A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a proactive manner. Our work follows the existing risk management practice and standard and considers risks from the stakeholder model, cyber, and physical system components along with their dependencies. The approach enables identification of critical CPS assets and assesses the impact of vulnerabilities that affect the assets. It also presents a cybersecurity attack scenario that incorporates a cascading effect of threats and vulnerabilities to the assets. The attack model helps to determine the appropriate risk levels and their corresponding mitigation process. We present a power grid system to illustrate the applicability of our work. The result suggests that risk in a CPS of a critical infrastructure depends mainly on cyber-physical attack scenarios and the context of the organization. The involved risks in the studied context are both from the technical and nontechnical aspects of the CPS.

Critical infrastructure (CI) is vital for the overall economic growth and its reliable and safe operation is essential for a nation's stability and people's safety. Proper operation of the assets is essential for such a system and any threats that could negatively impact the asset could have a severe disruption. Risk management is an important aspect of the protection of CI. There are several frameworks and methodologies for identifying assets, quantifying and analysing vulnerabilities. However, there is a lack of focus on the interdependencies among the assets and cascading effect of the inherent vulnerabilities on the asset. This study attempts to bridge that gap by presenting a novel asset focus risk management approach for the CI. It presents a systematic methodology for identifying and analysing critical assets, their potential vulnerabilities, threats and risks facing CI. This work taking into account cascading vulnerability impacts on assets leading to threats and causing risk. The authors use a running example from a smart grid system to demonstrate the usability of the approach. The result shows that some assets are prioritised and more vulnerable than other assets for the power grid system and it can severely impact on the overall business continuity.

The rise in sophisticated cyber threats demands advanced cybersecurity methods that surpass traditional rule-based approaches. This study explores the application of Artificial Intelligence (AI) to enhance predictive cybersecurity, enabling more accurate threat forecasting and effective vulnerability management. The research assesses various AI modelssuch as neural networks, decision trees, and Support Vector Machines (SVMs) in their ability to predict cyber threats. Employing a quantitative methodology, the study utilizes historical data from cybersecurity sources, threat intelligence feeds, vulnerability logs, and incident reports. Key performance metrics, including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), were used to test, validate, and train the AI models. Neural networks emerged as the most accurate, achieving 93% accuracy, particularly excelling in identifying phishing attacks and zero-day vulnerabilities. SVM models also performed well, minimizing false positives and increasing detection rates, while decision trees proved computationally efficient and easily interpretable in simpler cybersecurity scenarios. The findings underscore the superiority of AI models over traditional methods, offering dynamic solutions for evolving cyber threats. This research contributes to the field by demonstrating the extensive potential of AI in predictive cybersecurity, providing actionable insights for organizations implementing AI-driven threat detection and vulnerability management.

The main objective of reliability-centered maintenance is the cost-effectiveness of the maintenance strategy. These strategies, rather than the different components of reliability-centered maintenance being applied independently, are optimally integrated to take advantage of their respective strengths to optimize equipment reliability and life-cycle costs. The article uses reliability parameters to define the type of maintenance strategy and time to perform maintenance on gas compressors. This article presents a methodology using the gas compressor's reliability parameters to model reliability-centered maintenance procedure for the gas compressors. The approach is based on reliability parameters gotten from the liner regression carried out on the gas compressors. The shape parameter (β) from the Weibull linear regression shows that most components in the two gas compressors were experiencing early failure with their β < 1 and the distribution that best fits the data is the lognormal distribution, whose parameters are the shape parameter (σ') and the scale parameter (µ').