Search Results (9)

Background: The increasing prevalence of cyber threats in industrial cyber–physical systems (ICPSs) necessitates advanced solutions for threat detection and analysis. This research proposes a novel GPT-based framework for assessing cyber threats, leveraging artificial intelligence to process and analyze large-scale cyber event data. Methods: The framework integrates multiple components, including data ingestion, preprocessing, feature extraction, and analysis modules such as knowledge graph construction, clustering, and anomaly detection. It utilizes a hybrid methodology combining spectral residual transformation and Convolutional Neural Networks (CNNs) to identify anomalies in time-series cyber event data, alongside regression models for evaluating the significant factors associated with cyber events. Results: The system was evaluated using 9018 cyber-related events sourced from 44 global news portals. Performance metrics, including precision (0.999), recall (0.998), and F1-score (0.998), demonstrate the framework’s efficacy in accurately classifying and categorizing cyber events. Notably, anomaly detection identified six significant deviations during the monitored timeframe, starting from 25 September 2023 to 25 November 2024, with a sensitivity of 75%, revealing critical insights into unusual activity patterns. The fully deployed automated model also identified 11 correlated factors and five unique clusters associated with high-rated cyber incidents. Conclusions: This approach provides actionable intelligence for stakeholders by offering real-time monitoring, anomaly detection, and knowledge graph-based insights into cyber threats. The outcomes highlight the system’s potential to enhance ICPS security, supporting proactive threat management and resilience in increasingly complex industrial environments. Full article

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics. Full article

Nowadays, as the cyber-threat landscape is evolving and digital assets are proliferating and becoming more and more interconnected with the internet and heterogeneous devices, it is fundamental to be able to obtain a sensible measure of the security of devices, networks, and systems. Industrial cyber–physical systems (ICPSs), in particular, can be exposed to high operational risks that entail damage to revenues, assets, and even people. A way to overcome the open question of measuring security is with the use of security metrics. With metrics it is possible to rely on proven indicators that benchmark systems, identify vulnerabilities, and show practical data to assess the risk. However, security metrics are often proposed with specific contexts in mind, and a set of them specifically crafted for ICPSs is not explicitly available in the literature. For this reason, in this work, we analyze the current state of the art in the selection of security metrics and we propose a systematic methodology to gather, filter, and validate security metrics. Then, we apply the procedure to the ICPS domain, gathering almost 300 metrics from the literature, analyzing the domain to identify the properties useful to filter the metrics, and applying a validation framework to assess the validity of the filtered metrics, obtaining a final set capable of measuring the security of ICPSs from different perspectives. Full article

Anomaly detection has been proven to be an efficient way to detect malicious behaviour and cyberattacks in industrial cyber–physical systems (ICPSs). However, most detection models are not entirely adapted to the real world as they require intensive computational resources and labelled data and lack interpretability. This study investigated the traffic behaviour of a real coal mine system and proposed improved features to describe its operation pattern. Based on these features, this work combined the basic deterministic finite automaton (DFA) and normal distribution (ND) models to build an unsupervised anomaly detection model, which uses a hierarchical structure to pursue interpretability. To demonstrate its capability, this model was evaluated on real traffic and seven simulated attack types and further compared with nine state-of-the-art works. The evaluation and comparison results show that the proposed method achieved a 99% F1-score and is efficient in detecting sophisticated attacks. Furthermore, it achieved an average 17% increase in precision and a 12% increase in F1-Score compared to previous works. These results confirm the advantages of the proposed method. The work further suggests that future works should investigate operation pattern features rather than pursuing complex algorithms. Full article

Recent research studies on industrial cyber-physical systems (ICPSs) have witnessed vast patronage with emphasis on data utility for improved design, maintenance, and high-level decision making. The design of proton-exchange membrane fuel cells (PEMFC) is geared towards improving performance and extending life cycles. More often, material selection of PEMFC components contributes a major determining factor for efficiency and durability with the seal/gasket quality being one of the most critical components. Finite element analysis (FEA) offers a simulated alternative to real-life stress analysis of components and has been employed on different rubber-like gasket materials for hydrogen fuel cells for determining an optimal strain energy density function using different hyperelastic models following uniaxial tensile testing. The results show that the Mooney–Rivlin, Ogden, and Yeoh models were the most fitting model with the best stress–strain fit following a weighted error evaluation criteria which returned 18.54%, 19.31%, and 21.96% for 25% displacement, and 22.1%, 21.7%, and 21.17% for 40% displacements, respectively. Further empirical analysis using the multi-metric regression technique for compatibility testing (curve similarity) between the hyperelastic model outputs and the tensile data reveal that the Yeoh model is the most consistent as seen in the marginal error difference amidst increasing displacement while the Arruda–Boyce model is most inconsistent as shown in the high error margin as the displacement increases from 25% to 40%. Lastly, a comparative assessment between different rubber-like materials (RLM) was presented and is expected to contribute to improved decision-making and material selection. Full article

Circularity is clearly a competitive advantage and a market opportunity for European industries. From this perspective, while digitalization is largely recognized as an accelerator and an enabler of Circular Economy, the fact that European industry is strong but fragmented (highly specialized medium- and small-sized companies have different needs and different tools) naturally results in the proliferation of commercial platforms for digitalized manufacturing. If such fragmentation is not properly addressed, it will eventually become a threat to European competitiveness. Despite some examples, value networks still do not operate in a seamless, transparent, and effective way. This paper addresses the challenges and the resulting technical funding pillars for an IDS (International Data Space) manufacturing platform meant to empower a fully digital circular thread of products and services. Full article

Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain. Full article

This paper studies denial-of-services (DoS) attacks against industrial cyber-physical systems (ICPSs) for which we built a proper ICPS model and attack model. According to the impact of different attack rates on systems, instead of directly studying the time delay caused by the attacks some security zones are identified, which display how a DoS attack destroys the stable status of the ICPS. Research on security zone division is consistent with the fact that ICPSs’ communication devices actually have some capacity for large network traffic. The research on DoS attacks’ impacts on ICPSs by studying their operation conditions in different security zones is simplified further. Then, a detection method and a mimicry security switch strategy are proposed to defend against malicious DoS attacks and bring the ICPS under attack back to normal. Lastly, practical implementation experiments have been carried out to illustrate the effectiveness and efficiency of the method we propose. Full article

New paradigms such as the Industry 4.0, the Industrial Internet of Things (IIoT), or industrial cyber-physical systems (ICPSs) have been impacting the manufacturing environment in recent years. Nevertheless, these challenging concepts are also being faced from the educational field: Engineering students must acquire knowledge and skills under the view of these frameworks. This paper describes the utilization of an Industry 4.0-oriented flexible manufacturing system (FMS) as an educational tool to develop final projects (FPs) of engineering degrees. A number of scopes are covered by an FMS, such as automation, supervision, instrumentation, communications, and robotics. The utilization of an FMS with educational purposes started in the academic year 2011–2012 and still remains active. Here, the most illustrative FPs are expounded, and successful academic outcomes are reported. In addition, a set of initial considerations based on the experience acquired by the FP tutors is provided. Full article