One has to have a holistic picture based on digital twins for involved processes. The digital twins shall become “eyes” to contemplate the situation from various perspectives and thus rapidly detect anomalies. Such a vision raises many research questions. In the D-CRISP project, we focus on three main complementary directions:
- Antagonistic threats representation and formal validation: How to model a digital twin in a context of antagonistic threats (cyber and/or physical) and how to formally validate it (to allow automated warnings for human decision makers)?
- Digital twins cross-domain integration for revealing antagonistic threats: How to seamlessly integrate digital twins? What roadmaps [4] can be suggested for a cross-domain integration of digital twins in mitigation of antagonistic threats?
- Humans in preventing and detecting antagonistic threats: How to bring together operating digital twins and engage human stakeholders to report and to receive information about potential or developing threats or breaches? How to identify the roles and prepare humans for those roles at the “area being under potential antagonistic threat”? How to respect the privacy of involved human stakeholders?
In the modern, data- and software-intensive world, in order to maintain further responsible and responsive development of our societies, the D-CRISP project proposes a solution based on intensive, real-time use of digital models that come from different problem domains helping to detect anomalies and prevent catastrophic outcomes. In order to stay ahead of perpetrators, the D-CRISP project suggests learning from different disciplines via corresponding threats-aware digital twins and mitigating risks through the Integrated Antagonistic Threats Prevention Digital Twin.
