The scope of the THREAT-ARREST project includes cyber security training and deployment of cyber-ranges. Therefore, we develop a training platform for advance training on virtual systems (representations of the pilot environments). The goal is not just the training itself, but to assess if the training affects the security level of the actual system afterwards, thus, if the trainees really apply what they learn in the piloting environment. The whole THREAT-ARREST platform operation is modelled in a formalism, called CTTP, and all the underlying functionality (e.g. creation of virtual labs, automated assessment of the trainee, etc.) is driven by the deployed CTTP models. The final goal is the composition of continuous assurance on the whole setting, where the training will be adapted continuously in order to increase the security status of the pilots up to a designated protection level.
For the successful evaluation of the platform, we also demonstrate its application and training capabilities for the three piloting sectors of smart energy, healthcare, and smart shipping. Following the initial analysis of the pilots, we designed 13 main CTTP driven scenarios.The scenarios cover the training for all the defined actuator types (e.g. simple users, administrators, security experts, etc.), the main security properties (e.g. confidentiality, integrity, availability) and key data states (i.e. data in-transit, at-rest, and in-processing), as well as the physical and software components of cyber systems. Moreover, the expected actions for the trainees include, among others, preparedness, detection and analysis, security incident response and post security incident response. For the first integrated version of the platform, we have implemented 3 full demonstrators, one indicative scenario for each pilot.
Once deployed in the platform, the main scenarios can be applied in the other pilots as well or cover different actuators and security properties, by slightly configuring the CTTP model and tailoring it to the examined use case. Thus, after the initial development of the main models, the generation of new scenarios can be increased exponentially.