A Novel Zero-Trust Machine Learning Green Architecture for Healthcare IoT Cybersecurity: Review, Analysis, and Implementation (2401.07368v1)
Abstract: The integration of Internet of Things (IoT) devices in healthcare applications has revolutionized patient care, monitoring, and data management. The Global IoT in Healthcare Market value is $252.2 Billion in 2023. However, the rapid involvement of these devices brings information security concerns that pose critical threats to patient privacy and the integrity of healthcare data. This paper introduces a novel ML based architecture explicitly designed to address and mitigate security vulnerabilities in IoT devices within healthcare applications. By leveraging advanced convolution ML architecture, the proposed architecture aims to proactively monitor and detect potential threats, ensuring the confidentiality and integrity of sensitive healthcare information while minimizing the cost and increasing the portability specialized for healthcare and emergency environments. The experimental results underscore the accuracy of up to 93.6% for predicting various attacks based on the results demonstrate a zero-day detection accuracy simulated using the CICIoT2023 dataset and reduces the cost by a factor of x10. The significance of our approach is in fortifying the security posture of IoT devices and maintaining a robust implementation of trustful healthcare systems.
- P. V. Shevchenko, J. Jang, M. Malavasi, G. W. Peters, G. Sofronov, and S. Trück, “The nature of losses from cyber-related events: risk categories and business sectors,” Journal of Cybersecurity, vol. 9, no. 1, 2023.
- A. A. Mahdavifar Samaneh, Ghorbani, “The nature of losses from cyber-related events: risk categories and business sectors,” Journal of Cybersecurity, vol. 32, no. 18, 2020.
- M. H. Kashani, M. Madanipour, M. Nikravan, P. Asghari, and E. Mahdipour, “A systematic review of iot in healthcare: Applications, techniques, and trends,” Journal of Network and Computer Applications, vol. 192, p. 103164, 2021.
- J. A. Stankovic, “”real-time operating systems,” pp. 65–82, 1994.
- A. Ghosh, R. Ratasuk, B. Mondal, N. Mangalvedhe, and T. Thomas, “Lte-advanced: next-generation wireless broadband technology,” IEEE wireless communications, vol. 17, no. 3, pp. 10–22, 2010.
- S. Alasmari and M. Anwar, “Security & privacy challenges in iot-based health cloud,” in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 198–201, IEEE, 2016.
- “Landscape of iot security,” Computer Science Review, vol. 44, p. 100467, 2022.
- K. Darshan and K. Anandakumar, “A comprehensive review on usage of internet of things (iot) in healthcare system,” in 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 132–136, IEEE, 2015.
- M. Wazid, A. K. Das, V. Chamola, and Y. Park, “Uniting cyber security and machine learning: Advantages, challenges and future research,” ICT Express, vol. 8, no. 3, pp. 313–321, 2022.
- S. K. Jagatheesaperumal, S. Rajkumar, J. V. Suresh, A. H. Gumaei, N. Alhakbani, M. Z. Uddin, and M. M. Hassan, “An iot-based framework for personalized health assessment and recommendations using machine learning,” Mathematics, vol. 11, no. 12, 2023.
- Y. Guo, “A review of machine learning-based zero-day attack detection: Challenges and future directions,” Computer Communications, vol. 198, pp. 175–185, 2023.
- S. Huda, S. Miah, M. Mehedi Hassan, R. Islam, J. Yearwood, M. Alrubaian, and A. Almogren, “Defending unknown attacks on cyber-physical systems by semi-supervised approach and available unlabeled data,” Information Sciences, vol. 379, pp. 211–228, 2017.
- H. Hindy, R. Atkinson, C. Tachtatzis, J.-N. Colin, E. Bayne, and X. Bellekens, “Utilising deep learning techniques for effective zero-day attack detection,” Electronics, vol. 9, no. 10, 2020.
- Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, “Kitsune: An ensemble of autoencoders for online network intrusion detection,” ArXiv, vol. abs/1802.09089, 2018.
- A.-S. Poncette, L. Mosch, L. Stablo, C. Spies, M. Schieler, S. Weber-Carstens, M. Feufel, and F. Balzer, “Human-centered design with usability evaluation of a remote patient monitoring system for intensive care medicine: A mixed-methods study (preprint),” JMIR Human Factors, vol. 9, 2021.
- F. Lamonaca, E. Balestrieri, I. Tudosa, F. Picariello, D. L. Carnì, C. Scuro, F. Bonavolontà, V. Spagnuolo, G. Grimaldi, and A. Colaprico, “An overview on internet of medical things in blood pressure monitoring,” in 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6, 2019.
- M. Waleed, T. Kamal, T.-W. Um, A. Hafeez, B. Habib, and K. E. Skouby, “Unlocking insights in iot-based patient monitoring: Methods for encompassing large-data challenges,” Sensors, vol. 23, no. 15, 2023.
- S. Nasiri, F. Sadoughi, M. H. Tadayon, and A. Dehnad, “Security requirements of internet of things-based healthcare system: a survey study,” Acta Informatica Medica, vol. 27, pp. 253–258, 12 2019.
- V. O. Nyangaresi, “Privacy preserving three-factor authentication protocol for secure message forwarding in wireless body area networks,” Ad Hoc Networks, vol. 142, p. 103117, 2023.
- P. Li, Y. Pei, and J. Li, “A comprehensive survey on design and application of autoencoder in deep learning,” Applied Soft Computing, vol. 138, p. 110176, 2023.
- J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of machine learning research, vol. 13, no. 2, 2012.
- A. T. A. E. . D. K. Peppes, N., “Effectiveness of zero-day attacks data samples generated via gans on deep learning classifiers,” Sensors (Basel, Switzerland), vol. 23, no. 2, 2023.
- e. a. Gharib, Mohammed, “Autoids: auto-encoder based method for intrusion detection system,” arXiv preprint arXiv:1911.03306, 2019.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.