Digital Twins for Enhancing Cybersecurity of Community Health System

Interdisciplinary Areas: Data and Engineering Applications, Security and Privacy, Others

Project Description

Healthcare data breach has been an increasing concern as the data could be misused and even threaten human lives. The objective of this research is to leverage advanced cybersecurity techniques to ensure the privacy, security, and integrity of different healthcare data collected in community health systems while utilizing the power of digital twins to provide real-time insights, predictive analytics, and data-informed decision support for care delivery in community settings.
This research uses digital twins as a platform to investigate cybersecurity in community healthcare systems by incorporating encryption, access controls, secure data storage, and privacy-preserving techniques. This will enable the protection of sensitive healthcare data, prevent cyberattacks, and mitigate the risk of data breaches.
Meanwhile, the digital twin architecture could be designed to capture the complexity of community health systems, integrate diverse healthcare data sources, leverage advanced data analytics and AI/machine learning algorithms, and test and evaluate cyberattack scenarios without disrupting the current process. The platform will facilitate real-time monitoring, risk prediction, and personalized interventions, ensuring community healthcare is data-driven and secure.
A digital twin platform created from this project will serve as a cybersecurity testbed and would empower stakeholders, including healthcare providers, policymakers, and community leaders, to make evidence-based decisions, optimize resource allocation, and improve healthcare outcomes.


Start Date

After Gilbreth postdoc Fellows are announced


Postdoc Qualifications

- Doctoral Degree: Obtain a Ph.D. in a relevant field such as computer science, cybersecurity, information security, computer engineering, or a related discipline.
- Research Experience: Prior research experience in cybersecurity, digital twin technology, community healthcare, or related areas is highly valuable. This can include publishing papers in reputable conferences or journals, contributing to open-source projects, or participating in collaborative research efforts.
- Technical Skills: Have proficiency in various technical areas, including but not limited to:
+ Cybersecurity: Familiarity with network security, cryptography, vulnerability analysis, penetration testing, secure coding practices, and incident response.
+ Digital Twin Technology: Understanding of data analytics, modeling and simulation techniques, data integration, cloud computing, and Internet of Things (IoT) concepts.
- Programming and Software Development: Possess strong programming skills in languages such as Python, C/C++, Java, or scripting languages commonly used in cybersecurity research. Additionally, experience with software development methodologies and tools will be an advantage.
- Knowledge of Security Standards and Frameworks: Having knowledge of relevant security standards and frameworks, such as NIST Cybersecurity Framework, ISO/IEC 27001, Common Vulnerability Scoring System (CVSS), and others.
- Machine Learning and Data Analytics: The ideal candidate should have background in machine learning algorithms, data mining techniques, statistical analysis, and visualization tools.
- Communication and Collaboration: Strong written and verbal communication skills are essential for presenting research findings, writing grant proposals, and collaborating with other researchers and industry professionals. Effective teamwork and project management abilities are also valuable.
- Industry Experience: While not mandatory, having practical experience in the industry, such as building a digital twin, setting up a cybersecurity system, or a relevant work experience is an advantage. 



Tho Le, PhD. Assistant Professor of Industrial Engineering Technology. School of Engineering Technology.

Nan Kong, PhD. Professor and Interim Head. Weldon School of Biomedical Engineering.


Short Bibliography

1. Faleiro, R., Pan, L., Pokhrel, S. R., & Doss, R. (2022). Digital twin for cybersecurity: Towards enhancing cyber resilience. In Broadband Communications, Networks, and Systems: 12th EAI International Conference, BROADNETS 2021, Virtual Event, October 28–29, 2021, Proceedings 12 (pp. 57-76). Springer International Publishing.
2. De Benedictis, A., Esposito, C., & Somma, A. (2022, September). Toward the adoption of secure cyber digital twins to enhance cyber-physical systems security. In International Conference on the Quality of Information and Communications Technology (pp. 307-321). Cham: Springer International Publishing.
3. Jimenez, J. I., Jahankhani, H., & Kendzierskyj, S. (2020). Health care in the cyberspace: Medical cyber-physical system and digital twin challenges. Digital twin technologies and smart cities, 79-92.
4. Pokhrel, A., Katta, V., & Colomo-Palacios, R. (2020, June). Digital twin for cybersecurity incident prediction: A multivocal literature review. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (pp. 671-678).
5. Mora, A. C., Nadjm-Tehrani, S., Weippl, E., & Eckhart, M. (2022). Digital twins for cyber-physical systems security (Dagstuhl Seminar 22171). In Dagstuhl Reports (Vol. 12, No. 4). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.