Search Results (215)

Cyber–physical systems (CPSs) increasingly rely on complex software components whose vulnerabilities may affect both digital services and physical processes. Fuzzing is a practical technique for discovering such vulnerabilities in CPS-facing parsers, protocol handlers, and edge services. Distributed fuzzing improves throughput, but outsourcing fuzzing tasks to multiple untrusted nodes introduces privacy risks: valuable seeds, especially crash-triggering samples, may reveal vulnerability information before affected users are protected. In this paper, we propose PrivFuzz, a privacy-preserving collaborative fuzzing framework. PrivFuzz allows organizations and individuals to collaborate and receive rewards while keeping fuzzing seeds confidential and enabling controlled encrypted seed reuse among untrusted fuzzing nodes. The key idea is to combine trusted execution environments (TEEs) with blockchain-based smart contracts to support confidentiality and fair reward settlement. We give game-based definitions and reduction-style arguments for seed confidentiality, worker soundness, outsourcer atomicity, and duplicate-claim resistance under an attested execution model. We implement a PrivFuzz prototype and evaluate it on four open-source parsing targets. Separately, native AFL++ sanity checks suggest that CPS-facing industrial protocol parsers such as Modbus and OPC UA fall within the same fuzzable target domain. Demonstrating end-to-end PrivFuzz on CPS control programs is left as future work. Using PrivFuzz, we discovered nine bugs and reported them to the developers. Full article

Networked Cyber–Physical Systems (CPSs), like autonomous quadrotor swarms, tightly couple continuous physical kinematics, wireless information exchange, and discrete real-time task scheduling. While conventional consensus security architectures focus exclusively on data-layer falsification, they fundamentally decouple adversarial behavior from onboard computational resource state profiles. This paper addresses a core CPS vulnerability termed Computational Weaponization, the deliberate injection of complex computational workloads (adversarial LLM token parsing or cryptographic verification) to intentionally manipulate hardware execution delays. Through this exploit, strategic cyber–physical perturbations force resource-constrained embedded microcontrollers to saturate their task execution queues, inducing real-time scheduling starvation and physical tracking divergence. To mitigate this without optimization bottlenecks, we present a state-dependent, Self-Triggered Control (STC) and Prospect Theoretic Alignment (PTA) co-design framework. The proposed protocol models the hardware microprocessor’s execution delay as an endogenous dynamic state coupled directly to continuous tracking spaces. By mapping discrete topology reconfigurations and variable task delays to a switched impulsive time-delay system, we leverage an Input-to-State Stability (ISS) to derive sufficient linear matrix inequality conditions. We prove that the coupled cyber–physical–computational loop achieves asymptotic consensus and bounded trajectory containment under adversarial actions. Full article

This study addresses an integrated security control and communication co-design problem for nonlinear CPSs subject to coexisting FDI attacks and actuator faults. A novel adaptive discrete event-triggered communication scheme (ADETCS) is proposed. Its triggering threshold adapts to the system state. State estimation, fault estimation, and attack detection are all migrated to the control unit. Based on this framework, a closed-loop T–S fuzzy model is established for active defense against actuator faults and dual-end FDI attacks. A robust augmented observer is then developed via Lyapunov stability theory to jointly estimate system states, actuator faults, and FDI attacks. Sufficient conditions are further derived for an integrated security controller that unifies attack tolerance and fault tolerance. Simulation results on a quadruple-tank system show that the proposed method effectively counteracts coexisting attacks and faults while significantly reducing resource consumption. Over an 800-s horizon, data transmissions drop to 712 (8.9% transmission rate). The sensor-node computational load is also reduced from 8000 time-triggered executions to 712 event-triggered ones. Full article

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values. Full article

State estimation is a fundamental component of model-based Fault Detection and Diagnosis (FDD) in dynamic systems, underpinning real-time monitoring, predictive maintenance, and safety-critical operations across industries such as aerospace, power systems, robotics, and autonomous vehicles. Traditional estimators, including the Kalman Filter (KF) and its variants, provide physically interpretable residuals for fault detection but often fail to deliver reliable performance under nonlinear dynamics, modeling uncertainties, sensor faults, and non-Gaussian noise. This paper presents a comprehensive review of state estimation-based FDD approaches, with a particular focus on Artificial Intelligence (AI)-augmented Kalman filtering and hybrid frameworks that integrate Machine Learning (ML) models, including Neural Networks (NNs), Support Vector Machines (SVMs), and Gaussian Processes (GPs), with classical estimation theory. The review systematically evaluates model-based, data-driven, and hybrid methods, comparing their robustness, accuracy, computational efficiency, scalability, and interpretability in complex Cyber-Physical Systems (CPSs). Furthermore, emerging trends and open research challenges are identified, including online adaptation, fault-tolerant estimation, sensor fusion, explainable artificial intelligence (XAI), and deployment in Industry 4.0 and Internet of Things (IoT)-enabled environments. By bridging classical estimation theory with modern AI techniques, this review provides a roadmap for designing intelligent, adaptive, and resilient FDD systems capable of enhancing reliability, operational safety, and real-world applicability. Full article

Field-Programmable Gate Array System-on-Chip (FPGA-SoC) platforms are increasingly adopted in modern industrial Cyber-Physical Systems (CPSs), enabling real-time control, monitoring, and automation of critical industrial processes. The increasing integration density of modern FPGA-SoC architectures introduces new thermal security challenges, where heat evolves from a reliability concern into a potential source of information leakage. Thermal Side-Channel Attacks (TSCAs) exploit runtime thermal variations to infer sensitive operational, architectural, or cryptographic information from the underlying hardware. While this study is centered on FPGA-SoC platforms, comparable thermal security challenges are increasingly reported across other densely integrated computing architectures, including Multiprocessor System-on-Chip (MPSoC), System-in-Package (SiP), and emerging Three-Dimensional Integrated Circuit (3D-IC) technologies. Consequently, the detection of thermal side-channel intrusions has become a critical hardware security challenge for next generation industrial CPS infrastructures. To address this challenge, an AI-enabled Digital Twin (DT) framework is introduced for TSCA detection in densely integrated FPGA-SoC microarchitectures. By combining thermal behavioral modeling, feature engineering, and machine learning-based anomaly detection, the proposed framework extends conventional Thermal Digital Twin (TDT) approaches beyond monitoring and mitigation toward autonomous thermal threat detection. The proposed framework is experimentally validated using an NI myRIO-1900 platform integrating a Xilinx Zynq-7010 FPGA-SoC representative of modern industrial embedded control architectures. Experimental results demonstrate the feasibility of the proposed framework, achieving an accuracy of approximately 75% with an Area Under the ROC Curve (AUC) of 0.76 using a lightweight Isolation Forest model. These results validate the capability of the proposed AI-enabled Digital Twin framework to learn normal thermal behavioral patterns and autonomously detect anomalous thermal activities potentially related to TSCAs. 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

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

Medication dispensing errors remain a significant concern in healthcare systems, particularly in elderly care and long-term medication management, where incorrect medication delivery may compromise patient safety and treatment outcomes. This study presents the design and experimental validation of a cyber–physical medication dispensing platform integrating robotic manipulation, edge AI-based visual verification, distributed motion control, and cloud synchronization. The platform combines a rotary medication storage mechanism, vacuum-based pill handling, a Klipper-based control framework, and a YOLOv8 perception subsystem deployed on a Hailo AI accelerator for real-time edge inference. Experimental evaluation was conducted under controlled laboratory conditions. Using an environment-specific validation dataset, the perception subsystem achieved a precision of 0.627, recall of 0.739, and mAP@0.5 of 0.786. An adaptive verification strategy was subsequently evaluated to improve dispensing verification under varying pill occupancy conditions. End-to-end system testing comprising 80 dispensing trials achieved an overall dispensing success rate of 86.25%, with no incorrect dispensing events observed. The results demonstrate the feasibility of integrating edge AI verification, distributed control, and cloud connectivity within a cyber–physical medication dispensing platform. The presented system provides a foundation for future research on perception-assisted medication dispensing, long-term deployment, and clinical validation in smart healthcare environments. Full article

AI-enabled cyber–physical systems (CPSs) are increasingly deployed in public governance contexts where they sense human populations, infer classifications or risks, and trigger interventions that can shape liberty, equality, and access to essential services. In these deployments, governance failures often arise not only from model error but from systems-level interactions across data generation, model updates, organizational practices, and downstream actuation. This paper introduces a Risk–Rights–Rules (3R) architecture that treats fundamental rights and legal rules as enforceable constraints on the sensing–inference–actuation loop, rather than as external ethical aspirations. Building on established risk-management baselines and safety engineering practice, we specify a testable assurance object, a structured 3R assurance case, that links rights claims to explicit assumptions, measurable evidence, and accountable control points across the lifecycle. The approach is designed to reduce “legitimacy drift” in stochastic decision pipelines by making uncertainty, demographic error, contestability, and procurement leverage auditable at the system level. The result is a governance blueprint for high-consequence public-sector AI deployments for governance failures, which is both technically robust and institutionally defensible. Full article

Cyber-physical systems (CPSs) integrate physical processes and information components through communication networks and are therefore vulnerable to cyber attacks. Opacity is a security property that prevents an adversary from inferring sensitive information from observations, and it has been studied mainly for discrete-event systems. In this paper, we extend this concept to discrete-time piecewise affine (DT-PWA) systems, which constitute an important class of hybrid systems used to model CPSs. In conventional opacity analysis, the result is typically binary, i.e., a system is either opaque or not. For systems with continuous dynamics, however, such a binary characterization may be insufficient, and it is desirable to evaluate the degree of security. To address this issue, we introduce a notion of opacity that incorporates security levels. We first formulate opacity for DT-PWA systems and then derive a necessary and sufficient condition for opacity. Based on this condition, we present a verification method using polytope computations and discuss the interpretation of the proposed notion. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. Full article

The escalating frequency of extreme weather events poses severe threats to power system security, often resulting in catastrophic economic and societal consequences. As modern information and communication technologies (ICTs) integrate deeply with power grids, post-disaster communication failures and electrical faults become increasingly interdependent, complicating the restoration of distribution cyber–physical systems (CPSs). To bridge the gap where conventional Unmanned Aerial Vehicle (UAV)-enabled emergency communication ignores coordination with power system restoration, this paper proposes a coordinated recovery method featuring a two-stage UAV deployment strategy. First, a coupled cyber–physical model is established to characterize the cross-layer interaction mechanisms. On this basis, a bi-level optimization framework is developed: the upper level formulates a dynamic two-stage UAV deployment strategy to minimize the mobilization of resources, while the lower level executes network topology reconfiguration to maximize weighted load restoration, constrained by the recovered communication coverage. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method significantly enhances restoration efficiency. Compared with conventional schemes, the cumulative load loss rate is reduced by 15.75% and 2.42% across different scenarios; the two-stage UAV deployment method achieves a time reduction of 67.23%, 21.40% and 71.56%, validating the superior performance of the coordinated recovery strategy in disaster-stricken CPS. 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

This paper presents a process-centric ontology for the semantic representation of cyber–physical systems (CPSs) within knowledge-based production planning (KPP). The approach integrates physical systems (PSs), cyber systems (CSs), and CPSs into a unified semantic model based on a three-layer classification. The ontology was implemented using OWL and integrated into a Neo4j-based graph architecture to support semantic querying and process modeling. The evaluation was conducted using prototypical manufacturing scenarios, including semiconductor and mechanical engineering domains. Validation included (i) consistency checking using the HermiT reasoner, (ii) execution of SPARQL queries for retrieving CPS-related process information, and (iii) integration into a three-stage planning model. The results show that the ontology enables consistent semantic representation and cross-domain querying of CPS-based production processes. The work provides a validated proof-of-concept and establishes a foundation for future research on ontology-based production systems. Full article