RESIST project involves the usage of the System-Theoretic principles such as STAMP (System Theoretic Accident Model and Processes) model developed at MIT (Massachusetts Institute of Technology) in Boston, USA. STAMP model is a systemic model that allows the mapping of socio-technical systems by focusing on the system elements and their relationships among them. 

The STAMP model will be used to map the system agents and their symbiotic  relationships and mutual dependencies in the context of the operations involved in an industrial Cyber-Socio-Technical Systems (CSTS) in complex scenarios.

For instances, a simplified model with only two agents is shown in the figure, while reference is made to the following studies for further information on the topic:

• The past and present of System-Theoretic Accident Model And Processes (STAMP) and its associated techniques: A scoping review

• A STAMP Model for Safety Analysis in Industrial Plants

• A New Accident Model for Engineering Safer Systems

The use of a Digital Twin can be extremely useful in improving the resilience of an industrial plant in the event of cyber-attacks, as required by the RESIST project in terms of monitoring, simulating, and analyzing the system's behaviors.

A DT enables continuous monitoring of plant operations. In the event of a cyber attack, the system can detect anomalies or suspicious behavior's in real-time, allowing for an immediate response. Additionally, it is possible to conduct simulations of cyber-attacks to assess the vulnerability of the system. This allows operators to identify and rectify any security weaknesses before they can be exploited by real attackers, integrating predictive analysis and machine learning capabilities to identify patterns, predict failures, and improve operational management. It can be used to quickly isolate compromised parts of the plant and implement mitigation measures to limit damage.

Examples of industrial applications of these technologies, not yet tailored to risk assessment and (cyber)-attacks as in RESIST, are:

• An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

• Intelligent Retrofitting Paradigm for Conventional Machines towards the Digital Triplet Hierarchy

• The Digital Twin Realization of an Ejector for Multiphase Flows

RESIST project considers the human operator in the CSTS including it within a two-fold bidirectional flow: on one side, the human activities are tracked to have knowledge of the behaviour, actions, and impact on the industrial system. On the other side, the human operator is supported in its interaction with the external environment to increase its effectiveness, reducing errors and losses of time and resources, to increase the overall resilience of the CSTS.

Motion capture solutions support the former goal, while augmented reality devices, worn by the human operator during its action in the CSTS, increase the informative dimensions and knowledge.

Examples of industrial applications of these technologies, not yet tailored to risk assessment and (cyber)-attacks as in RESIST, are:

• Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes - ScienceDirect

• Applied Sciences | Free Full-Text | Interactions in Augmented and Mixed Reality: An Overview (mdpi.com)

• Multi-objective assembly line balancing considering component picking and ergonomic risk - ScienceDirect