In recent years, the Industry 4.0 paradigm has accelerated the digitalization process of the industry, and it slowly diminishes the line between information technologies (IT) and operational technologies (OT). Among the advantages, this brings up the convergence issue between IT and OT, especially in the cybersecurity-related topics, including new attack vectors, threats, security imperfections, and much more. This cause raised new topics for methods focused on protecting the industrial infrastructure, including monitoring and detection systems, which should help overcome these new challenges. However, those methods require high quality and a large number of datasets with different conditions to adapt to the specific systems effectively. Unfortunately, revealing field factory setups and infrastructure would be costly and challenging due to the privacy and sensitivity causes. From the lack of data emerges the new topic of industrial testbeds, including sub-real physical laboratory environments, virtual factories, honeynets, honeypots, and other areas, which helps to deliver sufficient datasets for mentioned research and development. This paper summarizes related works in the area of industrial testbeds. Moreover, it describes best practices and lessons learned for assembling physical, simulated, virtual, and hybrid testbeds. Additionally, a comparison of the essential parameters of those testbeds is presented. Finally, the findings and provided information reveal research and development challenges, which must be surpassed.

Our test environment incorporates a unique blend of physical, emulated, and virtualized components, spanning from electrical substations to SCADA systems, thereby offering a versatile platform for testing against cyber threats, facilitating educational programs, and supporting advanced traffic simulation. Motivated by the increasing digitalization of energy networks and the growing risks associated with cybersecurity and crisis management, this work responds to geopolitical situations and a series of attacks on electricity infrastructure, such as in Ukraine between 2015 and 2024. For instance, in 2015 and 2021, major cyberattacks caused power outages in Ukraine and the USA, emphasizing the urgent requirement for robust testing platforms. Key findings from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities, enhancing cybersecurity measures, and providing valuable hands-on learning experiences. The integration of such diverse components not only exemplifies a significant step forward in testbed design but also showcases its potential in fostering innovation and security in the power sector. Through detailed comparisons with existing testbeds, we underscore our testbed’s distinct features and its contribution to bridging the gap in current methodologies, setting a new benchmark for future developments in smart grid testing and education.

The manufacturing and critical infrastructure world is increasingly dependent on cyber security. Cyber-attacks are becoming increasingly sophisticated, targeting traditional Information Technology (IT) and Operational Technology (OT) systems mainly due to the convergence of these infrastructures. Cyber-range assists with educating professionals and stakeholders about current trends and techniques by simulating realistic scenarios. This paper presents a scenario using the BUTCA (Brno University of Technology Cyber Arena) platform, developed at the Department of Telecommunications with a focus on IT and OT. The presented solution provides an effective and entertaining form of education using gamification aimed at a sector of interested students from primary school students to university students, professionals and interested members of the general public. As a proof of concept, we have developed a game (within BUTCA) targeting an industrial sector. Where the main objective of the cyber game is to demonstrate the potential impact of a successful cyber attack caused by human error through a phishing attack.

The creation of suitable and efficient tools for anomaly detection constitutes a crucial aspect of security, applicable not only to industrial networks but also to cyber-physical systems. This article elucidates a framework designed to automate the selection of an optimal deep neural network architecture, thereby expediting the creation and implementation of neural network-based tools. The framework presented here enables a rapid design of an Artificial Neural Network structure without necessitating user intervention. Its efficacy has been showcased through experimentation with the publicly accessible HAI dataset, yielding an accuracy of approximately 0.94 after 10 epochs. Subsequently, a second scenario was performed where a total of 5456 models were generated and trained, with an average time of approximately 9.95 seconds per model.