SADECEI-4.0 aims to automate all the analysis, detection and evaluation processes of potential cyber-attacks against industrial environments such as Advanced Persistent Threats (APTs), explaining the origen of a problem and tracing the stealthy threat sequence over time. To do this, the project deals with combining several technologies, such as distributed consensus algorithms (e.g., Opinion Dynamics or other related ones), machine-learning/data mining and blockchain, to detect and delimit in time the malicious actions. This also means that this analysis does not only involve the advanced management of events originated in the present but also events generated in the past and in the future to anticipate and respond in time, illustrating the traceability of such actions through heat maps, states and health indicators.
To make viable the SADECEI-4.0 development for manufacturing systems and test it through the new industrial virtualization paradigm, a Digital Twin will be designed and implemented. Through this Digital Twin, we will explore a way to model and simulate the construction and assemble of critical monitoring components such as Programable Logical Controllers (PLCs) and Remote Terminal Unit (RTUs). These components are in charge of controlling sub-areas of a specific critical environment, collecting sensor information and redirecting commands from the central monitoring system to actuators. The learned lessons from this project will certainly be keys to determine the benefits and the real deployment of the new virtualization mechanisms for Industry 4.0 and related critical application scenarios.
Project supported by a 2019 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation.
With the collaboration of: