# Building Cyber-Resilient Smart Grids with Digital Twins and Data Spaces
## 2 Digital Twins and Data Spaces
### Data Spaces
Our work distinctively combines digital twin technology with a data space to augment smart grid resilience, focusing on open-source technologies and grounding our framework in the Common Information Model (CIM), a prevalent standard in industrial settings. The adoption of a digital twin enables the correlation of information from diverse sources, which has been proven to be an effective approach for improving IDS performance [29]. Figure 6: Performance analysis of CRUD operations at varying levels of client concurrency. (**a**) Average latency for the ‘create’ operation. (**b**) Average latency for the ‘retrieve’ operation. (**c**) Average latency for the ‘update’ operation. (**d**) Average latency for the ‘delete’ operation. The architecture's reliance on open-source technology not only guarantees cost efficiency but also encourages updates and improvements driven by the community, ensuring that the system remains flexible and current with evolving trends and practices in cybersecurity. This approach, leveraging current technologies with specific capabilities, provides a general yet practical architecture, distinguishing our work from the existing literature in the field. ## 6 Conclusions and Future Work

In our pursuit to safeguard critical infrastructure against cyber threats, the architecture we have proposed stands as a testament to the efficacy of standardized data models aligned with the data space view. This alignment is not merely a technical detail but a strategic approach that ensures interoperability and scalability across various systems and operators within the energy sector. Our architecture serves as a step towards the creation of a suite of services specifically designed to bolster the cybersecurity position of Electric Power and Energy System (EPES) operators. By adhering to standardized data models, we facilitate a seamless integration of diverse systems and enable a unified response to cyber threats. This standardization is crucial for EPES operators as it lays the groundwork for a collaborative defence strategy, enhancing the overall resilience of critical infrastructures. The implementation of our architecture within the smart grid has underscored the importance of real-time monitoring and rapid response capabilities. It has proven to be a viable solution that not only detects and communicates anomalies but also adheres to the principles of a data space, where information sharing and system interoperability are key. The open-source nature of the proposed architecture not only ensures cost-effectiveness but also fosters community-driven enhancements and updates, keeping the system agile and up-to-date with the latest cybersecurity trends and practices. Looking ahead, our commitment is to broaden the reach and enhance the functionality of our architectural framework. In fact, system monitoring presents some drawbacks. Without recognising the state of the system, the architecture might be limited in its ability to conduct comprehensive behavioural analyses. State-based monitoring enables the identification of anomalous behaviours that could indicate a cybersecurity threat. Moreover, incident management is also needed in the approach and must be integrated into our proposal. Hence, our future work will focus on the creation of an extensive service catalogue, which will encompass a range of cybersecurity services. This catalogue will serve as a resource for Electric Power and Energy System (EPES) operators, offering them ready-to-implement solutions that adhere to a uniform standard for threat identification and mitigation. Simultaneously, we intend to cultivate a cooperative environment dedicated to cybersecurity. This will be a space where EPES operators can exchange knowledge, operational best practices, and actionable threat intelligence. By capitalizing on the data space model, this collaborative platform will significantly contribute to the collective fortification of security measures across the sector. Conceptualization, L.C., R.N. and L.R. ; methodology, R.N. ; software, A.P.