Digital Twin offers significant advantages to cybersecurity experts, empowering them to predict risks and anomalies without entering the physical world and to simulate and test cyber-attacks that would otherwise be infeasible to do in real-time physical environments [1-3]. Therefore, there is a need to develop novel and automated methodologies for enhancing anomaly detection potential in IoT-based healthcare using DT technology. In [4], we have developed a DT-based cybersecurity framework for IoT-based healthcare applications, which includes contextual computing and simulation technologies for healthcare to forecast and mitigate security threats in real-time. In this master thesis, some of the components of the framework will be implemented and tested for anomaly detection in the healthcare sector.
Tasks:
T1: To study related literature review and prepare a short report
T2: To implement and test the selected components of the framework in [4]
T3: To prepare a report based on the results from implementation and testing
Reference
[1] Zhang, J., Li, L., Lin, G., Fang, D., Tai, Y., & Huang, J. (2020). Cyber Resilience in Healthcare Digital Twin on Lung Cancer. IEEE Access, 8, 201900-201913.
[2] Mittal, S., Tolk, A., Pyles, A., Van Balen, N., & Bergollo, K. (2019, December). Digital twin modelling, co-simulation and cyber use-case inclusion methodology for IoT systems. In 2019 Winter Simulation Conference (WSC) (pp. 2653-2664). IEEE.
[3] Pirbhulal, Sandeep, Habtamu Abie, Ankur Shukla, and Basel Katt. "A Cognitive Digital Twin Architecture for Cybersecurity in IoT-Based Smart Homes." In International Conference on Sensing Technology, pp. 63-70. Cham: Springer Nature Switzerland, 2022.
[4] Pirbhulal, S., Abie, H., & Shukla, A. (2022, June). Towards a Novel Framework for Reinforcing Cybersecurity using Digital Twins in IoT-based Healthcare Applications. In 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring) (pp. 1-5). IEEE.
The project is in collaboration with Norsk Regnesentral (Norwegian Computing Center)
