Cyber Security In Healthcare Using Iomt
DOI:
https://doi.org/10.52783/jns.v14.4009Keywords:
IoMT, nCyberSecurity, SmartPills, HealthcareAbstract
Internet of Medical Things (IoMT) is one such realistic application of IoT in healthcare domain, which provides an opportunity to evolve the existing healthcare system and provide better healthcare services and quality life to the patients. It opens a way for the potential security attacks to the ongoing IoMT communication where the adversaries can get unauthorised access to the personal, confidential and sensitive health related information that can be utilised for malicious purposes. To overcome security issues of IoMT, various security protocols have been proposed and designed in the recent couple of years. This paper discussed about healthcare analytics using IoT. The Internet of Medical Things (IoMT) refers to the network of interconnected medical devices, sensors, and applications that transmit health data over the internet for improved healthcare delivery
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A. Sardi, A. Rizzi, E. Sorano, and A. Guerrieri, “Cyber Risk in Health Facilities: A Systematic Literature Review,” Sustainability, vol. 12, no. 17, p. 7002, Aug. 2020, doi: https://doi.org/10.3390/su12177002.
P. Ambrose and C. Basu, “Interpreting the impact of perceived privacy and security concerns in patients’ use of online health information systems,” Journal of Information Privacy and Security, vol. 8, no. 1, pp. 38–50, Jan. 2012, doi: 10.1080/15536548.2012.11082761.
R. K. Pathinarupothi, ``Clinically aware data summarization at the edge for Internet of Medical Things,'' in Proc. IEEE Int. Conf. Pervasive Comput. Commun.Workshops (PerCom Workshops), Mar. 2019, pp. 437_438, doi: 10.1109/PERCOMW.2019.8730765.
D. Birnbaum, E. Borycki, B. T. Karras, E. Denham, and P. Lacroix, “Addressing Public Health informatics patient privacy concerns,” Clinical Governance an International Journal, vol. 20, no. 2, pp. 91–100, Apr. 2015, doi: 10.1108/cgij-05-2015-0013.
Q. W. Cao, ``Description of SA weak password's harm and solution in the SQL server system,'' J. Xingtai Polytech. College, vol. 29, no. 1, Feb. 2012.
A. S. Salsabila, M. D. Fikri, M. S. Andika, and N. A. Harahap, “Potential and threat analysis towards cybersecurity in South East Asia,” Journal of ASEAN Dynamics and Beyond, vol. 1, no. 1, p. 1, Dec. 2020, doi: 10.20961/aseandynamics.v1i1.46794.
W. Burke, T. Oseni, A. Jolfaei, and I. Gondal, “Cybersecurity indexes for eHealth,” Proceedings of the Australasian Computer Science Week Multiconference, Jan. 2019, doi: 10.1145/3290688.3290721.
A. Raghavan, M. A. Demircioglu, and A. Taeihagh, “Public Health Innovation through Cloud Adoption: A Comparative Analysis of Drivers and Barriers in Japan, South Korea, and Singapore,” International Journal of Environmental Research and Public Health, vol. 18, no. 1, p. 334, Jan. 2021, doi: 10.3390/ijerph18010334.
T. A. Mattei, ``Privacy, con_dentiality, and security of health care information: Lessons from the recent wannacry cyberattack,'' World Neuro-surgery, vol. 104, pp. 972_974, Aug. 2017.
Y. He, A. Aliyu, M. Evans, and C. Luo, ``Health care cybersecurity challenges and solutions under the climate of COVID-19: Scoping review,'' J. Med. Internet Res., vol. 23, no. 4, Apr. 2021, Art. no. e21747.
Prasad, Ramjee, and Vandana Rohokale. "Cyber Threats and Attack Overview." In Cyber Security: The Lifeline of Information and Communication Technology, pp. 15-31. Springer, Cham, 2020.
Vyawahare, M., & Chatterjee, M. (2020). Survey on Detection and Prediction Techniques of Drive-by Download Attack in OSN. In Advanced Computing Technologies and Applications (pp. 453-463). Springer, Singapore.
R. Sihwail, K. Omar, and K. A. Z. Ariffin, “A survey on malware analysis techniques: static, dynamic, hybrid and memory analysis,” International Journal on Advanced Science Engineering and Information Technology, vol. 8, no. 4–2, pp. 1662–1671, Sep. 2018, doi: 10.18517/ijaseit.8.4-2.6827.
Tahir, Rabia. "A study on malware and malware detection techniques." International Journal of Education and Management Engineering 8, no. 2 (2018):20.
C. T. Thanh and I. Zelinka, “A survey on Artificial Intelligence in Malware as Next-Generation Threats,” MENDEL, vol. 25, no. 2, pp. 27–34, Dec. 2019, doi: 10.13164/mendel.2019.2.027.
P. Bory, “Deep new: The shifting narratives of artificial intelligence from Deep Blue to AlphaGo,” Convergence the International Journal of Research Into New Media Technologies, vol. 25, no. 4, pp. 627–642, Feb. 2019, doi: 10.1177/1354856519829679.
Y.-T. Hou, Y. Chang, T. Chen, C.-S. Laih, and C.-M. Chen, “Malicious web content detection by machine learning,” Expert Systems With Applications, vol. 37, no. 1, pp. 55–60, May 2009, doi: 10.1016/j.eswa.2009.05.023.
K. Singh and N. Goyal, "A Comparison of Machine Learning Attributes for Detecting Malicious Websites," 11th International Conference on Communication Systems & Networks (COMSNETS 2019), Bengaluru, India, 2019, pp. 352-358.
Ma J, Saul L.K., Savage S. and Voelker, 2009, June. Beyond blacklists: learning to detect malicious websites from suspicious URLs. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining(pp.1245-1254). ACM.
David, Eli, Nadav Maman, and Guy Caspi. "Methods and systems for detecting malicious webpages." US Patent Application 15/641,851, filed January 10, 2019.
Al-Yaseen, W., Othman, Z., Ahmad Nazri, M.Z.: Multi-level hybrid support vector machine and extreme learning machine based on modified k-means for intrusion detection system. Expert Systems with Applications 67 (01 2017).
Banu, R., M, A., C, A., S, A., Ujwala, H., N, H.: Detecting phishing attacks using natural language processing and machine learning. pp. 1210–1214 (05 2019).
I. Baptista, S. Shiaeles, and N. Kolokotronis, “A novel malware detection system based on machine learning and binary visualization,” 2022 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6, May 2019, doi: 10.1109/iccw.2019.8757060.
Barbara, D., Couto, J., Jajodia, S., Popyack, L., Wu, N.: Adam: Detecting intru-sions by data mining pp. 5–6 (07 2001).
Bose, S., Barao, T., Liu, X.: Explaining ai for malware detection: Analysis of mechanisms of malconv. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8 (2020).
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