The past years have witnessed an increasing interest and concern regarding the development of security monitoring and management mechanisms for Critical Infrastructures, due to their vital role in ensuring the availability of many essential services. This task is not easy due to the specific characteristics of such systems, and the natural resistance of Critical Infrastructures operators against actions implying downtime. Digital Twins, as accurate virtual models of physical objects or processes, can provide a faithful environment for security analysis or evaluation of potential mitigation strategies to be deployed in face of specific situations. Nonetheless, their on-premises deployment can be expensive, implying a significant CAPEX whose return will depend on the ability to plan and deploy a suitable support infrastructure, as well as implementing efficient and scalable data collection and processing mechanisms capable of taking advantage of the acquired resources. This paper presents an off-premises approach to design and deploy Digital Twins to secure critical infrastructures, developed in the scope of the ELEGANT project. Such Digital Twins are built using real-time, high fidelity replicas of Programming Logic Controllers, coupled with scalable and efficient data collection processes, supporting the development and validation of Machine Learning models to mitigate security threats like Denial of Service attacks. The validation approach of ELEGANT, which leveraged from the capabilities of the Fed4Fire federated testbeds evaluated the feasibility of using cloudified Digital Twins, thus converting a significant part of the projected CAPEX for the in-premises model into on-demand, pay-as-you-go OPEX, eventually paving the way for the establishment of a DTaaS (Digital Twin as a Service) paradigm. The achieved results demonstrate that the data pipelines providing support for the ELEGANT Digital Twins have low impact in terms of resource usage in Denial of Service and Distributed Denial of Service attack scenarios, when higher volumes of data are generated.

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