Search Results (1,083)

Autonomous ship maneuvering in harbor environments is increasingly supported by advances in model predictive control, reinforcement learning, digital twins, multi-sensor fusion, berth allocation, and multi-agent coordination. However, these developments are often studied as separate technological domains, while real harbor autonomy requires coordinated operation across vessels, port infrastructure, regulatory systems, cybersecurity mechanisms, and human supervisory processes. This study presents an architecture-oriented critical review of autonomous ship and harbor maneuvering research published between 2015 and May 2026. The review synthesizes literature from control engineering, maritime artificial intelligence, sensor fusion, digital twins, port logistics, cyber-physical systems, regulation, cybersecurity, and human–AI supervision. The analysis introduces two conceptual contributions: a layered cyber-physical taxonomy and an integration maturity model. The taxonomy organizes autonomous harbor maneuvering into seven interdependent layers: physical dynamics, perception and sensor fusion, prediction and state estimation, control, decision and coordination, digital twin federation, and regulatory–supervisory governance. The maturity model distinguishes isolated vessel autonomy, assisted coordination, shared digital synchronization, agent-based coordination, and fully federated maritime cyber-physical autonomy. The reviewed evidence shows substantial progress in individual layers, especially control, perception, digital twins, and berth allocation. However, major gaps remain in cross-layer synchronization, semantic interoperability, regulation-aware decision-making, cybersecurity integration, and validated ship–shore federation. To address these gaps, this study proposes a Federated Maritime Cyber-Physical Architecture for autonomous harbor maneuvering. The architecture integrates vessel autonomy cores, port intelligence cores, semantic federation middleware, agent-based negotiation, regulatory verification, cybersecurity safeguards, and human supervisory interfaces. This review argues that future progress in autonomous harbor operations depends not only on stronger algorithms, but on interoperable, explainable, regulation-aware, and cyber-resilient ship–shore ecosystems. Full article

Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts–Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to $1.8\times$ higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet. Full article

Cyber-physical systems (CPSs) based on the Internet of Things (IoT) form the backbone of modern smart infrastructures, including smart cities, healthcare monitoring, industrial automation, and intelligent transportation. However, connecting many resource-limited IoT devices makes them more vulnerable to cyber threats, particularly quantum attacks. This review comprehensively examines quantum-secure communication (QSC) frameworks for IoT-enabled CPS, focusing on Quantum Key Distribution (QKD), post-quantum cryptographic (PQC) algorithms, and hybrid quantum–classical security models suitable for constrained devices. A PRISMA-guided search of the Scopus and Google Scholar database was conducted in January 2026 using three keyword groups related to hybrid security, artificial intelligence, and cyber-physical systems. Based on the evaluation, 6008 publications have been identified between 2001 and 2026. The first-round screening was performed for 4948 articles, after excluding duplicates. During the screening stage, 348 articles were selected for abstract scrutiny, 115 records were excluded due to no direct focus on CPS/IoT applications, 52 studies were excluded because these papers relied on traditional security models, 25 studies were excluded due to insufficient relevance to the review objectives, and 15 additional non-English studies were removed. Following the screening stage, 141 studies were selected for full-text eligibility. Out of those, 86 studies were removed due to a lack of specific evaluation metrics or not being published in a peer-reviewed venue. Furthermore, the publications are classified as QKD-based secure CPS and QSC for industrial IoT, AI-Assisted Secure Communication for CPS Networks, and hybrid PQC-QKD models for CPS/IoT devices. This article investigates recent advancements in secure data transmission, verified protocols, and AI-driven anomaly detection customized to CPS/IoT environments. In addition, operational hurdles, interaction with open innovations, real-time deployment, and secure edge-cloud integration are highlighted. By analyzing recent developments and identifying research gaps, this review provides a structured roadmap for designing secure, scalable, and quantum-safe IoT-based CPS frameworks capable of withstanding next-generation cyber threats. This systematic review was performed and reported according to the PRISMA 2020 guidelines. Full article

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly significant due to frequent data exposure incidents. Statistical analyses further demonstrate significant differences between non-profit organizations aligned with DHS CISA critical infrastructure sectors and those operating outside these sectors, especially regarding the prevalence of availability-focused attacks. In addition to its empirical contribution, the NPCIR initiative supports experiential learning opportunities for undergraduate and graduate students in cybersecurity and information technology. The resulting dataset provides actionable cyber threat intelligence for researchers, practitioners, and non-profit leaders seeking to strengthen organizational cybersecurity resilience and awareness. Full article

Cyber–Physical Systems (CPSs) are increasingly vulnerable to Distributed Denial-of-Service (DDoS) attacks, which can disrupt critical operations and compromise system safety. Although deep learning (DL) techniques are widely adopted for cyberattack detection, conventional DL-based classifiers often struggle to handle the uncertainty and ambiguity inherent in network traffic data. To address this limitation, this paper proposes an AI-driven hybrid framework, termed LSTM–Fuzzy–CPS, for adaptive DDoS detection in CPS environments. Unlike prior LSTM–Fuzzy approaches that are primarily restricted to SDN settings, the proposed framework is adapted for CPS environments and introduces continuous risk scoring, reduced false positives for safety-critical operation, and proportional mitigation mechanisms. The framework consists of a detection module and a conceptual mitigation module. The detection module, named LSTM–Fuzzy–Detector, integrates an LSTM network with a Mamdani-type fuzzy inference system that maps LSTM outputs into a continuous risk score using triangular membership functions (Low, Medium, High) and centroid defuzzification. The mitigation module is designed as a rule-based conceptual framework that translates risk levels into adaptive response actions; however, its experimental implementation is left for future work. The proposed detector is evaluated on the CICIoT2023 dataset and achieves an accuracy of 99.83% with a false-positive rate of 0.12%, demonstrating strong robustness against complex and evolving attack patterns. These results indicate that the proposed framework provides an effective, interpretable, and scalable solution for intelligent threat detection in CPS environments. Full article

Digital twins (DTs) in the energy sector are operational-data-driven models of assets, installations, and networks. Their value grows alongside renewable expansion, electronic communications, and stricter resilience requirements for critical infrastructure. This study evaluates DT applications in Poland’s energy transition, identifying regulatory and cybersecurity determinants required for safe, scalable use. The methodology combines an international literature review, regulatory assessment, and qualitative desk research focusing on DT projects across four Polish energy groups: Enea, Energa, PGE, and Tauron. Each case is assessed using a DT maturity and governance framework covering scope, data coupling, decision support, and security posture. The study identifies four primary deployment types: (1) operational network twins for distribution system operators leveraging SCADA/ADMS, GIS, and state estimation; (2) AI-driven asset performance twins for wind turbines and CHP plants; (3) flexibility twins for hydropower system services; and (4) immersive training twins for the offshore wind sector. Main constraints include data quality, interoperability, fragmented data access regulations, and expanded cyber-attack surfaces from OT/IT convergence. DTs aid spatial planning, mitigating location and land use conflicts. Recommendations emphasize harmonized data governance, cybersecurity-by-design, special determinants, and the creation of regulatory sandboxes to support DT implementation within critical energy infrastructure. Full article

Time-delay systems (TDSs) arise in many engineering applications where sensing, actuation, computation, transport, or communication delays affect closed-loop stability and performance. Classical control methods, including predictor-based control, Lyapunov–Krasovskii functional approaches, robust control, model predictive control, and adaptive control, provide rigorous theoretical foundations for delay compensation and stability analysis. However, their effectiveness may be limited when the system is nonlinear, uncertain, poorly modeled, or subject to unknown and time-varying delays. In recent years, artificial intelligence (AI)-based methods, such as neural networks, fuzzy systems, deep learning, and reinforcement learning, have attracted increasing attention for their capabilities in learning, approximation, prediction, and adaptation. This paper presents a comprehensive review and bibliometric analysis of control strategies for TDSs, with an emphasis on the interactions among classical, AI-based, and hybrid methods. Publications indexed in the Web of Science database from 2010 to 2025 are analyzed using bibliometrix and VOSviewer to identify publication trends, influential contributors, collaboration patterns, citation structures, and thematic evolution. In addition, a unified framework is proposed to classify TDS control strategies into classical, AI-based, and hybrid categories. The results show that classical stability and robustness analysis remain central to the field, while AI-based and hybrid methods are increasingly used to address nonlinearities, uncertainties, communication delays, and real-time implementation challenges. Finally, key research gaps and future directions are discussed, including stability-guaranteed learning, learning-based delay compensation, interpretable AI control, benchmarking, and practical deployment in cyber-physical, robotic, aerospace, and networked systems. Full article

This study presents a comparative analysis of six DFS implementations representing different maturity levels and investigates the systemic gap between technological capabilities and regulatory approaches. A structured narrative review with case-based analysis was conducted using the Scopus database (2015–2026) with six targeted queries. The case selection followed the PICo protocol. An original ten-criterion DFS maturity assessment rubric—grounded in the Technology Readiness Level (TRL), Integration Readiness Level (IRL), and Digital Twin Maturity Model frameworks—was applied to all six cases. Inter-rater validation yielded substantial agreement (κw = 0.797; unweighted κ = 0.674 [95% CI: 0.509, 0.839]). The results indicate a clear maturity gradient (Dimension X: 4–9 points; Dimension Y: 2–8 points). Benefits reported in the analysed primary studies include up to a 55 s reduction in evacuation time, a 72% improvement compared with static signage, and a 34-percentage-point increase in evacuation success rate under simulation-based conditions. Five normative recommendations are proposed to address the structural regulatory gap between current prescriptive frameworks and DFS deployment in Poland and the EU. This study argues that prescriptive rules should remain the baseline, whereas complex facilities may adopt performance-based DFS solutions, provided that equivalence to conventional protection levels is rigorously demonstrated. From a sustainability perspective, the study frames DFS as a dynamic safety layer that supports occupant protection, operational resilience, and lifecycle adaptability in complex buildings exposed to uncertain fire and crowd conditions. Full article

The growing sophistication of the internet and web space has spawned highly dynamic, multi-vector cyber threats that cannot be handled by automated detectives and hence the necessity to introduce analyst-oriented, cognitively powerful security analysis apparatus. The character of current visualization-based security frameworks is that they are inclined to deliver data unproactively, fail to engage the dynamic setting, and fail to comprehend the evolving motive of assailants, resulting in subsequent identification and a fractured understanding of coordinated web attacks. The paper introduces a new model of interactive security visualization known as Context-Oriented Visual Exploration of Resilient Threats (COVERT), a hybrid of behavioral context modeling, adaptive visual storytelling, and intent-sensitive interaction. COVERT is dynamically rearranged to the development of threats, patterns of interaction between analysts, and objectives of the possible attacks, which helps in releasing relevant security capabilities gradually. The framework integrates graphical threat flows, attention-directed visual cues, and real-time feedback loops to align system responses to the thinking processes of the analysts. The evaluation of high-scale web traffic and attack simulation dataset indicates that COVERT is much more effective in the multi-stage detection of attacks, false-positive interpretation is minimized, and the investigation period is reduced compared to the visualization infrastructure of the static and semi-interactive infrastructure. According to user studies, there is higher situation awareness, enhanced correlation of distributed events, and enhanced decision-making in complex web intrusion situations, such as advanced persistent threats and web exploitation coordination. Combining contextual intelligence with adaptive interaction and visualization of security, COVERT reveals that intent-based visual analytics may greatly improve internet and web threat detection and analysis systems to support more agile and resilient cyber defense procedures. The proposed COVERT strategy achieved 93% threat-detection rate, the false positives were reduced to 6%, the response time of the analysts was reduced to 140 s, and the situational awareness was increased to 88%. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article

Large language models (LLMs) offer a flexible interface for human–machine interaction, but their direct use in embedded control remains difficult because low-cost microcontrollers cannot host such models locally and unconstrained language generation is not physically grounded. This paper presents EdgeTalk-MCU, a local host–microcontroller framework for low-latency natural-language control of resource-constrained devices. The system couples a locally deployed LLM on the host side with an ESP32-S3 microcontroller through a lightweight serial protocol and closes the loop with real-time state feedback. The reported end-to-end decision latency of ∼0.15 s refers to the host-side inference pipeline; physical platform latency additionally includes UART round-trip and servo actuation overhead. The design combines two complementary mechanisms: a state-aware prompt constraint that injects task progress and physical state into the host-side policy, and a runtime shield that enforces hard execution consistency before actuation. This decomposition separates raw policy quality from executed safety. Across representative obstacle scenarios in simulation, unshielded controllers remain unreliable—LLM-only and Prompt-only exhibit collision rates of 30.6% and 26.5%, respectively, in the Sudden Obstacle setting—whereas both shielded methods reduce collision to 0%. An ablation study confirms that the runtime shield is the decisive safety mechanism; the state-aware prompt constraint contributes primarily at the raw-proposal level by reducing the fraction of unsafe proposals submitted to the shield, rather than by independently guaranteeing safe execution. Hardware-in-the-loop (HIL) validation on a physical ESP32-S3 platform confirms that the same qualitative pattern holds under real sensing and communication conditions. Full article

Manufacturing systems are undergoing a fundamental transition as efficiency-driven optimisation paradigms prove increasingly inadequate for meeting net-zero, resource-efficiency, and resilience objectives. Digital twins have emerged as a central enabler of this transition, offering continuously coupled physical–digital representations capable of real-time monitoring, prediction, and control. Recent advances in artificial intelligence have accelerated this evolution, transforming digital twins from static simulation artefacts into adaptive, learning-enabled systems embedded within cyber–physical manufacturing environments. However, this shift has also exposed critical challenges related to trust, interpretability, scalability, and sustainability alignment. This review provides a critical synthesis of AI-enabled digital twin research with a specific focus on manufacturing and additive manufacturing systems. It examines the progression from physics-based and data-driven twins toward hybrid AI–physics architectures that balance predictive performance with physical consistency and explainability. Beyond technical performance, the review reframes digital twins as decision-making infrastructures whose value depends on how effectively they integrate energy consumption, material efficiency, carbon intensity, and lifecycle impacts into optimisation and control logic. Particular attention is given to real-time optimisation, predictive maintenance, and intelligent asset management, highlighting persistent gaps in uncertainty propagation, cross-scale coordination, and sustainability-aware governance. The review further identifies structural barriers to large-scale industrial adoption, including data interoperability fragmentation, platform lock-in, organisational resistance, and regulatory ambiguity surrounding AI-driven decisions. Synthesising insights across domains, it argues that many current digital twin implementations remain technically sophisticated yet strategically conservative, reinforcing throughput-centred objectives rather than enabling systemic decarbonisation and circularity. The paper concludes by outlining future research directions and policy-relevant opportunities, emphasising the need for digital twins that reason across timescales, objectives, and lifecycle boundaries. By aligning manufacturing intelligence with measurable sustainability outcomes, AI-enabled digital twins can move from incremental efficiency gains toward transformative impact in net-zero and circular manufacturing systems. Full article

The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN’s programmable global visibility complements BC’s decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency–security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Critical infrastructures (CI) are essential to modern society, providing vital services such as energy, water, and transportation. However, these systems are increasingly targeted by sophisticated cyberattacks, exploiting vulnerabilities in both IT (Information Technology) and OT (Operational Technology) environments, posing significant risks to safety, economic stability, and national security. Despite advancements, current anomaly detection models for CI often cannot effectively integrate diverse data sources or provide detailed attack classifications. To address these challenges, we propose a novel Graph Convolutional Network (GCN) model integrated with Long Short-Term Memory (LSTM) layers for effective anomaly detection and attack classification in CI. The model leverages Cyber Threat Intelligence (CTI) and MITRE ATT&CK techniques, integrating network traffic and physical device data to enhance detection of sophisticated threats. Unlike approaches using binary classification, our model performs multiclass classification to recognize specific attack types, bridging the gap in understanding complex attack patterns within CI. By incorporating Indicators of Compromise (IoCs) from MISP (Malware Information Sharing Platform) with the SWAT (Secure Water Treatment) dataset, we developed a graph-based data structure where nodes represent entities like SCADA tags and IP addresses. The model processes this dynamic graph using convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. Results indicate a significant improvement over existing solutions, achieving a test accuracy of 99.04% and a macro F1-score of 0.9151. The integration of multiple data sources enhances the model’s capacity to handle evolving cyber threats, making it well-suited for protecting CI. Full article