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Securing the Hyperconnected Healthcare Ecosystem

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AI and IoT for Sustainable Development in Emerging Countries

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 105))

Abstract

Digitalization across the healthcare industry is improving the quality and personalization of the services provided to patients. Currently, we are observing great advances in aspects such as doctor-patient coordination, real-time health monitoring, communication between different specialist, or administrative tasks automation, among others. However, this hyperconnectivity also brings important challenges related to the cyber-security of the critical eHealth infrastructures. Privacy concerns, intrusion detection, secure data exchange, etc. are crucial factors that should be addressed when designing digital healthcare platforms. In this chapter, a wide overview of the cyber-security landscape in the eHealth sector is presented, emphasizing the biggest challenges to be faced during the next years. Then, as the main contribution of this work, it is proposed a holistic cyber-security platform that tackles privacy and security risks in an automated fashion to foster the development of innovative applications within the healthcare ecosystem.

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Notes

  1. 1.

    https://digital-strategy.ec.europa.eu/en/policies/nis-directive.

  2. 2.

    https://ec.europa.eu/info/law/law-topic/data-protection_en.

  3. 3.

    https://digital-strategy.ec.europa.eu/en/policies/nis-cooperation-group.

  4. 4.

    https://www.enisa.europa.eu/publications/eisas-deployment-feasibility-study/at_download/fullReport.

  5. 5.

    https://github.com/casework/case.

  6. 6.

    https://stixproject.github.io/supporters/.

  7. 7.

    https://www.misp-project.org/.

  8. 8.

    https://github.com/certtools/intelmq.

  9. 9.

    https://github.com/casework/case.

  10. 10.

    https://digital-strategy.ec.europa.eu/en/policies/eidas-regulation.

References

  1. Chehri A, Fofana I, Yang X (2021) Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence. Sustainability 13(6):3196

    Google Scholar 

  2. Li W, Chai Y, Khan F, Jan SRU, Verma S, Menon VG, Kavita, Li X (2021) A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile Network Appl 26(1):234–252

    Google Scholar 

  3. Muhammad G, Alshehri F, Karray F, El Saddik A, Alsulaiman M, Falk TH (2021) A comprehensive survey on multimodal medical signals fusion for smart healthcare systems. Inf Fusion (in press)

    Google Scholar 

  4. Coventry L, Branley D (2018) Cybersecurity in healthcare: a narrative review of trends, threats and ways forward. Maturitas 113:48–52

    Google Scholar 

  5. Tully J, Selzer J, Phillips JP, O’Connor P, Dameff C (2020) Healthcare challenges in the era of cybersecurity. Health Secur 18(3):228–231

    Google Scholar 

  6. Zhang J, Zhao Y, Jie W, Chen B (2020) LVPDA: a lightweight and verifiable privacy-preserving data aggregation scheme for edge-enabled IoT. IEEE Internet Things J 7(5):4016–4027

    Google Scholar 

  7. Sanchez-Iborra R, Skarmeta AF (2020) TinyML-enabled Frugal smart objects: challenges and opportunities. IEEE Circ Syst Mag 20(3):4–18

    Google Scholar 

  8. Benhamouda F, Joye M, Libert B (2016) A new framework for privacy-preserving aggregation of time-series data. ACM Trans Inf Syst Secur 18(3):1–21

    Google Scholar 

  9. Derler D, Ramacher S, Slamanig D (2017) Homomorphic proxy re-authenticators and applications to verifiable multi-user data aggregation. In: Kiayias A (ed) Financial cryptography and data security, pp 124–142

    Google Scholar 

  10. Walfish M, Blumberg AJ (2015) Verifying computations without reexecuting them. Commun ACM 58(2):74–84

    Google Scholar 

  11. Yu X, Yan Z, Vasilakos AV (2017) A survey of verifiable computation. Mobile Networks Appl 22(3):438–453

    Google Scholar 

  12. Thakkar A, Lohiya R (2021) A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artif Intell Rev (in press)

    Google Scholar 

  13. Tan S, Guerrero JM, Xie P, Han R, Vasquez JC (2020) Brief survey on attack detection methods for cyber-physical systems. IEEE Syst J 14(4):5329–5339

    Google Scholar 

  14. Daniya T, Suresh Kumar K, Santhosh Kumar B, Kolli CS (2021) A survey on anomaly based intrusion detection system. Mater Today Proc (in press, Apr 2021)

    Google Scholar 

  15. Sun J, Wang X, Xiong N, Shao J (2018) Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access 6:33353–33361

    Google Scholar 

  16. Lee J-H, Kim YS, Kim JH, Kim IK (2017) Toward the SIEM architecture for cloud-based security services. In: IEEE Conference on communications and network security (CNS), Oct 2017. IEEE, pp 398–399

    Google Scholar 

  17. Rashid F, Miri A (2021) User and event behavior analytics on differentially private data for anomaly detection. In: 7th IEEE International conference on big data security on cloud (BigDataSecurity), IEEE International conference on high performance and smart computing (HPSC) and IEEE International conference on intelligent data and security (IDS), May 2021. IEEE, pp 81–86

    Google Scholar 

  18. Moukafih N, Orhanou G, El Hajji S (2020) Neural network-based voting system with high capacity and low computation for intrusion detection in SIEM/IDS systems. Secur Commun Networks 2020:1–15

    Google Scholar 

  19. Jin H, Luo Y, Li P, Mathew J (2019) A review of secure and privacy-preserving medical data sharing. IEEE Access 7:61656–61669

    Google Scholar 

  20. Zibak A, Simpson A (2019) Cyber threat information sharing: perceived benefits and barriers. In: Proceedings of the 14th international conference on availability, reliability and security, Aug 2019. ACM, New York, NY, USA, pp 1–9

    Google Scholar 

  21. Du L, Fan Y, Zhang L, Wang L, Sun T (2020) A summary of the development of cyber security threat intelligence sharing. Int J Digit Crime Forensics 12(4):54–67

    Google Scholar 

  22. Bass T (2000) Intrusion detection systems and multisensor data fusion. Commun ACM 43(4):99–105

    Google Scholar 

  23. Rajamäki J, Katos V (2020) Information sharing models for early warning systems of cybersecurity intelligence. Inf Secur Int J 46(2):198–214

    Google Scholar 

  24. Lorenzen C, Agrawal R, King J (2018) Determining viability of deep learning on cybersecurity log analytics. In: IEEE International conference on big data (big data), Dec 2018. IEEE, pp 4806–4811

    Google Scholar 

  25. Hallaq B, Nicholson A, Smith R, Maglaras L, Cook A, Janicke H, Jones K (2021) A novel hybrid cyber range for security exercises on cyber-physical systems. Int J Smart Secur Technol 8(1):16–34

    Google Scholar 

  26. Vykopal J, Vizvary M, Oslejsek R, Celeda P, Tovarnak D (2017) Lessons learned from complex hands-on defence exercises in a cyber range. In: IEEE Frontiers in education conference (FIE), Oct 2017. IEEE, pp 1–8

    Google Scholar 

  27. Martin G, Martin P, Hankin C, Darzi A, Kinross J (2017) Cybersecurity and healthcare: how safe are we? BMJ 358(j3179)

    Google Scholar 

  28. Karjalainen M, Kokkonen T (2020) Comprehensive cyber arena; The next generation cyber range. In: IEEE European symposium on security and privacy workshops (EuroS&PW), Sept 2020. IEEE, pp 11–16

    Google Scholar 

  29. Drǎgulinescu AMC, Manea AF, Fratu O, Drǎgulinescu A (2020) LoRa-Based medical IoT system architecture and testbed. Wirel Personal Commun

    Google Scholar 

  30. Shamayleh A, Awad M, Farhat J (2020) IoT Based predictive maintenance management of medical equipment. J Med Syst 44(4):72

    Google Scholar 

  31. Matheu SN, Hernández-Ramos JL, Skarmeta AF, Baldini G (2021) A survey of cybersecurity certification for the internet of things. ACM Comput Surv 53(6):1–36

    Google Scholar 

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Acknowledgements

This work has been supported by the European Commission, under the project CyberSec4Europe (Grant No. 830929) and by the Spanish Ministry of Science, Innovation and Universities, under the project ONOFRE 3 (Grant No. PID2020-112675RB-C44).

Author information

Authors and Affiliations

  1. University Center of Defense, General Air Force Academy, San Javier, Spain

    Ramon Sanchez-Iborra

  2. Computer Science Faculty, University of Murcia, Murcia, Spain

    Antonio Skarmeta

Authors
  1. Ramon Sanchez-Iborra
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  2. Antonio Skarmeta
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Corresponding author

Correspondence to Ramon Sanchez-Iborra .

Editor information

Editors and Affiliations

  1. LIM, Hassan II University, Mohammedia, Morocco

    Zakaria Boulouard

  2. Moulay Ismail University, Meknes, Morocco

    Mariya Ouaissa

  3. Moulay Ismail University, Meknes, Morocco

    Mariyam Ouaissa

  4. Sidi Mohamed Ben Abdellah University, Fes, Morocco

    Sarah El Himer

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Sanchez-Iborra, R., Skarmeta, A. (2022). Securing the Hyperconnected Healthcare Ecosystem. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (eds) AI and IoT for Sustainable Development in Emerging Countries. Lecture Notes on Data Engineering and Communications Technologies, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-90618-4_22

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