Blockchain-assisted cyber security in medical things using artificial intelligence

Mohammed Alshehri; Mohammed Alshehri

doi:10.3934/era.2023035

Electronic Research Archive

2023, Volume 31, Issue 2: 708-728. doi: 10.3934/era.2023035

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Research article Special Issues

Blockchain-assisted cyber security in medical things using artificial intelligence

Mohammed Alshehri ^,

Department of Information Technology, College of Computer and Information Technology, Majmaah University, Majmaah 11952, Saudi Arabia

Received: 20 October 2022 Revised: 03 November 2022 Accepted: 07 November 2022 Published: 22 November 2022

The Internet of Medical Things (IoMT) significantly impacts our healthcare system because it allows us to track and verify patient medical data before storing it in the cloud for future use. A rapidly expanding platform like IoMT requires high security to keep all data safe. The patient's prescription history and other sensitive information must be encrypted and managed with great care. Nevertheless, it is challenging to determine what data uses are acceptable while protecting patient privacy and security. Understanding the limits of current technologies and envisioning future research paths is crucial for establishing a safe and reliable data environment. An untrustworthy person can communicate with a trustworthy person via blockchain, a decentralized digital ledger that allows for end-to-end communication. Therefore, this research suggests that the healthcare industry with blockchain-integrated cyber-security based on artificial intelligence (BICS-AI) in medical care to preserve medical-related things. Blockchain applications have the potential to consistently identify the most severe, potentially life-threatening mistakes in the medical field. The use of blockchain for decentralized data protection helps to protect patient health records from compromise. With the help of an access control provider (ACP), here came up with a lightweight solution that addresses this issue by allowing the delegating of security operations. Medical data from IoMT and integrated devices can be collected and stored securely and distributed using a conventional in-depth approach combined with blockchain, making it suitable for healthcare professionals such as nursing homes, hospitals, and the healthcare industry where data exchange is required. The research findings indicate that the suggested system is viable and has a 94.84$ \% $ security rate, a security performance of 96.4$ \% $, a success rate of 89.9$ \% $, and a 5.1$ \% $ latency rate compared to traditional methods.
- blockchain,
- cyber security,
- artificial intelligence,
- medical care,
- IoMT
Citation: Mohammed Alshehri. Blockchain-assisted cyber security in medical things using artificial intelligence[J]. Electronic Research Archive, 2023, 31(2): 708-728. doi: 10.3934/era.2023035

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Abstract

The Internet of Medical Things (IoMT) significantly impacts our healthcare system because it allows us to track and verify patient medical data before storing it in the cloud for future use. A rapidly expanding platform like IoMT requires high security to keep all data safe. The patient's prescription history and other sensitive information must be encrypted and managed with great care. Nevertheless, it is challenging to determine what data uses are acceptable while protecting patient privacy and security. Understanding the limits of current technologies and envisioning future research paths is crucial for establishing a safe and reliable data environment. An untrustworthy person can communicate with a trustworthy person via blockchain, a decentralized digital ledger that allows for end-to-end communication. Therefore, this research suggests that the healthcare industry with blockchain-integrated cyber-security based on artificial intelligence (BICS-AI) in medical care to preserve medical-related things. Blockchain applications have the potential to consistently identify the most severe, potentially life-threatening mistakes in the medical field. The use of blockchain for decentralized data protection helps to protect patient health records from compromise. With the help of an access control provider (ACP), here came up with a lightweight solution that addresses this issue by allowing the delegating of security operations. Medical data from IoMT and integrated devices can be collected and stored securely and distributed using a conventional in-depth approach combined with blockchain, making it suitable for healthcare professionals such as nursing homes, hospitals, and the healthcare industry where data exchange is required. The research findings indicate that the suggested system is viable and has a 94.84$ \% $ security rate, a security performance of 96.4$ \% $, a success rate of 89.9$ \% $, and a 5.1$ \% $ latency rate compared to traditional methods.

References

[1]	B. Alqaralleh, T. Vaiyapuri, V. S. Parvathy, D. Gupta, A. Khanna, K. Shankar, Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things Environment, Pers. Ubiquitous Comput., 5 (2021). https://doi.org/10.1007/s00779-021-01543-2
[2]	O. Diekmann, M. Gyllenberg, H. Huang, M. Kirkilionis, J. A. J. Metz, H. R. Thieme, On the formulation and analysis of general deterministic structured population models. II. Nonlinear theory, J. Math. Biol., 43 (2001), 157–189. https://doi.org/10.1007/s002850170002 doi: 10.1007/s002850170002
[3]	S. Marchesseau, H. Delingette, M. Sermesant, R. Cabrera-Lozoya, C. Tobon-Gomez, P. Moireau, et al., Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes, Med. Image Anal., 17 (2013), 816–829. https://doi.org/10.1016/j.media.2013.04.012 doi: 10.1016/j.media.2013.04.012
[4]	A. K. Das, B. Bera, D. Giri, AI and blockchain-based cloud-assisted secure vaccine distribution and tracking in the iomt-enabled covid-19 environment, IEEE Internet Things Mag., 4 (2021), 26–32. https://doi.org/10.1109/IOTM.0001.2100016 doi: 10.1109/IOTM.0001.2100016
[5]	A. Abbas, R. Alroobaea, M. Krichen, S. Rubaiee, S. Vimal, F. M. Almansour, Blockchain-assisted secured data management framework for health information analysis based on the Internet of Medical Things, Pers. Ubiquitous Comput., 19 (2021), 1–4. https://doi.org/10.1007/s00779-021-01583-8 doi: 10.1007/s00779-021-01583-8
[6]	S. Razdan, S. Sharma, Internet of Medical Things (IoMT): overview, emerging technologies, and case studies, IETE Tech. Rev., 39 (2022), 775–788. https://doi.org/10.1080/02564602.2021.1927863 doi: 10.1080/02564602.2021.1927863
[7]	M. Elsayeh, K. A. Ezzat, H. El-Nashar, L. N. Omran, Cybersecurity architecture for the Internet of Medical Things and connected devices using blockchain, Biomed. Eng. Appl. Basis Commun., 33 (2021), 2150013. https://doi.org/10.4015/S1016237221500137 doi: 10.4015/S1016237221500137
[8]	A. Sharma, Sarishma, R. Tomar, N. Chilamkurti, B. G. Kim, Blockchain-based smart contracts for the Internet of Medical Things in e-healthcare, Electronics, 9 (2020), 1609. https://doi.org/10.3390/electronics9101609 doi: 10.3390/electronics9101609
[9]	B. S. Egala, A. K. Pradhan, V. R. Badarla, S. P. Mohanty, Fortified-Chain: a blockchain-based framework for security and privacy assured Internet of medical things with effective access control, IEEE Internet Things J., 8 (2021), 11717–11731. https://doi.org/10.1109/JIOT.2021.3058946 doi: 10.1109/JIOT.2021.3058946
[10]	X. Li, B. Tao, H. N. Dai, M. Imran, D. Wan, D. Li, Is blockchain for the Internet of Medical Things a panacea for COVID-19 pandemic? Pervasive Mob. Comput., 75 (2021), 101434. https://doi.org/10.1016/j.pmcj.2021.101434
[11]	M. Wazid, A. K. Das, S. Shetty, M. Jo, A tutorial and future research for building a blockchain-based secure communication scheme for the Internet of intelligent things, IEEE Access, 8 (2020), 88700–88716. https://doi.org/10.1109/ACCESS.2020.2992467 doi: 10.1109/ACCESS.2020.2992467
[12]	M. Wazid, A. K. Das, S. Shetty, M. Jo, Blockchain-enabled internet of medical things to combat COVID-19, IEEE Internet Things Mag., 3 (2020), 52–57. https://doi.org/10.1109/IOTM.0001.2000087 doi: 10.1109/IOTM.0001.2000087
[13]	R. Kumar, R. Tripathi, Towards design and implementation of security and privacy framework for Internet of medical things (iomt) by leveraging blockchain and ipfs technology, J. Supercomputing, 77 (2021), 7916–7955. https://doi.org/10.1007/s11227-020-03570-x doi: 10.1007/s11227-020-03570-x
[14]	R. A. Rayan, C. Tsagkaris, Blockchain-Based IoT for Personalized Pharmaceuticals, in Internet of Medical Things, (2021), 51–62. https://doi.org/10.1201/9780429296864-4
[15]	P. P. Ray, D. Dash, K. Salah, N. Kumar, Blockchain for IoT-based healthcare: background, consensus, platforms, and use cases, IEEE Syst. J., 15 (2020), 85–94. https://doi.org/10.1109/JSYST.2020.2963840 doi: 10.1109/JSYST.2020.2963840
[16]	M. A. Rahman, M. S. Hossain, An internet of medical things-enabled edge computing framework for tackling COVID-19, IEEE Syst. J., 8 (2020), 15847–15854. https://doi.org/10.1109/JIOT.2021.3051080 doi: 10.1109/JIOT.2021.3051080
[17]	W. Meng, W. Li, L. Zhu, Enhancing medical smartphone networks via blockchain-based trust management against insider attacks, IEEE Trans. Eng. Manage., 67 (2020), 1377–1386. https://doi.org/10.1109/TEM.2019.2921736 doi: 10.1109/TEM.2019.2921736
[18]	P. A. Catherwood, D. Steele, M. Little, S. McComb, J. McLaughlin, A community-based IoT personalized wireless healthcare solution trial, IEEE J. Transl. Eng. Health Med., 6 (2021), 1–13. https://doi.org/10.1109/JTEHM.2018.2822302 doi: 10.1109/JTEHM.2018.2822302
[19]	H. Liao, Z. Zhou, X. Zhao, L. Zhang, S. Mumtaz, A. Jolfaei, et al., Learning-based context-aware resource allocation for edge-computing-empowered industrial IoT, IEEE Internet Things J., 7 (2019), 4260–4277. https://doi.org/10.1109/JIOT.2019.2963371 doi: 10.1109/JIOT.2019.2963371
[20]	S. Chakraborty, V. Bhatt, T. Chakravorty, Impact of IoT adoption on agility and flexibility of healthcare organization, Int. J. Innovative Technol. Exploring Eng., 8 (2019), 2673–2681. https://doi.org/10.35940/ijitee.K2119.0981119 doi: 10.35940/ijitee.K2119.0981119
[21]	X. Chen, M. Ma, A. Liu, Dynamic power management and adaptive packet size selection for IoT in e-Healthcare, Comput. Electr. Eng., 65 (2018), 357–375. https://doi.org/10.1016/j.compeleceng.2017.06.010 doi: 10.1016/j.compeleceng.2017.06.010
[22]	A. Choi, H. Shin, Longitudinal healthcare data management platform of healthcare IoT devices for personalized services, J. Universal Comput. Sci., 24 (2018), 1153–1169. Available from: https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/5295.
[23]	M. M. Dhanvijay, S. C. Patil, Internet of Things: A survey of enabling technologies in healthcare and its applications, Comput. Networks, 153 (2019), 113–131. https://doi.org/10.1016/j.comnet.2019.03.006 doi: 10.1016/j.comnet.2019.03.006
[24]	O. I. Khalaf, G. M. Abdulsahib, N. A. Zghair, IOT fire detection system using sensor with Arduino, AUS, 26 (2019), 74–78.
[25]	B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant, K. Mankodiya, Towards fog-driven IoTeHealth: promises and challenges of IoT in medicine and healthcare, Future Gener. Comput. Syst., 78 (2018), 659–676. https://doi.org/10.1016/j.future.2017.04.036 doi: 10.1016/j.future.2017.04.036
[26]	G. García, P. Sánchez-Alonso, C. E. M. Marín, Visualization of information: a proposal to improve the search and access to digital resources in repositories, Ing. Invest., 34 (2014), 83–89. https://doi.org/10.15446/ing.investig.v34n1.39449 doi: 10.15446/ing.investig.v34n1.39449
[27]	M. M. Islam, A. Rahaman, M. R. Islam, Development of smart healthcare monitoring system in IoT environment, SN Comput. Sci., 1 (2020), 185. https://doi.org/10.1007/s42979-020-00195-y doi: 10.1007/s42979-020-00195-y
[28]	V. E. Sathishkumar, C. Shin, Y. Cho, Efficient energy consumption prediction model for a data analytics-enabled industry building in a smart city, Build. Res. Inf., 49 (2021), 127–143. https://doi.org/10.1080/09613218.2020.1809983 doi: 10.1080/09613218.2020.1809983
[29]	Y. S. Jeong, S. S. Shin, An IoT healthcare service model of a vehicle using implantable devices, Cluster Comput., 21 (2018), 1059–1068. https://doi.org/10.1007/s10586-016-0689-z doi: 10.1007/s10586-016-0689-z
[30]	W. Li, C. Jung, J. Park, IoT healthcare communication system for IEEE 11073 PhD and IHE PCD-01 integration using CoAP, KSII Trans. Internet Inf. Syst., 12 (2018), 1396–1414. https://doi.org/10.3837/tiis.2018.04.001 doi: 10.3837/tiis.2018.04.001
[31]	M. O. Lawal, Tomato detection based on modified YOLOv3 framework, Sci. Rep., 11 (2021), 1447. https://doi.org/10.1038/s41598-021-81216-5 doi: 10.1038/s41598-021-81216-5
[32]	W. Khan, K. Raj, T. Kumar, A. M. Roy, B. Luo, Introducing urdu digits dataset with demonstration of an efficient and robust noisy decoder-based pseudo example generator, Symmetry, 14 (2022), 1976. https://doi.org/10.3390/sym14101976 doi: 10.3390/sym14101976

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