Investigating the security issues of multi-layer IoMT attacks using machine learning techniques

Conference poster


Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Investigating the security issues of multi-layer IoMT attacks using machine learning techniques.
AuthorsAl Sukhni, B., Manna, S., Dave, J. and Zhang, L.
TypeConference poster
Description

The Internet of Medical Things (IoMT) plays a significant role in the healthcare system as it improves effectiveness and efficiency of treatment by continuously monitoring patients using smart home sensor and wearables (Fig. 1), early disease diagnosis using data collected from the Internet of Medical Things (IoMT) devices and assisting doctors in deciding the best treatment and acting immediately if necessary. Additionally, it helps to reduce the number of hospital visits, limiting carbon footprint.IoMT devices are vulnerable to Multi-layer attacks because most of these devices are resource-constrained and portable, which is why there is not that much implementation of security features in these devices and making them a prime target for intruders looking to steal patients’ sensitive information and healthcare records. Multi-layer attacks are a group of attacks exploiting multiple layers of IoMT architecture. Denial-of-service (DoS) and Man-In-The-Middle (MITM) attacks, for instance, can target the three layers of the IoMT system and lead to serious consequences, such as theft of patients’ sensitive data and reputational damages. The main aim of the project is to create a robust IDS for IoT devices.

KeywordsIoMT security; Multi-layer attacks ; Machine learning
Year2022
ConferenceExploring Research and Development in the MedTech, Life Science and Healthcare Sectors
Related URLhttps://www.eventbrite.co.uk/e/maidstones-first-research-development-event-tickets-408607454897
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References

[1] Rasool, R.U., Ahmad, H.F., Rafique, W., Qayyum, A. and Qadir, J., 2022. Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML. Journal of Network and Computer Applications, p.103332.
[2] Khanam, S., Ahmedy, I.B., Idris, M.Y.I., Jaward, M.H. and Sabri, A.Q.B.M., 2020. A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things. IEEE access, 8, pp.219709-219743.
[3] Doshi, R., Apthorpe, N. and Feamster, N. (2018) “Machine learning DDoS detection for consumer internet of things devices,” in Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018. doi:10.1109/SPW.2018.00013.
[4] Moustafa, N., Turnbull, B. and Choo, K.K.R., 2018. An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Internet of Things Journal, 6(3), pp.4815-4830.
[5] Shafiq, M. et al. (2020) “Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city,” Future Generation Computer Systems, 107. doi:10.1016/j.future.2020.02.017.
[6] Liang, C. et al. (2019) “Intrusion Detection System for Internet of Things based on a Machine Learning approach,” in Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019. doi:10.1109/ViTECoN.2019.8899448.
[7] Hady, A.A. et al. (2020) “Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study,” IEEE Access, 8. doi:10.1109/ACCESS.2020.3000421.

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Deposited14 Nov 2022
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