Search Results (485)

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

The rapid evolution of cyberattacks, coupled with the increasing capacity of computing environments and the emergence of artificial intelligence (AI), has significantly complicated the security landscape. While existing studies largely emphasize improving AI model performance for individual cybersecurity tasks, this survey shifts the focus toward operationalizing the deployment rationale and understanding when, where, and why different AI paradigms should be deployed, the capabilities they offer; and the challenges that must be addressed to enable trustworthy and effective real-world cyber defense. This paper aims to provide researchers and practitioners with a comprehensive reference for understanding the evolving role of AI in cybersecurity and the challenges that must be addressed to develop trustworthy and resilient AI-driven cyber defense systems. In this paper, we propose a structured taxonomy to organize various dimensions of AI-driven cybersecurity; review them critically; and finally, discuss key challenges, open problems, and emerging trends. Full article

UAV-assisted cyber–physical–social systems increasingly use visual analytics for disaster response, infrastructure monitoring, traffic management, and safety-oriented situational awareness. Raw aerial imagery can expose faces, license plates, private-property context, location traces, and sensitive human activity, while cloud-based vision–language processing can increase latency, bandwidth use, and governance risk. This article presents PrivEdge-VLM, a risk-adaptive privacy-preserving edge vision–language analytics framework for UAV-assisted cyber–physical–social systems. PrivEdge-VLM integrates object detection, privacy-sensitive region detection, mission-aware semantic tokenization, local inference, sanitized split inference, federated LoRA adaptation with differential privacy and simulated masked aggregation, and deterministic operator guardrails. Across public UAV imagery, public privacy benchmarks, and non-human staged object protocol examples, the evaluation compares raw-cloud, downsampling, static-blur, local-VLM, raw-embedding split, federated static-privacy, no-DP, and full configurations. The full configuration preserves mission utility while reducing residual visual PII, plate-OCR leakage, membership-inference risk, embedding inversion, bandwidth, and unsafe identity-oriented responses under the stated attack protocols. The edge hardware deployment separates privacy-front-end throughput from event-level VLM query latency. The results indicate that UAV VLM privacy is a CPS co-design problem involving semantic transformation, attack-based leakage evaluation, resource-aware deployment, and operator oversight. 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 convergence of cyber-physical systems (CPS) with operational technology (OT) and critical infrastructure (CI) in an emerging quantum computing landscape exposes industrial operators to a twofold gap: the absence of semantic evaluation mechanisms to assess the alignment of a cybersecurity reference model with current regulatory frameworks, and the lack of instruments that systematically incorporate the post-quantum transition as a core evaluation dimension. This article presents a Systematic Literature Review (SLR) examining studies published between 2020 and 2026 on standards, practices, and ontological evaluation models for the cybersecurity of CPS and CI in the quantum era. The protocol combined PRISMA 2020, Kitchenham & Brereton, and the GQM framework with a standardized quality instrument. A total of 116 primary studies from seven sources were analyzed. Within the reviewed corpus, no study was found that simultaneously addressed the ontological and quantum dimensions (n = 0), indicating a structural gap between these two research lines. From the corpus, seven key requirements were derived to guide the future development of the Quantum Ontological Cybersecurity Evaluation Model (QOCEM), intended to support the semantic evaluation of industrial reference models ahead of the post-quantum transition. Full article

Autonomous Cyber–Physical Systems (CPS) must jointly satisfy energy efficiency, accuracy, and real-time constraints, which are typically treated separately in existing methods. This paper proposes a verifiable service-oriented CPS framework for energy-aware autonomous navigation using a high-fidelity cyber–physical twin. The approach integrates physics-based Model Predictive Control (MPC) with explicit power modeling ($P=F·v$) and Dubins curve-based trajectory generation under the 5C (connection, conversion, cyber, cognition, and configuration) architecture using CARLA for synchronized cyber–physical interaction. The proposed method achieves 30.7% reduction in mean power consumption and 12.5% reduction in total energy usage while maintaining sub-centimeter tracking error (<0.05 m). Mission duration increases by 26.3% with only 7% computational overhead, confirming real-time feasibility. The framework provides a verifiable CPS methodology that unifies physics-based control, digital twin synchronization, and service-oriented design for energy-aware autonomous navigation. Full article

A new generation of smart buildings and precision agriculture is evolving through the integration of cyber–physical systems (CPS), which combine IoT sensors with machine learning (ML). As such, there is an implicit assumption made by researchers in most of these studies that the ML component represents the decision making mechanism within the overall system. Furthermore, most researchers do not articulate the full scope of the cyber–physical feedback loop linking prediction outputs, operational decisions based upon those predictions, actual actuation of the physical plant or farm operation, and subsequent performance evaluations. The outcome of this paper brings out transferable decision support patterns across domains such as the mechanisms which have proven to be effective in scenarios with low number or quality of data measurements. Specifically, we present a review for two CPS domains that benefit intensely from decision support: smart buildings and precision agriculture. We examined how sensing, data processing, ML, and control modules are combined in practice when creating decision support applications. This resulted in a review of the literature to identify architectural patterns, decision objectives, and feedback mechanisms in both domains. This combination insight paves the way for more flexible and more effective decision making applications compatible with different domains. Full article

The transition from traditional siloed to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart city as an urban cognitive Cyber–Physical System (CPS) across environmental, economic, and social sustainability pillars, metrics and their trade-offs. A methodology based on Deep Reinforcement Learning (DRL), specifically adopting Deep Q-Networks (DQNs), is proposed to represent and assess sustainability pillar dependencies and their interplay. A case study on Low-Power Wide-Area Network planning, deployment and management in a Sicilian municipality has been developed to demonstrate the effectiveness of the proposed approach in dealing with the dynamics and non-linear dependencies of the sustainability pillars. 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 (CPS), including smart grids and industrial control networks, must maintain secure and stable operations despite increasingly adaptive cyber threats. Existing moving target defense (MTD) approaches often rely on fixed reconfiguration strategies or flat learning architectures that fail to scale and do not explicitly ensure operational resilience under real-time constraints. This study proposes a resilience-oriented hierarchical multi-agent reinforcement learning (MARL) framework for adaptive MTD in CPS environments. The attacker–defender interaction is modeled as a partially observable stochastic game, enabling defenders to learn adaptive strategies with incomplete information. The proposed architecture consists of three layers: a strategic MARL layer that optimizes high-level defense parameters, a distributed k-winner-take-all coordination layer for low-latency defender selection, and a robust execution layer based on sliding-mode control to preserve physical system stability during reconfiguration. By decoupling strategic adaptation from real-time control, the framework improves scalability and supports resource-aware defense through selective agent activation. Extensive simulations with up to 50 defender agents demonstrate that the proposed approach achieves a defense success rate of 92.4%, reduces the response time by 15% compared with the random MTD, and lowers the energy consumption by 34% on average (up to 52% at N = 50) relative to the flat MARL. These results indicate that hierarchical MARL can significantly enhance CPS resilience by enabling adaptive, efficient, and operationally safe defenses against dynamic cyber-attacks. The proposed framework is particularly suitable for edge-enabled CPS environments with strict, real-time, and safety constraints. Full article

Dataset quality significantly affects the effectiveness of a machine learning (ML) model in an intrusion detection system (IDS) for cyber-physical industrial control systems (CPS/ICS) and Industrial Internet of Things (IIoT). Existing surveys compare datasets qualitatively or along limited dimensions, whereas this review introduces quantitative documentation and decision-aid scoring across 23 ICS/OT/IIoT datasets. These datasets are analyzed along seven measurable axes, with their attacks mapped to MITRE ATT&CK for ICS tactics. Quantitatively, 14 of the 23 datasets (60.9%) are built on physical testbeds, and 22 of the 23 map to MITRE ATT&CK for ICS, spanning 11 of the 12 tactics. We introduce a checklist for documentation completeness (0–7) and a decision-aid rubric (0–15) covering realism, attack diversity, class imbalance, documentation, and reproducibility. Protocol coverage across these datasets is skewed toward Modbus (13 of 23 datasets, 57%), while many other protocols (such as Profinet and OPC UA) are underrepresented relative to their industry deployment. The available datasets show structural gaps in capturing multi-stage adversary behavior. In practice, dataset selection should pair a realism-anchored dataset with a high-reproducibility one, and account for protocol diversity and APT representation. Full article

Electromyographic (EMG) signals are increasingly used in wearable cyber–physical systems (CPS), where reliable movement recognition must be achieved under limited computational resources. In this study, we present a compact EMG processing framework that integrates signal acquisition, preprocessing, segmentation, and movement classification within a unified pipeline designed for embedded-oriented applications. The proposed approach combines a multi-channel EMG acquisition system with a lightweight one-dimensional convolutional neural network (1D CNN) developed according to TinyML principles, withprocessing input windows of size 32 × 3 and low computational complexity and memory requirements. Experimental evaluation was conducted on a dataset collected from 15 participants performing squat, walking, and running activities under realistic acquisition conditions. The proposed model achieved an accuracy of 0.9135, an F1-score of 0.9124, and a ROC AUC of approximately 0.96, demonstrating reliable classification performance. Following 8-bit quantization, the model size was reduced to approximately 2 KB, supporting deployment on resource-constrained embedded platforms. The results show that compact CNN architectures can effectively classify EMG-based movement patterns while maintaining a small computational footprint, providing a practical foundation for future wearable CPS and TinyML-enabled applications. 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

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