Search Results (91)

Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable decision support, and human-in-the-loop control. This paper presents a proposal of a technology-agnostic reference architecture that builds on Industry 4.0 frameworks by incorporating the human-centric, resilient, and sustainable principles of Industry 5.0. Its intelligent layer enables the new approach to human involvement in the process, facilitating meaningful human–machine collaboration. The proposed research provides a practical and conceptual framework for systems engineers, industrial software architects, and operations managers seeking to transition legacy operational plants into human-aligned ecosystems. Its feasibility is evaluated through a simulation-based underground mining testbed, where heterogeneous data sources and communication protocols are integrated into a common operational environment. The proof of concept shows how telemetry, data storage, machine learning models, and operator feedback can be combined to support auditable, explainable, and human-contestable industrial decisions, demonstrating the classification accuracy, remaining useful life forecasting capabilities, and enhanced recommendation precision enabled by iterative operator feedback loops. Full article

Phishing attacks exploit human vulnerabilities through social engineering techniques; therefore, a multidimensional framework integrating personality and cognition is crucial for tailoring personalized defenses against phishing threats. To prevent phishing attacks, focusing on psychological mechanisms has become the primary approach to address the human-centric nature of these threats. In response, we propose an integrated framework that synthesizes dimensions from the Five-Factor Model (FFM) and the Myers–Briggs Type Indicator (MBTI), grounded in Dual Process Theory to explore the cognitive drivers underlying decision-making under threat. To operationalize this framework, we developed a decision tree classifier to quantify the predictive significance of various personality traits and their hierarchical interactions. The results indicate that the personality types associated with the highest phishing risk profiles are ENFP, ESFP, ESTP, and ENFJ. These hierarchical classification results are projected onto the C.O.R.E. Quadrant (Cognition Orientation Risk Evaluation Quadrant) which enables the systematic representation of risk patterns across all 16 personality types. By providing a structured visualization of personality-driven risk patterns, the C.O.R.E. Quadrant offers a practical foundation for developing personalized defense mechanisms and tailored cybersecurity training strategies, moving beyond one-size-fits-all security protocols. 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

Large Language Models (LLMs) are emerging as key enablers of Future Internet ecosystems, supporting intelligent, adaptive, and context-aware services across distributed cyber-physical environments. Beyond traditional natural language processing, LLMs are increasingly integrated into Smart Internet of Things (SIoT) systems to enable semantic interoperability, edge intelligence, multimodal interaction, and autonomous service orchestration. This paper presents a taxonomy-based review of LLM architectures, training paradigms, deployment strategies, and emerging applications within Future Internet infrastructures. The review classifies existing studies by deployment environment, architecture, training strategy, accessibility, and application scope, and analyses the role of LLMs in intelligent IoT environments, with emphasis on edge-based reasoning, agentic AI, human-centric automation, and context-aware decision-making. Key challenges are examined, including scalability, inference latency, privacy, trustworthiness, security, hallucination, and energy efficiency in resource-constrained environments. A comparative analysis of representative LLMs is presented, based on deployment feasibility, multimodal capability, accessibility, and suitability for distributed intelligent services. The originality of the review lies in conceptualizing LLMs as cognitive middleware that provides semantic, reasoning, and coordination capabilities across Smart IoT infrastructures. Finally, future research directions are highlighted, including decentralized AI architectures, digital twins, the Model Context Protocol, retrieval-augmented generation, multimodal sensing, and autonomous agent-based ecosystems. Full article

In the complex landscape of Industry 5.0, traditional management systems for smart manufacturing struggle to harmonize high-speed production with the rapid integration of AI and digital technologies. Crucially, these legacy frameworks often fail to capture tacit human knowledge or ensure trustworthy AI and trusted sharing of sensitive industrial data. This paper proposes a novel Decision Support Framework (DSF) that addresses these challenges through a multi-layered approach. At its core, the framework utilizes Data Spaces to enable secure, sovereign data sharing, ensuring that organizations maintain control over their assets. To handle the inherent ambiguity of industrial data, the system employs fuzzy logic and DAG-based root-cause-oriented investigation to provide robust recommendations, helping users distinguish descriptive correlations from plausible structural dependencies that require expert validation. Furthermore, the framework integrates eXplainable AI (XAI) services and AI-driven visual analytics, transforming complex algorithmic outputs into transparent, intuitive insights. By synthesizing data sovereignty with interpretable machine intelligence, this framework empowers trusted data sharing and human-centric decision-making, providing an advanced platform for achieving operational excellence within the Industry 5.0 vision. The proposed DSF is validated in three different pilot cases with end-users to be a milk industry, an automotive supplier and a machine manufacturer. Full article

There is a need to ensure that the application of artificial intelligence (AI) in increasingly automated operations is safe, human-centric, and trustworthy, and respects ethical principles. To this end, this paper presents an innovative holistic assessment framework to support certification-aware design of AI-based sociotechnical systems with a range of levels of automation along multiple design stages from low to high technology and human readiness levels (TRLs/HRLs). The holistic scope considers a range of relevant key performance areas (KPAs): safety, resilience, security, Human Factors, accountability, responsibility, liability, efficiency, societal sustainability, and environmental sustainability. The core of the framework is a seven-step cycle that assesses the KPAs for critical scenarios and evaluates the combined performance, including uncertainty and trade-offs. This provides feedback to either adapt the design at the same TRL/HRL or refine it at higher TRLs/HRLs. The framework enacted by a toolbox of assessment methods for the KPAs. The framework has been developed in the aviation domain, but it is formulated in a generic manner, enabling application to various AI techniques and operational domains. Its application is illustrated in detail for an air traffic management use case that employs an AI-based system to support air traffic controllers in sequencing aircraft. It is concluded that the framework provides a viable approach for holistic assessment of AI-based sociotechnical systems. Full article

Industrial digitalization is moving from Industry 4.0 toward Industry 5.0’s emphasis on resilience, human-centric operation, and sustainability. This shift is enabled by the convergence of Operational Technology and Information Technology, but this integration also broadens the exposure of industrial infrastructures to cyber threats targeting communication integrity and process continuity. Mitigating these risks requires network control that is both programmable and aware of each asset’s operational context. However, there is still a lack of operational interfaces that translate the semantics of industrial assets into programmable, runtime-enforceable network behavior. In this paper, following a Design Science Research methodology, we introduce an asset-aware, closed-loop network control abstraction in which the industrial network itself is modeled as a managed asset through Asset Administration Shells. Asset state, lifecycle phase, and operational intent are translated into network policies enforced at runtime on programmable data planes, while in-network telemetry is exposed at the asset level and correlated with operational metrics. We validate the abstraction on a hybrid testbed that combines virtualized components with industrial-grade hardware and virtualized 5G connectivity, through three security-oriented use cases: (i) asset-driven customization of forwarding policies; (ii) human-centric secure maintenance with controlled remote access over 5G; and (iii) anomaly detection and isolation based on cross-layer telemetry correlation. The results show that asset-level operations can drive programmable network enforcement and make network telemetry available at the asset layer. Finally, the work outlines a first step toward standardizing network-oriented asset submodels by separating control-plane operations from data-plane state and telemetry. Full article

The Fifth Industrial Revolution (Industry 5.0) requires human-centric mechanisms that preserve the integrity, reproducibility, and interpretability of AI-driven decisions in smart manufacturing. Injection molding generates heterogeneous, imbalanced, and weakly labeled process data, posing reliability and integrity risks to data-driven quality control. This study proposes an integrity-verified and reproducibility-instrumented secure machine learning framework for operating-regime analysis in injection molding that integrates (i) SHA-256-based data-integrity verification at ingestion, (ii) Pearson correlation-based feature selection, and (iii) a Gaussian Mixture Model (GMM) under a passive-adversary threat model with Transport Layer Security (TLS)-secured transmission. Evaluated on real industrial data (n = 6719 cycles, seven process variables), correlation-based feature selection retained four non-redundant variables and improved the GMM Silhouette Score from 0.274 ± 0.075 (all features) to 0.323 ± 0.014 (95% CI [0.318, 0.329]), a +18.2% relative improvement (paired t(29) = 3.39, p = 0.002; Cohen’s d = 0.62; Wilcoxon p = 0.022), while lowering the Davies–Bouldin Index from 1.63 to 1.17. The Silhouette standard deviation of 0.014 over 30 seeds meets the σ ≤ 0.02 reproducibility target. The GMM resolves four interpretable operating regimes—one low-load regime consistent with nominal operation and three elevated-load regimes (left-side, right-side, and bilateral)—with operator-readable per-variable signatures. Relative to hard-partition and projection baselines, the GMM is not Silhouette-optimal but provides an interpretable, generative regime model that meets the σ ≤ 0.02 reproducibility target. The framework operationalizes human-centric manufacturing security as measurable integrity, reproducibility, and interpretability. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Phishing susceptibility reflects both stable psychological traits and transient user states, but confirmed victim cases remain rare in survey studies. This study evaluated BioHARP, a feasibility framework that pairs an outcome-independent psychometric prior with a prospective bio-adaptive fusion design. Using $N=136$ anonymized respondents (12 strict victims), we constructed 69 pre-incident predictors after excluding administrative metadata, exposure indicators, and post-incident response items. A cost-sensitive TabTransformer was trained without synthetic minority generation and benchmarked against six conventional tabular baselines and FT-Transformer under identical splits, unified preprocessing, and model-appropriate cost-sensitive imbalance handling. Out-of-sample performance was primarily assessed with a 60-seed repeated stratified hold-out protocol with fixed four-positive/thirty-negative test composition. Across the sixty splits, TabTransformer yielded a mean AUC of $0.534\pm 0.157$, whereas CatBoost yielded $0.736\pm 0.108$. On fixed Seed 100, TabTransformer reached AUC $=0.8167$ and CatBoost AUC $=0.775$; for the single-init TabTransformer, this was the best-observed split and was therefore interpreted as an optimistic upper-end point estimate. Threshold-dependent metrics were reported separately as an exploratory analysis with explicit leakage labeling. The physiological fusion layer was evaluated as an outcome-informed oracle upper bound, reaching AUC $=0.944$ on Seed 100 and $0.878\pm 0.058$, range [0.73, 0.98], across 70 alternative scenario RNG seeds. This result was interpreted strictly as theoretical headroom rather than deployment-calibrated performance. Overall, BioHARP was framed as a feasibility framework with a clearly bounded physiological-fusion design and explicit calibration and sensor requirements for future deployment-ready bio-adaptive detectors. Full article

Critical infrastructures are increasingly Internet-connected cyber–physical systems whose recovery after cyber incidents must satisfy safety, timing, regulatory, and interdependency constraints. Yet, the use of large language models (LLMs) for generating recovery plans remains fragmented across cybersecurity, industrial control, digital twins, and AI assurance research. This review synthesizes that emerging field through a structured critical survey of studies on LLMs in incident response, OT/ICS resilience, and cyber–physical recovery, with a focused perspective on grounding, trust, and assurance mechanisms relevant to recovery-plan generation. It develops an architecture-centric taxonomy spanning prompt-only assistants, retrieval-augmented copilots, graph-aware planners, multi-agent systems, and hybrid verification/simulation pipelines; maps realistic applications across energy, water, manufacturing, transportation, healthcare, and telecommunications; and organizes limitations into technical, security, governance, and human-factor categories. Based on this synthesis, the paper proposes the Grounded Recovery Planning Stack as a reference architecture and outlines a staged roadmap from human-in-the-loop copilots to bounded orchestration. The main conclusion is that near-term value lies in grounded, auditable, compliance-aware copilots, whereas autonomous recovery execution remains premature without stronger validation, state-aware grounding, sector-specific benchmarks, and formal safeguards. Full article

Modern cybersecurity challenges span multiple layers, from human behavior and identity management to network communication and device security. This paper proposes a unified multi-layered security framework that integrates human-centric, identity-centric, and communication-centric defenses into a coherent architecture. Drawing on insights from diverse domains (industrial control systems, IoT, healthcare, blockchain, and quantum communications), we identify common defense-in-depth principles and interdependencies across layers. The study highlights the persistent gaps in current research, which often focuses on isolated layers or domain-specific models, and addresses these gaps by synthesizing a cross-domain framework. We develop a mixed-method methodology to compare and integrate multi-layer security mechanisms, and we implement a proof-of-concept risk assessment engine to evaluate the framework’s effectiveness. Preliminary results from this implementation demonstrate that combining layers yields significantly improved detection performance and resilience compared to single-layer baselines. The framework’s contributions include a comprehensive literature-driven model, an operational validation in a simulated environment, and guidelines for deploying multi-layer defenses in complex, interconnected infrastructures. Empirical findings confirm that an integrated multi-layer approach can adapt to varied threat scenarios and reduce vulnerabilities, underscoring the value of coordinated controls across technical and human factors. The proposed framework lays a foundation for future work on scalable, cross-layer cybersecurity architectures that better protect contemporary cyber–physical systems. Full article

Users encounter privacy and permission settings across digital platforms, yet often struggle to understand, locate, and manage them effectively. Despite regulatory efforts such as the General Data Protection Regulation (GDPR) and platform mechanisms like App Tracking Transparency, these challenges persist due to interface design limitations rather than solely user capability. This study evaluates the My Information and Data Access (MIDA) framework, a user-centred privacy interface designed to support users with different levels of expertise. A between-subjects usability study was conducted with 44 participants (novice n = 15, intermediate n = 14, advanced n = 15), combining System Usability Scale (SUS) scores, task completion rates, error rates, and think-aloud protocols. The results show high usability across all groups, with SUS scores of 80 (novice), 84 (intermediate), and 92 (advanced), all exceeding the acceptability threshold of 68. Task completion rates exceeded 80%, whilst error rates remained below 25% across most tasks. These findings indicate that MIDA can support users in understanding, configuring, and managing privacy settings across different levels of expertise. This study builds on prior HCI research linking privacy management challenges to interface design limitations and provides empirical evidence that an expertise-adaptive interface can improve users’ ability to understand and manage privacy settings. Full article

The transition toward Industry 5.0 in sustainable construction demands a radical reconceptualisation of workforce development, moving beyond purely technical training to embrace human-centricity, digitalisation, green competencies, and socio-cognitive resilience. Traditional vocational and higher education systems have largely failed to bridge the gap between emerging construction industry demands and the competencies possessed by current and future professionals. This systematic review investigates how Learning Factories’ 5.0 immersive, experiential, and technology-rich educational environments can address these gaps in sustainable construction contexts. Drawing on a synthesis of 71 peer-reviewed publications spanning 2015–2026 and supplemented by targeted construction-domain literature, this study pursues three objectives: (1) identifying core competencies for Industry 5.0 readiness in sustainable construction, (2) examining how Learning Factories 5.0 support the development of these competencies, and (3) proposing a competency-driven framework for integrating Learning Factories 5.0 into sustainable construction education and training. Seven transdisciplinary competency clusters are identified—Attitude toward Digitalisation, Technical–Green Proficiency, Information and Data Literacy, Digital Security, Collaborative Systems Thinking, Adaptive Problem-Solving, and Reflective Sustainability Practice—and a theoretically derived, eight-phase Construction Learning Factory 5.0 (CLF5.0) Framework is proposed as a conceptual architecture for future empirical development and institutional adaptation. The framework is presented as a generative starting point rather than a prescriptive model, and its effectiveness in diverse construction education contexts requires empirical validation through future implementation studies. Findings reveal that while Learning Factories offer transformative potential, critical barriers remain in terms of economic feasibility, faculty development, industry–academia alignment, and empirical validation. This paper contributes a construction-specific competency architecture and implementation pathway to support the industry’s transition toward a sustainable, human-centric, and Industry 5.0-aligned future. Full article

The incorporation of Artificial Intelligence (AI) into cybersecurity has become widespread, largely propelled by the emergence of Generative AI (GenAI) and Large Language Models (LLMs). While these technologies promise to revolutionize threat detection, they introduce profound challenges regarding explainability, trust, and deployment feasibility in resource-constrained environments. Current research often exhibits a form of technological determinism, prioritizing algorithmic performance over the operational realities of Security Operations Centers (SOCs). This paper presents a hybrid qualitative Systematic Literature Review (SLR) and Mapping Study, adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. Our research questions are narrowly focused, seeking to explore how four key domains intersect: (1) Explainable AI (XAI) methods; (2) cybersecurity operations; (3) human-centered design; and (4) the constraints inherent to edge computing. From an initial corpus of 385 records drawn from Scopus and OpenAlex (spanning a search window from 2014 to 2025, with relevant findings heavily clustered in the 2020–2025 period), included studies were evaluated using a quality assessment protocol adapted from Kitchenham’s guidelines, scoring each study on a 0–24 scale across four dimensions (Venue Quality, Methodological Rigor, Dataset Realism, and Depth of XAI/Human Validation). The results reveal a significant “validation gap”: while 63% of studies claim human-centric relevance, only ~22% incorporate empirical validation with human operators. Furthermore, we identify a critical trade-off between the reasoning power of cloud-based LLMs and the privacy requirements of Edge security. We conclude by proposing a research agenda for “Cognitive SOCs”, emphasizing the need for Small Language Models (SLMs), standardized human-centric metrics, and robust hallucination detection mechanisms. Full article