Abstract
The advent of the internet of things (IoT) in the global communication network has made everything interconnected and accessible. Therefore, the fields of medicine and diagnosis have emerging trend of using heterogeneous Internet of Medical Things (IoMT). The IoMT makes use of wearable health devices to transfer a huge amount of sensitive medical data to primary servers for diagnosis via a Wireless Medical Sensor Network (WMSN). Although it brings much convenience to patients as well as medical professionals, there are risks of security and privacy breaches. Recently, Wang et al. proposed “Blockchain and PUF-based Authentication Protocol for Wireless Medical Sensor Networks” (DOI 10.1109/JIOT.2021.3117762) for WMSN. Although their protocol deploys security benefits of both the blockchain and PUF technology but cryptanalysis of this protocol shows that the impersonation of the entities involved in the protocol makes it highly vulnerable to eavesdropping, incorrect notion of user anonymity and masquerading attacks. This study pinpoints several security breaches of the said protocol and proposes an enhanced protocol to resolve these security flaws in an invulnerable way. We show that the proposed protocol is safe against various attacks like impersonation, man-in-the-middle, user anonymity and system key leakage using Automated Validation of Internet Security Protocols and Applications tools and Random Oracle Model. We offer pragmatic security analysis and proofs to show that the suggested protocol meets the intended security objectives. Our protocol surpasses four other competitive protocols in terms of computing, communication, and storage costs, according to a thorough performance comparison.







Data Availability
There is no data or any other material associated with this manuscript.
Code Availability
Not Applicable.
References
Khairuddin, A., Azir, K.F.K., & Kan, P.E. (2017). Limitations and future of electrocardiography devices: A review and the perspective from the internet of things, in 2017 international conference on research and innovation in information systems (ICRIIS).IEEE, pp. 1–7.
Deshkar, S., Thanseeh, R., & Menon, V. G. (2017). A review on iot based m-health systems for diabetes. International Journal of Computer Science and Telecommunications, 8(1), 13–18.
Vergara, P. M., de la Cal, E., Villar, J. R., González, V. M., & Sedano, J. (2017). An iot platform for epilepsy monitoring and supervising. Journal of Sensors, 2017(1), 6043069.
Msayib, Y., Gaydecki, P., Callaghan, M., Dale, N., & Ismail, S. (2017). An intelligent remote monitoring system for total knee arthroplasty patients. Journal of medical systems, 41(6), 1–6.
Kitsiou, S., Thomas, M., Marai, G.E., Maglaveras, N., Kondos, G., Arena, R., & Gerber, B. (2017) Development of an innovative mhealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients, in 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).IEEE, pp. 133–136.
Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017). Advanced internet of things for personalised healthcare systems: A survey. Pervasive and Mobile Computing, 41, 132–149.
Al Mamun, K. A., Alhussein, M., Sailunaz, K., & Islam, M. S. (2017). Cloud based framework for parkinson’s disease diagnosis and monitoring system for remote healthcare applications. Future Generation Computer Systems, 66, 36–47.
Crema, C., Depari, A., Flammini, A., Sisinni, E., Vezzoli, A., & Bellagente, P. (2017). Virtual respiratory rate sensors: An example of a smartphone-based integrated and multiparametric mhealth gateway. IEEE Transactions on Instrumentation and Measurement, 66(9), 2456–2463.
Silsupadol, P., Teja, K., & Lugade, V. (2017). Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket. Gait & Posture, 58, 516–522.
Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2017). Can smartphone mental health interventions reduce symptoms of anxiety? a meta-analysis of randomized controlled trials. Journal of Affective Disorders, 218, 15–22.
Firouzi, F., Rahmani, A.M., Mankodiya, K., Badaroglu, M., Merrett, G.V., Wong, P., & Farahani, B. (2018). Internet-of-things and big data for smarter healthcare: From device to architecture, applications and analytics, pp. 583–586.
Baranchuk, A., Refaat, M. M., Patton, K. K., Chung, M. K., Krishnan, K., Kutyifa, V., Upadhyay, G., Fisher, J. D., & Lakkireddy, D. R. (2018). A. C. of cardiology’s electrophysiology section leadership, Cybersecurity for cardiac implantable electronic devices: What should you know? Journal of the American College of Cardiology, 71(11), 1284–1288.
Wu, F., Li, X., Sangaiah, A. K., Xu, L., Kumari, S., Wu, L., & Shen, J. (2018). A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks. Future Generation Computer Systems, 82, 727–737.
Fotouhi, M., Bayat, M., Das, A. K., Far, H. A. N., Pournaghi, S. M., & Doostari, M.-A. (2020). A lightweight and secure two-factor authentication scheme for wireless body area networks in health-care iot. Computer Networks, 177, 107333.
Amin, R., Islam, S. H., Biswas, G., Khan, M. K., & Kumar, N. (2018). A robust and anonymous patient monitoring system using wireless medical sensor networks. Future Generation Computer Systems, 80, 483–495.
Li, X., Wu, F., Khan, M. K., Xu, L., Shen, J., & Jo, M. (2018). A secure chaotic map-based remote authentication scheme for telecare medicine information systems. Future Generation Computer Systems, 84, 149–159.
Wang, W., Qiu, C., Yin, Z., Srivastava, G., Gadekallu, T. R., Alsolami, F., & Su, C. (2021). Blockchain and puf-based lightweight authentication protocol for wireless medical sensor networks. IEEE Internet of Things Journal, 9(11), 8883.
Rodrigues, J. J., Segundo, D. B. D. R., Junqueira, H. A., Sabino, M. H., Prince, R. M., Al-Muhtadi, J., & De Albuquerque, V. H. C. (2018). Enabling technologies for the internet of health things. Ieee Access, 6, 13129–13141.
Sureshkumar, V., Amin, R., Vijaykumar, V., & Sekar, S. R. (2019). Robust secure communication protocol for smart healthcare system with fpga implementation. Future Generation Computer Systems, 100, 938–951.
Tai, W.-L., Chang, Y.-F., & Lo, Y.-L. (2019). An anonymity, availability and security-ensured authentication model of the iot control system for reliable and anonymous ehealth services. Journal of Medical and Biological Engineering, 39(4), 443–455.
Gope, P., Millwood, O., & Sikdar, B. (2021). A scalable protocol level approach to prevent machine learning attacks on physically unclonable function based authentication mechanisms for internet of medical things. IEEE Transactions on Industrial Informatics, 18(3), 1971–1980.
Liu, C.-H., & Chung, Y.-F. (2017). Secure user authentication scheme for wireless healthcare sensor networks. Computers & Electrical Engineering, 59, 250–261.
Jiang, Q., Ma, J., Yang, C., Ma, X., Shen, J., & Chaudhry, S. A. (2017). Efficient end-to-end authentication protocol for wearable health monitoring systems. Computers & Electrical Engineering, 63, 182–195.
Mo, J., Hu, Z., & Lin, Y. (2020). Cryptanalysis and security improvement of two authentication schemes for healthcare systems using wireless medical sensor networks. Security and Communication Networks, 2020(1), 5047379.
Azrour, M., Mabrouki, J., Guezzaz, A., & Farhaoui, Y. (2021). New enhanced authentication protocol for internet of things. Big Data Mining and Analytics, 4(1), 1–9.
Vinoth, R., Deborah, L. J., Vijayakumar, P., & Kumar, N. (2020). Secure multifactor authenticated key agreement scheme for industrial IoT. IEEE Internet of Things Journal, 8(5), 3801–3811.
Xue, L., Huang, Q., Zhang, S., Huang, H., & Wang, W. (2021). A lightweight three-factor authentication and key agreement scheme for multigateway wsns in iot. Security and Communication Networks, 2021, 1–15.
Challa, S., Das, A. K., Odelu, V., Kumar, N., Kumari, S., Khan, M. K., & Vasilakos, A. V. (2018). An efficient ECC-based provably secure three-factor user authentication and key agreement protocol for wireless healthcare sensor networks. Computers & Electrical Engineering, 69, 534–554.
Ali, R., Pal, A. K., Kumari, S., Sangaiah, A. K., Li, X., & Wu, F. (2018). An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring. Journal of Ambient Intelligence and Humanized Computing, 12, 1–22.
Li, X., Ibrahim, M. H., Kumari, S., Sangaiah, A. K., Gupta, V., & Choo, K.-K.R. (2017). Anonymous mutual authentication and key agreement scheme for wearable sensors in wireless body area networks. Computer Networks, 129, 429–443.
Masud, M., Gaba, G. S., Choudhary, K., Hossain, M. S., Alhamid, M. F., & Muhammad, G. (2021). Lightweight and anonymity-preserving user authentication scheme for IOT-based healthcare. IEEE Internet of Things Journal, 9(4), 2649.
Kaur, D., Saini, K. K., & Kumar, D. (2022). Cryptanalysis and enhancement of an authentication protocol for secure multimedia communications in IoT-enabled wireless sensor networks. Multimedia Tools and Applications, 81(27), 39 367-39 385.
Saini, K. K., Kaur, D., Kumar, D., & Kumar, B. (2024). An efficient three-factor authentication protocol for wireless healthcare sensor networks. Multimedia Tools and Applications, 24, 1–23.
Shao, X., Guo, Y., & Guo, Y. (2022). A PUF-based anonymous authentication protocol for wireless medical sensor networks. Wireless Networks, 28(8), 3753–3770.
Shamshad, S., Mahmood, K., Kumari, S., Chen, C.-M., et al. (2020). A secure blockchain-based e-health records storage and sharing scheme. Journal of Information Security and Applications, 55, 102590.
Xiao, L., Han, D., Meng, X., Liang, W., & Li, K.-C. (2020). A secure framework for data sharing in private blockchain-based wbans. IEEE Access, 8, 153 956-153 968.
Khujamatov, K., Reypnazarov, E., Akhmedov, N., & Khasanov, D. (2020). Blockchain for 5g healthcare architecture, in 2020 international conference on information science and communications technologies (ICISCT).IEEE, pp. 1–5.
Hong, Y., Yang, L., Liang, W., & Xie, A. (2023). Secure access control for electronic health records in blockchain-enabled consumer internet of medical things. IEEE Transactions on Consumer Electronics, 25, 23.
Kearney, J. J., & Perez-Delgado, C. A. (2021). Vulnerability of blockchain technologies to quantum attacks. Array, 10, 100065.
Cui, W., Dou, T., & Yan, S. (2020). Threats and opportunities: Blockchain meets quantum computation,” in 2020 39th Chinese control conference (CCC).IEEE, pp. 5822–5824.
Arpaia, P., Bonavolontà, F., Cioffi, A., & Moccaldi, N. (2021). Power measurement-based vulnerability assessment of IOT medical devices at varying countermeasures for cybersecurity. IEEE Transactions on Instrumentation and Measurement., 70, 1–9.
Chang, C.-C., & Le, H.-D. (2015). A provably secure, efficient, and flexible authentication scheme for ad hoc wireless sensor networks. IEEE Transactions on Wireless Communications, 15(1), 357–366. https://doi.org/10.1109/TWC.2015.2473165
Akram, M. A., Mahmood, K., Kumari, S., & Xiong, H. (2020). Comments on toward secure and provable authentication for internet of things: realizing industry 4.0. IEEE Internet of Things Journal, 7(5), 4676–4681.
Delvaux, J. (2019). Machine-learning attacks on polypufs, ob-pufs, rpufs, lhs-pufs, and puf-fsms. IEEE Transactions on Information Forensics and Security, 14(8), 2043–2058.
Yogesh, P. R., et al. (2020). Formal verification of secure evidence collection protocol using ban logic and Avispa. Procedia Computer Science, 167, 1334–1344.
Sahoo, S. S., Mohanty, S., Sahoo, K. S., Daneshmand, M., & Gandomi, A. H. (2023). A three factor based authentication scheme of 5G wireless sensor networks for IoT system. IEEE Internet of Things Journal., 1, 23.
Kumar, D. (2023). Cryptanalysis and improvement of an authentication protocol for wireless sensor networks. Transactions on Emerging Telecommunications Technologies, 34(5), e4747.
Huang, W. (2024). ECC-based three-factor authentication and key agreement scheme for wireless sensor networks. Scientific Reports, 14(1), 1787.
Acknowledgements
Not Applicable.
Funding
Not Applicable.
Author information
Authors and Affiliations
Contributions
Writing-original draft: Sumble Fatima (SF), Muhammad Arslan Akram (MAA), Adnan Noor Mian (ANM), Saru Kumari (SK), and Chien-Ming Chen (CMC); Conceptualization: SF, MAA, and ANM; Writing-review and Editing: MAA, ANM, SK, and CMC; Investigation ANM, SK and CMC; Supervision: MAA, ANM and SK;
Corresponding author
Ethics declarations
Conflict of interest
The authors proclaim that they have no Conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fatima, S., Akram, M.A., Mian, A.N. et al. On the Security of a Blockchain and PUF-Based Lightweight Authentication Protocol for Wireless Medical Sensor Networks. Wireless Pers Commun 136, 1079–1106 (2024). https://doi.org/10.1007/s11277-024-11318-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-024-11318-6