Search Results (188)

Industry 4.0 has produced one of the fastest-growing bodies of engineering and management research; much of this output remains siloed by technology domain. This study addresses that fragmentation through a structured critical review (a review-of-reviews), synthesising 70 peer-reviewed review articles and foundational sources (2003–2026) spanning 14 technology domains. The review introduces the I4.0-STS framework, an original four-layer structure organising evidence across physical, cyber, cognitive, and socio-organisational dimensions. Five principal findings emerge. The physical and cyber layers show consistent evidence of maturity. Industry-reported lighthouse IIoT deployments show 20–30% energy and up to 39% lead-time reductions. AI-driven predictive maintenance shows 30–50% unplanned-downtime reductions. The cognitive layer (LLM-augmented digital twins and generative AI interfaces) is technically feasible but outpaces its governance frameworks. Cybersecurity remains insufficiently governed, with documented ransomware incidents in manufacturing OT environments underscoring the risks of OT–IT convergence. SME adoption and developing-economy manufacturing transformation remain comparatively under-addressed. Finally, 12 research gaps are assessed as of June 2026, five rated Open, with future research directions proposed for each, framed against the emerging Industry 5.0 agenda. All findings are second-order interpretations from the source reviews, and their limitations are stated explicitly. Full article

Background: Generative artificial intelligence (GenAI) has attracted significant attention in supply chain management (SCM) due to its potential to improve data-driven decision-making and operational performance. However, existing studies mainly focus on individual GenAI models or specific supply chain applications, lacking a comprehensive understanding of how different GenAI architectures support decision-making across the supply chain. Methods: This study conducts a systematic literature review using the PRISMA framework to examine the applications of Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and flow-based models within a six-level supply chain decision-making framework. A total of 692 peer-reviewed publications were analyzed using bibliometric methods, including keyword co-occurrence, temporal and density analyses, and Supervised Embedding Visualization. Results: Current research is concentrated on Transformer and GAN applications, particularly in data analytics, optimization, forecasting, manufacturing, transportation, logistics, and quality management. The analyses also reveal major research themes, the evolution of GenAI in SCM, and limited attention to sustainability, cybersecurity, resilience, and reverse logistics. Conclusions: This study provides a comprehensive overview of GenAI applications in SCM, identifies key research gaps, and offers a foundation for future research while helping practitioners evaluate opportunities and limitations of GenAI for supply chain decision-making. Full article

The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud–edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols. Full article

The increasing digitization of healthcare records and growing reliance on interconnected systems have increased hospitals’ exposure to cyber threats, including ransomware, brute-force attacks, and Structured Query Language (SQL) injection. Traditional cybersecurity approaches alone are often insufficient to address these threats, prompting growing interest in artificial intelligence (AI)-based decision-support tools. This paper evaluates the potential of ChatGPT for hospital cybersecurity and incident response while introducing a structured qualitative framework for evaluating Large Language Model (LLM)-generated cybersecurity recommendations in healthcare. Through three progressively designed experiments and a ransomware case study, we evaluate ChatGPT’s role in developing a hospital cybersecurity plan, detecting brute-force login attempts, responding to an SQL injection attack, and managing a ransomware incident. Responses are assessed using five evaluation dimensions: specificity, completeness, technical correctness, feasibility, and alignment with the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF), including both explicit mapping and function coverage. The results demonstrate that ChatGPT provides structured, context-aware guidance that aligns well with NIST CSF 2.0 and addresses governance and third-party risks. However, the recommendations also exhibit limitations, including limited operational depth, assumptions about technology and regulatory environments, lack of prioritization for resource-constrained settings, and limited consideration of implementation costs. Overall, the proposed evaluation framework provides a systematic approach for assessing LLM-generated cybersecurity guidance, while the findings indicate that ChatGPT can serve as a valuable decision-support tool that should complement, rather than replace, qualified cybersecurity professionals. Full article

In critical infrastructure environments, cybersecurity situation-awareness platforms generate large volumes of alerts, including substantial numbers of false positives, placing a considerable burden on security analysts. At present, alert correlation methods mainly rely on rule-based matching or statistical clustering, and large language models often lack the domain-specific threat intelligence required for reliable security analysis. This paper proposes GRAL, which is an AI-driven framework that combines graph neural networks (GNN) for cross-asset temporal alert correlation, retrieval-augmented generation (RAG) for dynamic threat intelligence enrichment, and large language models (LLM) for semantic reasoning and verdict generation. A temporal heterogeneous graph attention network constructs alert-relation graphs within a 72 h sliding window, and temporal decay and multi-relational dependencies are captured. Powered by bge-m3 embeddings and a dense vector index, the RAG module retrieves the most relevant threat intelligence entries above a cosine similarity threshold of 0.75. A domain-specific dataset of 1000 annotated security alerts from a nuclear power operational environment was built, and Cohen’s Kappa reached 0.87. The experiments show that GRAL achieves a macro-averaged precision of 87.0%, a macro-averaged recall of 97.0%, and a binary false-positive rate of 9.1%, together with 92.5% alert compression. Generalisation capability is confirmed by cross-dataset evaluation on CICIDS2017 (93.0% accuracy and 92.5% F1-score) and UNSW-NB15 (89.4% accuracy and 89.8% F1-score). Full article

Generative artificial intelligence (GAI) is expanding from model-centered research into engineering and manufacturing activities, but its scope and maturity remain uneven. This PRISMA-guided bibliometric and abstract-level thematic review maps peer-reviewed industrial GAI research published from 2022 to 4 June 2026. Searches of Scopus, Web of Science, and the ACM Digital Library identified 492 records; 119 duplicates and 121 ineligible records were removed, leaving 252 studies. Keyword normalization, co-occurrence analysis, dominant and secondary thematic coding, and an abstract-reported evidence characterization were applied. The corpus shows two connected trajectories: engineering generation based on generative models for design, topology, materials, and electronics, and knowledge-intensive industrial intelligence based on large language models, retrieval-augmented generation, knowledge graphs, agents, and human–AI collaboration. Most studies report empirical or computational evaluation (72.2%), but 84.5% remain research-stage; only 0.8% indicate operational industrial evidence in their abstracts. The findings, therefore, distinguish publication activity from deployment maturity. Priority requirements for adoption include domain-grounded data, verification, manufacturability checks, traceability, cybersecurity, intellectual property protection, system integration, workforce preparation, and human accountability. This review contributes a reproducible cross-domain map, an overlap-aware synthesis, and stakeholder-specific guidance for trustworthy industrial GAI. Full article

Unmanned aerial vehicles have become indispensable components of modern defense systems by conducting Intelligence, Surveillance, and Reconnaissance (ISR) missions to collect critical operational information through onboard sensing technologies, supporting secure communication and information sharing among distributed military assets, and enhancing battlefield situational awareness through real-time sensing and data fusion. In addition, UAVs are increasingly capable of executing a wide range of defense missions, including target search and tracking, electronic warfare, search-and-rescue, and combat support. The integration of AI into UAV-based systems has the potential to enhance these operational capabilities by enabling intelligent perception, autonomous decision-making, adaptive mission planning, autonomous navigation, resilient communications, and cooperative multi-UAV coordination, thereby enabling the autonomous and collaborative execution of complex defense missions. This paper presents an up-to-date review of AI-enabled UAV-based defense systems, focusing on major operational domains including autonomous air combat and cooperative UAV operations, path planning and autonomous navigation, target tracking/detection/classification, cybersecurity, electronic warfare protection, and resilient UAV operation. In addition to surveying the recent literature, this paper provides an integrated system architecture, a functional classification framework, and an analysis of the AI paradigms enabling next-generation UAV-based defense systems. Furthermore, this review synthesizes the key technological trends, lessons learned, and cross-domain research challenges identified across the reviewed studies, providing a unified perspective on the current state of the field. Finally, this paper highlights promising future research directions for resilient, scalable, secure, and intelligent next-generation AI-enabled UAV-based defense systems. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. Full article

Personalized phishing is difficult to defend against because messages can be tailored to a target’s work, interests, and social context. Large language models may make such tailoring faster and easier, but it remains unclear whether messages produced from simple prompts are more convincing than those written by people. This 25-target pilot study compared personalized smishing messages generated by GPT-4 with messages written by novice student authors working under time constraints. Using the proposed Threshold Ranking Approach for Personalized Deception (TRAPD), participants ranked 12 messages written for them, indicated the point at which they would intend to click, explained their reasoning, and judged whether each message was authored by GPT-4 or a human. GPT-4-generated messages elicited an intention to click more often than student-authored messages (28% versus 21%), although the difference was uncertain. More broadly, our findings suggest that a simple prompt can produce personalized messages that participants found comparably convincing within the uncertainty of this pilot study. Job-related messages were significantly more likely to elicit an intention to click than hobby- or social-media-related messages. When asked whether a message was written by a human or generated by AI, participants identified the source no more accurately than chance, although the two study-specific message sets remained computationally distinguishable based on their text. Together, these findings suggest that accessible AI-assisted personalization may increase the practical scale of social-engineering threats, while also demonstrating both the value and current limitations of TRAPD for controlled and ethical comparison. Full article

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 has intensified the need for timely, trustworthy, and explainable cyber threat intelligence (CTI) for Industrial Control Systems (ICS). However, existing AI-enabled and Large Language Model (LLM)-based CTI solutions are predominantly designed for conventional IT environments and do not adequately address the safety, latency, governance, and operational constraints of industrial settings. This paper presents an AI-assisted CTI framework tailored to ICS and Industry 4.0 environments, integrating multi-source data ingestion, a Retrieval-Augmented Generation (RAG) knowledge store, a modular chain-of-agents architecture, and an explicit human-in-the-loop verification gate. Following a Design Science Research approach, the framework was evaluated through expert assessment involving twelve cybersecurity practitioners with experience in industrial and Security Operations Centre (SOC) environments and complemented by a proof-of-concept artefact instantiation based on the APT41 DUST campaign. The prototype integrated five heterogeneous CTI evidence sources and executed the automated analytical workflow in approximately 25 s (25.29 s) while illustrating evidence-grounded retrieval, specialized agent orchestration, and human-supervised intelligence generation. Practitioner feedback indicated that AI-assisted contextual intelligence and agent-based reasoning were perceived as valuable, while successful adoption depends primarily on governance, explainability, trust, and alignment with existing operational workflows rather than algorithmic sophistication alone. The study contributes a design-science artefact that combines retrieval-augmented intelligence, modular AI agents, and human oversight, providing practical design guidance for trustworthy AI-assisted CTI deployment in safety-critical Industry 4.0 environments. Full article

Phishing email attacks remain among the most common and damaging forms of cybercrimes. With the emergence of generative artificial intelligence (Gen-AI), adversaries can automatically generate tailored, well-crafted phishing emails for each potential victim rather than relying on mass-distributed templates, thereby reducing the effectiveness of traditional detection systems. In this study, we propose a novel hybrid framework for detecting AI-generated phishing emails that leverages natural language processing (NLP), machine learning (ML), and deep learning (DL). The uniqueness of the proposed approach lies in the dual application of bidirectional encoder representations from transformers (BERT): (1) as an embedding model to extract deep contextual representations of email content; (2) as a fine-tuned classifier. Additionally, we integrate high-impact common-word features, derived from the best-performing classifier, to enhance contextual interpretation and improve discrimination between human-crafted and AI-generated emails. The framework was evaluated on a balanced dataset combining real and Gen-AI phishing emails and benchmarked across six ML/DL models—support vector machine (SVM), random forest (RF), logistic regression (LR), long short-term memory (LSTM) networks, BERT, and generative pre-trained transformer (GPT)—using standardized preprocessing, hybrid feature engineering, and optimized hyperparameters. Experimental results show that the BERT fine-tuned classifier, enhanced with the integrated common-word features, achieved the highest accuracy of 98%, outperforming all other models and demonstrating strong generalizability. This study demonstrates how integrating contextual cues and custom lexical signals can significantly improve the detection of AI-generated phishing content. Cybersecurity professionals, policymakers, and researchers can develop sophisticated and resilient defenses against emerging AI-enabled threats. Full article

Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR^® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Paired-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (27.32%), outperforming both the benchmark and equal-weight portfolios, and the highest percentage of Sharpe ratios (1.2159), suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, and no statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimization requires balancing active security selection with diversification while recognizing the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy. Full article

This narrative review examines the evolving landscape of AI-based security in Cyber–Physical Systems 2.0 (CPS 2.0) within the context of AI-driven autonomous cybersecurity solutions for the Internet of Things (IoT). This article presents a narrative review, supported by a structured literature search inspired by the PRISMA 2020 project and descriptive publication statistics. It combines transparent study selection with qualitative conceptual synthesis, rather than a formal systematic review or bibliometric analysis. CPS 2.0 represents a new generation of interconnected systems that tightly integrate physical processes with intelligent computational components, enabling increased autonomy and operational efficiency. However, this growing complexity introduces advanced security threats and privacy challenges that traditional centralized security frameworks are ill-equipped to address due to limitations in scalability, latency, and data sensitivity. The paper explores how artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) enhance real-time threat detection, prediction, and response in distributed environments. It highlights the role of edge computing in decentralizing intelligence, thereby reducing latency and limiting exposure of sensitive data. Additionally, federated learning (FL) is discussed as a privacy-preserving paradigm that enables collaborative model training across distributed nodes without sharing raw data. The integration of GenAI, FL, and edge computing is presented as a synergistic approach that enables adaptive, context-aware, and proactive defense mechanisms against dynamic and evolving cyber threats. The review further analyzes architectural frameworks, key advantages, and inherent vulnerabilities of CPS 2.0, along with mitigation strategies and real-world applications, particularly in industrial control systems. By synthesizing current advancements and challenges, this work provides a comprehensive roadmap for designing resilient, scalable, and privacy-aware CPS infrastructures. The findings contribute to the development of secure and intelligent systems aligned with the future demands of Industry 4.0, 5.0, and beyond. Full article

The Internet of Vehicles (IoV) is a relatively new technology that enables smart, connected transportation networks but is increasingly susceptible to internet-based attacks such as spoofing and Denial-of-Service (DoS), which jeopardize user security and system integrity. Conventional Artificial Intelligence (AI) or Machine Learning (ML) models have been used in IoV security, but their limited flexibility and inability to handle complex, evolving attack patterns limit their usefulness in real-world IoV scenarios. This work examines the application of AI/ML models, such as Long Short-Term Memory (LSTM), Deep Reinforcement Learning (DRL), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to improve cybersecurity in IoV through both proactive and reactive defense strategies. An extensive literature review of current ML-based IoV security solutions is conducted, and AI/ML-based detection models are designed using CICIoV2024, a large-scale dataset obtained from experiments on the Electronic Control Units (ECUs) of a 2019 Ford car. Based on extensive experiments and analysis, we found that the LSTM model can outperform other AI/ML models in identifying spoofing and DoS attacks, thereby enhancing the security of next-generation IoV. Our work can pave the way for understanding the role of the latest AI/ML techniques in enhancing IoV security and the implementation challenges they pose in real-world scenarios. Full article

The digitalization of healthcare systems has transformed the production, storage, and sharing of clinical information. While electronic health records (EHRs) enhance care efficiency, accessibility, and continuity, they simultaneously introduce complex ethical, legal, and cybersecurity challenges that directly affect patient interests. This narrative review examines the emerging rights associated with digital health records, particularly the right to privacy and the right to be forgotten, alongside threats to confidentiality, cybersecurity, and patient safety. A comprehensive literature search was conducted across PubMed, Web of Science, MEDLINE, and the Cochrane Library. First, the right to be forgotten appears particularly relevant for oncological patients facing financial discrimination and for individuals asserting gender identity rights, yet it cannot be unconditionally extended to genetic data, given its relevance to relatives and future generations. Second, confidentiality risks are amplified by re-identification vulnerabilities, unauthorized access by personnel, and the broad connectivity of digital systems. Third, the secondary use of data from EHRs, including artificial intelligence (AI) integration, commercial exploitation, and large language model training, raises substantial privacy concerns. Fourth, ransomware, phishing, and data breaches can erode patient trust. In this article, we analyze these issues within the evolving European regulatory framework, highlighting the tension between individual privacy rights and broader public interests. We argue that robust data protection must be balanced with scientific progress and that this requires opt-in consent frameworks, staff training, and transparent AI governance. Full article