System for Analysis, Detection and Evaluation of Cyber-Attacks in Industry 4.0 Environments
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 the development of SADECEI-4.0 for manufacturing systems viable and to test it through the new industrial virtualization paradigm, a Digital Twin is designed and implemented. Through this Digital Twin, we explore a way to model and simulate the construction and assembly of critical monitoring components such as Programmable Logic Controllers (PLCs) and Remote Terminal Units (RTUs). These components are responsible for controlling sub-areas of a specific critical environment by gathering information from sensors and redirecting commands from the central monitoring system to the actuators. What is more, the lessons learned from this project have undoubtedly been essential in helping us understand the benefits of this new corresponding virtual conceptualisation of Industry 4.0/5.0, but also the security risks that can threaten the proper functioning of certain critical scenarios.
Project supported by a 2019 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation.
With the collaboration of:
- (2022): Digital Twin: A Comprehensive Survey of Security Threats. In: IEEE Communications Surveys & Tutorials, vol. 24, no. thirdquarter 2022, pp. 1475 - 1503, 2022, ISSN: 1553-877X.