wireless medical devices. Wirel Commun Mob Comput Conf (IWCMC) 13:1495–1501Google Scholar
  5. 5.
    Zheng G et al (2014) An ECG-based secret data sharing scheme supporting emergency treatment of implantable medical devices. In: Proceedings of international symposium on wireless personal multimedia communications (WPMC), pp 624–628Google Scholar
  6. 6.
    Rathore H et al (2018) DTW based authentication for wireless medical device security. In: IEEE 14th international wireless communications and mobile computing conference (IWCMC), pp. 476–481Google Scholar
  7. 7.
    Hei X, Du X (2011) Biometric-based two-level secure access control for implantable medical devices during emergency. In: Proceedings of IEEE INFOCOMGoogle Scholar
  8. 8.
    Rostami M, Juels A, Koushanfar F (2013) Heart-to-heart (H2H): authentication for implanted medical devices. In: Proceedings of ACM SIGSAC conference on computer and communications security, pp 1099–1112Google Scholar
  9. 9.
    Halperin D, Heydt-Benjamin TS, Ransford B, Clark SS, Defend B, Morgan W, Fu K, Kohno T, Maisel WH (2008)Pacemakers and implantable cardiac defibrillators: software radio attacks and zero-power defenses. In: IEEE symposium on security and privacy, pp 129–142Google Scholar
  10. 10.
    Rasmussen KB, Castelluccia C, Heydt-Benjamin TS, Capkun S (2009) Proximity-based access control for implantable medical devices. In: Proceedings of 16th ACM conference on computer and communications security, pp 410–419Google Scholar
  11. 11.
    Kim Yu BJ, Kim H (2012) In-vivo NFC: remote monitoring of implanted medical devices with improved privacy. In: Proceedings of 10th ACM conference on embedded network sensor systems, pp 327–328Google Scholar
  12. 12.
    Singh K, Muthukkumarasamy V (2007) Authenticated key establishment protocols for a home health care system. In: Proceedings of 3rd international conference on intelligent sensors, sensor networks and information, pp 353–358Google Scholar
  13. 13.
    Zheng G, Fang G, Shankaran R, Orgun MA, Dutkiewicz E (2014) An ECG-based secret data sharing scheme supporting emergency treatment of implantable medical devices. In: International symposium on IEEE wireless personal multimedia communications (WPMC), pp 624- 628. IEEEGoogle Scholar
  14. 14.
    Rieback MR, Crispo B, Tanenbaum AS (2005) RFID guardian: a battery-powered mobile device for RFID privacy management. In: Proceedings of Australasian conference on information security and privacy, pp 184–194CrossRefGoogle Scholar
  15. 15.
    Rathore H, Al-Ali A, Mohamed A, Du X, Guizani M. (2017) DLRT: deep learning approach for reliable diabetic treatment. In: Proceedings of IEEE GlobecomGoogle Scholar
  16. 16.
    Zhang M, Raghunathan A, Jha NK (2013) MedMon: securing medical devices through wireless monitoring and anomaly detection. IEEE Trans Biomed Circuits Syst 7(6):871–881CrossRefGoogle Scholar
  17. 17.
    Xu F, Qin Z, Tan CC, Wang B, Li Q (2011) IMDGuard: securing implantable medical devices with the external wearable guardian. In: Proceedings of IEEE INFOCOM, pp 1862–1870Google Scholar
  18. 18.
    Denning T, Fu K, Kohno T (2008) Absence makes the heart grow fonder: new directions for implantable medical device security. In: Proceedings of HotSecGoogle Scholar
  19. 19.
    Meste O et al (2005) Time-varying analysis methods and models for the respiratory and cardiac system coupling in graded exercise. IEEE Trans Biomed Eng 52(11):1921–1930CrossRefGoogle Scholar
  20. 20.
    Khalil I, Sufi F. (2008) Legendre polynomials based biometric authentication using QRS complex of ECG. In: Proceedings of international conference on intelligent sensors, sensor networks and information processing ISSNIP, pp 297–302Google Scholar
  21. 21.
    Friesen GM (1990) A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng 37(1):85–98CrossRefGoogle Scholar
  22. 22.
    Philips W, De Jonghe G (1992) Data compression of ECG’s by high-degree polynomial approximation. IEEE Trans Biomed Eng 39(4):330–337CrossRefGoogle Scholar
  23. 23.
    Rappaport TS (1996) Wireless communications: principles and practice, vol 2. Prentice Hall PTR, New JerseyzbMATHGoogle Scholar
  24. 24.
    Sufi F, Khalil I, Habib I (2010) Polynomial distance measurement for ECG based biometric authentication. Sec Commun Netw 3(4):303–319CrossRefGoogle Scholar
  25. 25.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  26. 26.
    Rathore H, Badarla V, Jha S, Gupta A (2014) Novel approach for security in wireless sensor network using bio-inspirations. In: Proceedings of sixth international conference on communication systems and networks (COMSNETS), pp 1–8. IEEEGoogle Scholar
  27. 27.
    Physionet. Accessed 15 July 2017
  28. 28.
    Chollet F (2015) Keras: Theano-based deep learning library. Code: Documentation:
  29. 29.
    The Theano Development (2016) A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688
  30. 30.
    Xu F et al (2011) IMDGuard: securing implantable medical devices with the external wearable guardian. In: Proceedings of IEEE INFOCOM, pp 1862–1870Google Scholar
  31. 31.
    Chi H et al (2018) e-SAFE: secure, efficient and forensics-enabled access to implantable medical devices. arXiv preprint arXiv:1804.02447
  32. 32.
    Weisman S. Web-connected medical devices are great. Unless. USA Today. Accessed 19 July 2018Google Scholar
  33. 33.
    Hei X, Du X, Wu J, Hu F (2010) Defending resource depletion attacks on implantable medical devices. In: Proceedings of IEEE GLOBECOMGoogle Scholar
  34. 34.
    Gollakota S, Hassanieh H, Ransford B, Katabi D, Fu K (2011) They can hear your heartbeats: non-invasive security for implantable medical devices. ACM SIGCOMM Comput Commun Rev 41(4):2–13CrossRefGoogle Scholar
  35. 35.
    Rathore H et al (2018) Multi-layer perceptron model on chip for secure diabetic treatment. IEEE AccessGoogle Scholar
  36. 36.
    Shen TW, Tompkins WJ, Hu YH (2002) One-lead ECG for identity verification. In: Proceedings of engineering in medicine and biology, 2002. 24th annual conference and the annual fall meeting of the biomedical engineering society embs/bmes conference. Proceedings of the second joint, vol 1, pp 62–63Google Scholar
  37. 37.
    Tuzcu V, Nas S (2005) Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances. In: 2005 IEEE international conference on systems, man and cybernetics, vol 1, pp 182–186. IEEEGoogle Scholar
  38. 38.
    Raspberry Pi Dramble. Accessed 8 June 2018
  39. 39.
    Herrero-Collantes M, Garcia-Escartin JC (2017) Quantum random number generators. Rev Mod Phys 89(1):015004MathSciNetCrossRefGoogle Scholar
  40. 40.
    Lee DU (2008) Hardware implementation trade-offs of polynomial approximations and interpolations. IEEE Trans Comput 57(5):686–701MathSciNetCrossRefGoogle Scholar

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

Personalised recommendations