Enhancing healthcare security measures in IoTT applications through a Hesitant Fuzzy-Based integrated approach

Waeal J. Obidallah; Waeal J. Obidallah

doi:10.3934/math.2024439

AIMS Mathematics

2024, Volume 9, Issue 4: 9020-9048. doi: 10.3934/math.2024439

Previous Article Next Article

Research article Special Issues

Enhancing healthcare security measures in IoTT applications through a Hesitant Fuzzy-Based integrated approach

Waeal J. Obidallah ^,

College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, 11673, Saudi Arabia

Received: 29 January 2024 Revised: 10 February 2024 Accepted: 21 February 2024 Published: 05 March 2024
MSC : 03B52, 94D05, 94D10

Due to their impact on transportation, Internet of Transportation Things (IoTT) devices have garnered attention recently. Their most notable use is in healthcare, where transportation has been significantly influenced by Internet of Things (IoT) devices. However, threats to infrastructure integrity, medical equipment vulnerabilities, encryption, data integrity threats, and various other security issues make these devices particularly vulnerable. They transmit a considerable amount of sensitive data via sensors and actuators. Given their susceptibility to various attacks, securing the application security of IoTT is crucial. Consequently, IoTT device-based applications must undergo thorough security screening before integration into the healthcare network. Additionally, the authentication technique employed must be robust and reliable. IoTT device evaluation should be impartial and take into account security risk issues. This study proposes an evaluation approach for IoTT devices that utilizes key security risk factors to ensure reliable and secure authentication. Employing hybrid multicriteria decision-making, the suggested strategy evaluates authentication features to select the optimal hospital information system. The hesitant fuzzy analytic hierarchy process-technique for order of preference by similarity to ideal solution (Hesitant Fuzzy AHP-TOPSIS) method is used to systematically examine security risks in a real-time case study with seven alternatives. Results indicate that mediXcel electronic medical records are the most viable, while the Caresoft hospital information system is the least viable, providing valuable insights for future studies and IoTT application professionals. This research addresses security issues to enhance patient data integrity and privacy, facilitating the seamless integration of IoTT applications into healthcare, particularly in emergency healthcare.
- web-based applications,
- internet of transportation things,
- security risks assessment,
- healthcare emergency services,
- hesitant fuzzy decision-making method.
Citation: Waeal J. Obidallah. Enhancing healthcare security measures in IoTT applications through a Hesitant Fuzzy-Based integrated approach[J]. AIMS Mathematics, 2024, 9(4): 9020-9048. doi: 10.3934/math.2024439

Related Papers:

Abstract

Due to their impact on transportation, Internet of Transportation Things (IoTT) devices have garnered attention recently. Their most notable use is in healthcare, where transportation has been significantly influenced by Internet of Things (IoT) devices. However, threats to infrastructure integrity, medical equipment vulnerabilities, encryption, data integrity threats, and various other security issues make these devices particularly vulnerable. They transmit a considerable amount of sensitive data via sensors and actuators. Given their susceptibility to various attacks, securing the application security of IoTT is crucial. Consequently, IoTT device-based applications must undergo thorough security screening before integration into the healthcare network. Additionally, the authentication technique employed must be robust and reliable. IoTT device evaluation should be impartial and take into account security risk issues. This study proposes an evaluation approach for IoTT devices that utilizes key security risk factors to ensure reliable and secure authentication. Employing hybrid multicriteria decision-making, the suggested strategy evaluates authentication features to select the optimal hospital information system. The hesitant fuzzy analytic hierarchy process-technique for order of preference by similarity to ideal solution (Hesitant Fuzzy AHP-TOPSIS) method is used to systematically examine security risks in a real-time case study with seven alternatives. Results indicate that mediXcel electronic medical records are the most viable, while the Caresoft hospital information system is the least viable, providing valuable insights for future studies and IoTT application professionals. This research addresses security issues to enhance patient data integrity and privacy, facilitating the seamless integration of IoTT applications into healthcare, particularly in emergency healthcare.

References

[1]	Cyber Management Alliance, IoT Security: 5 Cyber Attacks Caused by IoT Security Vulnerabilities, 2022. Available from: https://www.cm-alliance.com/cybersecurity-blog/iot-security-5-cyber-attacks-caused-by-iot-security-vulnerabilities
[2]	Developing National Information Security Strategy for the Kingdom of Saudi Arabia, National Information Security Strategy of Saudi Arabia, 2024. Available from: https://www.itu.int/en/ITU-D/Cybersecurity/Documents/National_Strategies_Repository/SaudiArabia_NISS_Draft_7_EN.pdf
[3]	Saudi Arabia Internet of Things (IoT) Market, Research and Markets, 2022. Available from: https://www.researchandmarkets.com/reports/5562151/saudi-arabia-internet-of-things-iot-market-by
[4]	Saudi Arabia-Country Commercial Guide, U.S. Department of Commerce, 2024. Available from: https://www.trade.gov/country-commercial-guides/saudi-arabia-information-and-communications-technology
[5]	Healthcare, Unified National Platform, Government of Saudi Arabia, 2024. Available from: https://www.my.gov.sa/wps/portal/snp/aboutksa/HealthCareInKSA/?lang = en
[6]	S. T. U. Shah, H. Yar, I. Khan, M. Ikram, H. Khan, Internet of things-based healthcare: recent advances and challenges, in Applications of Intelligent Technologies in Healthcare, (2019), 153–162. https://doi.org/10.1007/978-3-319-96139-2_15
[7]	Internet of Things, IoT Demand in Saudi Arabia, A Survey-Based Study, Communications and Information Technology Commission, 2021. IoT Demand in Saudi Arabia. Available from: https://www.cst.gov.sa/en/researchs-studies/research-innovation/Documents/CITC-IoT_Demand.pdf
[8]	Cyber Security Framework, Saudi Arabian Monetary Authority, Riyadh, Saudi Arabia, 2017. Available from: https://www.sama.gov.sa/en-US/Laws/FinanceRules/SAMA%20Cyber%20Security%20Framework%20v1.0%20final_updated.pdf
[9]	X. Huang, S. Nazir, Evaluating security of internet of medical things using the analytic network process method, Secur. Commun. Netw., 2020, 1–14. https://doi.org/10.1155/2020/8829595 doi: 10.1155/2020/8829595
[10]	K. Kim, I. M. Alshenaifi, S. Ramachandran, J. Kim, T. Zia, A. Almorjan, Cybersecurity and cyber forensics for smart cities: A comprehensive literature review and survey, Sensors, 23 (22023), 3681. https://doi.org/10.3390/s23073681 doi: 10.3390/s23073681
[11]	S. Nasiri, F. Sadoughi, M. H. Tadayon, A. Dehnad, Security requirements of Internet of Things-based healthcare system: A survey study, Acta Informatica Medica, 27 (2019), 253. https://doi.org/10.5455/aim.2019.27.253-258 doi: 10.5455/aim.2019.27.253-258
[12]	M. R. Mokhtar, M. P. Abdullah, M. Y. Hassan, F. Hussin, Combination of AHP-PROMETHEE and TOPSIS for selecting the best demand side management (DSM) options, In IEEE Student Conference on Research and Development (SCOReD), (2015), 367–372. https://doi.org/10.1109/SCORED.2015.7449357
[13]	A. Salamai, O. K. Hussain, M. Saberi, E. Chang, F. K. Hussain, Highlighting the Importance of Considering the Impacts of Both External and Internal Risk Factors on Operational Parameters to Improve Supply Chain Risk Management, IEEE Access, 7 (2019), 49297–49315. https://doi.org/10.1109/ACCESS.2019.2902191 doi: 10.1109/ACCESS.2019.2902191
[14]	P. Radanliev, D. C. D. Roure, R. Nicolescu, M. Huth, R. M. Montalvo, S. Cannady, et al., Future developments in cyber risk assessment for the Internet of Things, Comput. Ind., 102 (2018), 14–22. https://doi.org/10.1016/j.compind.2018.08.002 doi: 10.1016/j.compind.2018.08.002
[15]	M. Humayun, N. Z. Jhanjhi, A. Almotilag, Real-time security health and privacy monitoring for Saudi highways using cutting-edge technologies, Appl. Sci., 12 (2022), 1–19. https://doi.org/10.3390/app12042177 doi: 10.3390/app12042177
[16]	A. Hussain, T. Ali, F. Althobiani, U. Draz, M. Irfan, S. Yasin, et al., Security framework for IoT-based real-time health applications, Electronics, 10 (2021), 1–21. https://doi.org/10.3390/electronics10060719 doi: 10.3390/electronics10060719
[17]	Q. Li, An improved fuzzy AHP approach to evaluating conductor joint alternatives, In Seventh International Conference on Fuzzy Systems and Knowledge Discovery, (2010), 811–814. https://doi.org/10.1109/FSKD.2010.5569216
[18]	J. Shahid, R. Ahmad, A. K. Kiani, T. Ahmad, S. Saeed, A. M. Almuhaideb, Data protection and privacy of the Internet of Healthcare Things (IoHTs), Appl. Sci., 12 (2022), 1–18. https://doi.org/10.3390/app12041927 doi: 10.3390/app12041927
[19]	A. Rejeb, K. Rejeb, H. Treiblmaier, A. Appolloni, S. Alghamdi, Y. Alhasawi, et al., The Internet of Things (IoT) in healthcare: Taking stock and moving forward, Internet Things, (2023), 1–20. https://doi.org/10.1016/j.iot.2023.100721 doi: 10.1016/j.iot.2023.100721
[20]	M. R. Alsaadi, S. Z. Ahmad, A quality function deployment strategy for improving mobile-government service quality in the Gulf Cooperation Council countries, Benchmarking, 25 (2018), 3276–3295. https://doi.org/10.1108/BIJ-12-2017-0333 doi: 10.1108/BIJ-12-2017-0333
[21]	M. Alqahtani, IoT within the Saudi healthcare industry during Covid-19, In Proceedings of International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2021, 1 M. (2022), 469–483. https://doi.org/10.1007/978-3-030-82616-1_40
[22]	M. Binsawad, M. Albahar, A technology survey on IoT applications serving umrah and hajj, Appl. Comput. Intell. Soft Comput., (2022) 1–15. https://doi.org/10.1155/2022/1919152 doi: 10.1155/2022/1919152
[23]	L. Lee, S. Chen, Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets, In International Conference on Machine Learning and Cybernetics, 8 (2008), 3260–3265. https://doi.org/10.1109/ICMLC.2008.4620968 doi: 10.1109/ICMLC.2008.4620968
[24]	M. T. Quasim, Challenges and applications of Internet of Things (IoT) in Saudi Arabia, (2021). Available from: https://easychair.org/publications/preprint_download/r2W4
[25]	G. Büyüközkan, G. Çifçi, A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in the healthcare industry, Expert Syst. Appl., 39 (2015), 2341–2354. https://doi.org/10.1016/j.eswa.2011.08.061 doi: 10.1016/j.eswa.2011.08.061
[26]	C. Cubukcu, C. Cantekin, Using a combined fuzzy-AHP and TOPSIS decision model for selecting the best firewall alternative, J. Fuzzy Extension Appl., 3 (2022), 192–200. https://doi.org/10.22105/jfea.2021.313606.1167 doi: 10.22105/jfea.2021.313606.1167
[27]	A. Memari, A. Dargi, M. R. A. Jokar, R. Ahmad, A. Rahman, Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method, J. Manuf. Syst., 50 (2019), 9–24. https://doi.org/10.1016/j.jmsy.2018.11.002 doi: 10.1016/j.jmsy.2018.11.002
[28]	F. K. Gündoğdu, S. Duleba, S. Moslem, S. Aydın, Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model, Appl. Soft Comput., 100 (2021), 106920. https://doi.org/10.1016/j.asoc.2020.106920 doi: 10.1016/j.asoc.2020.106920
[29]	A. S. Anvari, The applications of MCDM methods in COVID-19 pandemic: A state of the art review, Appl. Soft Comput., (2022), 109238. https://doi.org/10.1016/j.asoc.2022.109238 doi: 10.1016/j.asoc.2022.109238
[30]	S. Chen, S. Cheng, T. Lan, A new multicriteria decision making method based on the TOPSIS method and similarity measures between intuitionistic fuzzy sets, In International Conference on Machine Learning and Cybernetics (ICMLC), Jeju, (2016), 692–696. https://doi.org/10.1109/ICMLC.2016.7872972
[31]	O. Dogan, M. Deveci, F. Canıtez, A corridor selection for locating autonomous vehicles using an interval-valued intuitionistic fuzzy AHP and TOPSIS method, Soft Comput., 24 (2020), 8937–8953. https://doi.org/10.1007/s00500-019-04421-5 doi: 10.1007/s00500-019-04421-5
[32]	Medixcel, 2024. Available from: https://www.medixcel.in/
[33]	TRIO Corporation, 2024. Available from: https://triocorporation.in/
[34]	CareSoft, 2024. Available from: https://caresoft.co.in/
[35]	Genipulse, 2024. Available from: http://www.genipulse.com/
[36]	LiveHealth, 2024. Available from: https://livehealth.solutions/
[37]	Visual Infosoft, 2024. Available from: https://www.visualinfosoft.com/
[38]	NextGen, 2024. Available from: https://www.nextgen.com/
[39]	T. Kaya, C. Kahraman, An integrated fuzzy AHP–ELECTRE methodology for environmental impact assessment, Expert Syst. Appl., 38 (2011), 8553–8562. https://doi.org/10.1016/j.eswa.2011.01.057 doi: 10.1016/j.eswa.2011.01.057
[40]	Saudi Arabia Internet of Things (IoT) Market, TechSci Research, 2023. Available from: https://www.techsciresearch.com/report/saudi-arabia-internet-of-things-iot-market/7663.html
[41]	V. Torra, Y. Narukawa, On hesitant fuzzy sets and decision, In Proc. IEEE Int. Conf. Fuzzy Syst., (2009), 1378–1382. https://doi.org/10.1109/FUZZY.2009.5276884 doi: 10.1109/FUZZY.2009.5276884
[42]	K. Sahu, R. K. Srivastava, S. Kumar, M. Saxena, B. K. Gupta, Integrated hesitant fuzzy-based decision-making framework for evaluating sustainable and renewable energy, Int. J. Data Sci. Anal., 16 (2023), 371–390. https://doi.org/10.1007/s41060-023-00426-4 doi: 10.1007/s41060-023-00426-4
[43]	R. M. Rodríguez, L. Martínez, V. Torra, Z. S. Xu, F. Herrera, Hesitant fuzzy sets: State of the art and future directions, Int. J. Intell. Syst., 29 (2014), 495–524. https://doi.org/10.1002/int.21654 doi: 10.1002/int.21654
[44]	P. Singh, Z. Elmi, V. K. Meriga, Internet of Things for sustainable railway transportation: Past, present, and future, Cleaner Logistics Supply Chain, 4 (2022), 100065. https://doi.org/10.1016/j.clscn.2022.100065 doi: 10.1016/j.clscn.2022.100065
[45]	P. Singh, Z. Elmi, Blockchain and AI technology convergence: Applications in transportation systems, Veh. Commun., 100521 (2022). https://doi.org/10.1016/j.vehcom.2022.100521 doi: 10.1016/j.vehcom.2022.100521
[46]	P. Singh, M. A. Dulebenets, Deployment of autonomous trains in rail transportation: Current trends and existing challenges, IEEE Access, 9 (2021), 91427–91461. 10.1109/ACCESS.2021.3091550
[47]	E. M. Adere, Blockchain in healthcare and IoT: A systematic literature review, Array, 14 (2022), 100139. https://doi.org/10.1016/j.array.2022.100139 doi: 10.1016/j.array.2022.100139
[48]	T. Wang, H. Hua, Challenges of blockchain in new generation energy systems and future outlooks, Int. J. Elec. Power, 135 (2021), 107499. https://doi.org/10.1016/j.ijepes.2021.107499 doi: 10.1016/j.ijepes.2021.107499
[49]	M. S. Sangari, A. Mashatan, A data-driven, comparative review of the academic literature and news media on blockchain-enabled supply chain management: Trends, gaps, and research needs, Comput. Ind., 143 (2022), 103769. https://doi.org/10.1016/j.compind.2022.103769 doi: 10.1016/j.compind.2022.103769

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)