New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the purpose of reaching optimum points of operation. These aspects promote the Smart Factory paradigm, integrating physical and digital systems to create smarts products and processes capable of transforming conventional value chains, forming the Cyber-Physical Systems (CPSs). This flexibility opens a large gap that affects the security of control systems since the new communication links can be used by people to generate attacks that produce risk in these applications. This is a recent problem in the control systems, which originally were centralized and later were implemented as interconnected systems through isolated networks. To protect these systems, strategies that have presented acceptable results in other environments, such as office environments, have been chosen. However, the characteristics of these applications are not the same, and the results achieved are not as expected. This problem has motivated several efforts in order to contribute from different approaches to increase the security of control systems. Based on the above, this work proposes an architecture based on artificial neural networks for detection and isolation of cyber attacks Denial of Service (DoS) and integrity in CPS. Simulation results of two test benches, the Secure Water Treatment (SWaT) dataset, and a tanks system, show the effectiveness of the proposal. Regarding the SWaT dataset, the scores obtained from the recall and F1 score metrics was $0.95$ and was higher than other reported works, while, in terms of precision and accuracy, it obtained a score of $0.95$ which is close to other proposed methods. With respect to the interconnected tank system, scores of $0.96,0.83,0.81$, and $0.83$ were obtained for the accuracy, precision, F1 score, and recall metrics, respectively. The high true negatives rate in both cases is noteworthy. In general terms, the proposal has a high effectiveness in detecting and locating the proposed attacks.

Modern industrial automation supported by Cyber–Physical Systems (CPSs) requires high flexibility, which is achieved through increased interconnection between modules. This interconnection introduces a layer of symmetry into the design and operation of CPSs, balancing the distribution of tasks and resources across the system and streamlining the flow of information. However, this adaptability also exposes control systems to security threats, particularly through novel communication links that are vulnerable to cyberattacks. Traditional strategies may have limitations in these applications. This research proposes a design approach for control applications supported by CPSs that incorporates cyberattack detection and tolerance strategies. Using a modular and adaptive approach, the system is partitioned into microservices for scalability and resilience, allowing structural symmetry to be maintained. Schedulability assessments ensure that critical timing constraints are met, improving overall system symmetry and performance. Advanced cyberattack detection and isolation systems generate alarms and facilitate rapid response with replicas of affected components. These replicas enable the system to recover from and tolerate cyberattacks, maintaining uninterrupted operation and preserving the balanced structure of the system. In conclusion, the proposed approach addresses the security challenges in CPS-based control applications and provides an integrated and robust approach to protect industrial automation systems from cyber threats. A case study conducted at a juice production facility in Colima, México, demonstrated how the architecture can be applied to complex processes such as pH control, from simulation to industrial implementation. The study highlighted a plug-and-play approach, starting with component definitions and relationships, and extending to technology integration, thereby reinforcing symmetry and efficiency within the system.

Modern industries require constant adaptation to new trends. Thus, they seek greater flexibility and agility to cope with disruptions, as well as to solve needs or meet the demand for growth. Therefore, smart industrial applications require a lot of flexibility to be able to react more quickly to continuous market changes, offer more personalized products, increase operational efficiency, and achieve optimum operating points that integrate the entire value chain of a process. This requires the capture of new data that are subsequently processed at different levels of the hierarchy of automation processes, with requirements and technologies according to each level. The result is a new challenge related to the addition of new functionalities in the processes and the interoperability between them. This paper proposes a distributed computational component-based framework that integrates communication, computation, and storage resources and real-time capabilities through container technology, microservices, and the publish/subscribe paradigm, as well as contributing to the development and implementation of industrial automation applications by bridging the gap between generic architectures and physical realizations. The main idea is to enable plug-and-play software components, from predefined components with their interrelationships, to achieve industrial applications without losing or degrading the robustness from previous developments. This paper presents the process of design and implementation with the proposed framework through the implementation of a complex pH control process, ranging from the simulation part to its scaling and implementation to an industrial level, showing the plug-and-play assembly from a definition of components with their relationships to the implementation process with the respective technologies involved. The effectiveness of the proposed framework was experimentally verified in a real production process, showing that the results scaled to an industrial scale comply with the simulated design process. A qualitative comparison with traditional industrial implementations, based on the implementation requirements, was carried out. The implementation was developed in the beverage production plant “Punta Delicia”, located in Colima, Mexico. Finally, the results showed that the platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the pH control.