Incorporating gamification elements and innovative approaches in training programs are promising for addressing cybersecurity knowledge gaps. Cybersecurity education should combine hard and soft skill development when building the capacity to manage cyber incidents requiring timely communication, team collaboration, and self-efficacy in risk assessment and incident mitigation. The paper presents a design and evaluation of the technology-enhanced cybersecurity education approach CAPE which works as a hybrid escape room. It combines a virtualized infrastructure simulating the business environment and the physical environment where game participants play the role of the incident response team at the organization’s premises. The CAPE could be applied as a student-centered approach in the educational environment. The work employed a multiple-methods research approach to design a gamified approach and investigate crisis communication, collaboration, self-regulation, and technical competences in incident management scenarios. The design science methodology empowered the game’s construction with an attack surface covering physical and digital security. The undergraduate students participated in the pilot execution and completed the psychological questionnaires. The results were analyzed using statistical methods. Results of the CAPE execution showed a positive impact on student performance and increased interest in cybersecurity. The designed approach promoted field-specific competence development. The results demonstrated the significance of psychological aspects related to incident management. Graphical abstract

Incorporating gamification elements and innovative approaches in training and educational programs are promising for addressing cybersecurity knowledge gaps. Cybersecurity training should consider a combination of hard and soft skills to deal with the diversity of cyber incidents. Therefore, this research aims to investigate if soft skills such as communication and collaboration enhances students’ performance in practical task execution and if the CyberEscape approach promotes students engagement and self-efficacy.This paper presents a cybersecurity game CyberEscape based on the intervention mapping methodology previously defined in the research. A virtualised infrastructure simulating the business environment works as a hybrid escape room. Physical resources and prepared information materials complement the game to support the scenario and ensure student engagement. The work employs a multiple-methods research approach. Participants filled out questionnaires in the pre-event and post-execution phases. Additionally, the participants were involved in small group semi-structured interviews. Results of the pilot study show a positive impact on student competence improvement and increased interest in cybersecurity.

The following topics are dealt with: security of data; decision making; data analysis; statistical analysis; information systems; data visualisation; decision trees; learning (artificial intelligence); pattern classification; and neural nets.

The aim of the study is to train a machine learning (ML) model for drinking water contamination detection and compare performance to statistical methods and existing anomaly detection solutions. A pilot drinking water supply system was made and equipped with drinking water quality sensors and a contamination dosing system. The results from this study demonstrated that using the statistical Mahalanobis distance (MD) method to predict the classification of drinking water measurements yields a 99% accuracy, 23% precision, and 28% F-score result (for wastewater contamination); however, the ML model yields a 99% accuracy, 98% precision, and a 98% F-score result. The results show that the application of ML methods can improve drinking water contamination detection speed and accuracy.

Graphical abstract showing potential contamination sources in drinking water, microbiological analysis methods, the aim of the study to compare culture-based methods and FCM and main conclusions. This study evaluates flow cytometry and culture-based indicators for detecting and tracking low-concentration contamination in a biologically stable, low disinfection dose setting. Traditionally, culture-based methods have been shown to discover only a very small fraction of bacteria; however, flow cytometry has been shown to be effective in rapid detection. In the pilot-scale study, flow cytometry intact cell counts rose fourfold compared with pre-contamination levels during contamination dosing, while heterotrophic plate count at 22 and 37 °C increased threefold. Wastewater produced the strongest responses, with median intact cell counts of approximately 1.9 × 106 cells/ml, exceeding those of other classes. In 20% of experiments, post-event flow cytometry (FCM) intact cell counts dropped immediately below pre-event levels, whereas Escherichia coli and coliform counts remained elevated for a longer period after dosing ceased, indicating persistent contamination not captured by FCM alone. Disinfection after contamination events reduced detections of faecal indicators in subsequent uncontaminated water samples. This supports the role of culture-based methods for detecting persistent contamination. The authors conclude that FCM is optimal for rapid evaluation of water quality, whereas culture-based methods are superior for confirming contamination persistence, while the combined use of both methods reduces blind spots and supports operational decision-making.