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AMCB: Authenticated Medical Cyber-Physical Blockchain model

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Abstract

At a time when technology is spreading rapidly and widely, technology has become a necessity in daily life and practical life, and this led to the emergence of many cyber-physical systems (CPS), among which the medical cyber-physical systems (MCPS) have emerged, which is one application of CPS that is particularly concerned with patients and health care providers. These systems generate a large amount of data that may be difficult to process and store, in addition to unauthorized access to these systems, which affects their protection. This paper presents a proposed Authenticated Medical Cyber-Physical Blockchain (AMCB) model in Medical Cyber-Physical Systems (MCPS) using Blockchain technology, machine learning, and physically unclonable function (PUF) to enhance the authentication process by controlling the access to electronic health records (EHR) that stored on the cloud server and physical authentication. In addition, it analyzes the data generated from the authorized devices to ensure that the devices do not contain malicious. This paper presents an experiment based on the medical Internet of Things (IoMT) dataset using K-nearest neighbors (k-NN), Random Forest (RF), Naive Bayes and Supporting Vector Machine (SVM) for malicious detection to test the proposed model’s accuracy. The Random Forest (RF) classifier gave more accurate results based on the preliminary results with a slight difference from k-NN.

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References

  1. Qiu H et al (2020) Secure health data sharing for medical cyber-physical systems for the healthcare 4.0. IEEE J Biomed Health Inf 24(9):2499–2505

    Article  Google Scholar 

  2. Chen F et al (2021) Medical cyber-physical systems: a solution to smart health and the state of the art. IEEE Trans Comput Soc Syst

  3. Cheng X et al (2020) Design of a secure medical data sharing scheme based on blockchain. J Med Syst 44(2):1–11

    Article  Google Scholar 

  4. Al-Ghuraybi HA, AlZain MA, Soh B (2023) Ensuring authentication in Medical Cyber-Physical Systems: a comprehensive literature review of blockchain technology integration with machine learning. Multimed Tools Appl

  5. Tyagi AK et al (2021) AARIN: affordable, accurate, reliable and innovative mechanism to protect a medical cyber-physical system using blockchain technology. Int J Intell Networks 2:175–183

    Article  Google Scholar 

  6. Gatouillat A et al (2018) Internet of medical things: a review of recent contributions dealing with cyber-physical systems in medicine. IEEE Internet Things J 5(5):3810–3822

    Article  Google Scholar 

  7. Xi P et al (2022) A review of Blockchain-based secure sharing of Healthcare Data. Appl Sci 12(15):7912

    Article  Google Scholar 

  8. Zhao W et al (2020) Blockchain-enabled cyber–physical systems: a review. IEEE Internet Things J 8(6):4023–4034

    Article  Google Scholar 

  9. Gawanmeh A, Alomari A (2018) Taxonomy analysis of security aspects in cyber physical systems applications. In: IEEE International Conference on Communications Workshops (ICC Workshops), IEEE

  10. Yu C, Jing S, Li X (2012) An architecture of cyber physical system based on service. In: International Conference on Computer Science and Service System, IEEE

  11. Rathore H, Mohamed A, Guizani M (2020) A survey of blockchain enabled cyber-physical systems. Sensors 20(1):282

    Article  Google Scholar 

  12. Salah K et al (2019) Blockchain for AI: review and open research challenges. IEEE Access 7:10127–10149

    Article  Google Scholar 

  13. Shafay M et al (2022) Blockchain for deep learning: review and open challenges. Cluster Comput, pp 1–25

  14. Wang W et al (2021) Blockchain and PUF-based lightweight authentication protocol for wireless medical sensor networks. IEEE Internet Things J 9(11):8883–8891

    Article  Google Scholar 

  15. Al-Ghuraybi HA, AlZain MA, Soh B (2023) Exploring the integration of blockchain technology, physical unclonable function, and machine learning for authentication in cyber-physical systems. Multimed Tools Appl

  16. Shishvan OR, Zois D-S, Soyata T (2018) Machine intelligence in healthcare and medical cyber physical systems: a survey. IEEE Access 6:46419–46494

    Article  Google Scholar 

  17. Tanwar S et al (2019) Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access 8:474–488

    Article  Google Scholar 

  18. Kumari A et al (2018) Fog computing for Healthcare 4.0 environment: opportunities and challenges. Comput Electr Eng 72:1–13

    Article  Google Scholar 

  19. Peralta G et al (2017) Fog computing based efficient IoT scheme for the Industry 4.0. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), IEEE

  20. Baniata H, Kertesz A (2020) A survey on blockchain-fog integration approaches. IEEE Access 8:102657–102668

    Article  Google Scholar 

  21. Chen F et al (2021) Blockchain-based efficient device authentication protocol for medical cyber-physical systems. Security and Communication Networks, 2021

  22. Chen F et al (2021) Data access control based on blockchain in medical cyber physical systems. Security and Communication Networks, 2021

  23. Sultana T et al (2020) Data sharing system integrating access control mechanism using blockchain-based smart contracts for IoT devices. Appl Sci 10(2):488

    Article  MathSciNet  Google Scholar 

  24. Khan MA et al (2020) A machine learning approach for blockchain-based smart home networks security. IEEE Network 35(3):223–229

    Article  MathSciNet  Google Scholar 

  25. Ghayvat H et al (2021) CP-BDHCA: Blockchain-based confidentiality-privacy preserving Big Data scheme for healthcare clouds and applications. IEEE J Biomed Health Inf 26(5):1937–1948

    Article  Google Scholar 

  26. Almaiah MA et al (2022) A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS. Sensors 22(4):1448

    Article  Google Scholar 

  27. Schneble W, Thamilarasu G (2019) Attack detection using federated learning in medical cyber-physical systems. In: 28th International Conference on Computer Communications and Networks (ICCCN)

  28. Shahbazi Z, Byun Y-C (2021) Integration of Blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors 21(4):1467

    Article  Google Scholar 

  29. Rathore S, Park JH (2020) A blockchain-based deep learning approach for cyber security in next generation industrial cyber-physical systems. IEEE Trans Industr Inf 17(8):5522–5532

    Article  Google Scholar 

  30. Kumar R et al (2019) A multimodal malware detection technique for Android IoT devices using various features. IEEE Access 7:64411–64430

    Article  Google Scholar 

  31. Hussain F et al (2021) A framework for malicious traffic detection in IoT healthcare environment. Sensors 21(9):3025

    Article  Google Scholar 

  32. Mohanty SP et al (2020) PUFchain: a hardware-assisted blockchain for sustainable simultaneous device and data security in the internet of everything (IoE). IEEE Consum Electron Mag 9(2):8–16

    Article  Google Scholar 

  33. Bathalapalli VK et al (2022) PUFchain 2.0: hardware-assisted robust blockchain for sustainable simultaneous device and data security in smart healthcare. SN Comput Sci 3(5):1–19

    Article  Google Scholar 

  34. Pereira A, Thomas C (2020) Challenges of machine learning applied to safety-critical cyber-physical systems. Mach Learn Knowl Extr 2(4):579–602

    Article  Google Scholar 

  35. Kim S, Park K-J (2021) A survey on machine-learning based security design for cyber-physical systems. Appl Sci 11(12):5458

    Article  Google Scholar 

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Acknowledgements

The researchers would like to acknowledge Deanship of Scientific Research, Taif University for funding this work.

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Correspondence to Hind A. Al-Ghuraybi.

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Al-Ghuraybi, H.A., AlZain, M.A. & Soh, B. AMCB: Authenticated Medical Cyber-Physical Blockchain model. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18904-7

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  • DOI: https://doi.org/10.1007/s11042-024-18904-7

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