Search Results (179)

Intelligent transport systems are revolutionising all aspects of modern life, increasing the efficiency of commerce, modern living, and international travel. Intelligent transport systems are systems of systems comprised of cyber, physical, and social nodes. They represent unique opportunities but also have potential threats to system operation and correctness. The emergent behaviour in Complex Cyber–Physical–Social Systems (C-CPSSs), caused by events such as cyber-attacks and network outages, have the potential to have devastating effects to critical services across society. It is therefore imperative that the risk of cascading failure is minimised through the fortifying of these systems of systems to achieve resilient mission assurance. This work designs and implements a programmatic model to validate the value of cascading failure simulation and analysis, which is then tested against a C-CPSS intelligent transport system scenario. Results from the model and its implementations highlight the value in identifying both critical nodes and percolation of consequences during a cyber failure, in addition to the importance of including social nodes in models for accurate simulation results. Understanding the relationships between cyber, physical, and social nodes is key to understanding systems’ failures that occur because of or that involve cyber systems, in order to achieve cyber and system resilience. Full article

As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization. Full article

The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams—a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework’s effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems. Full article

As industries become increasingly dependent on cyber-physical systems (CPSs), failures within these systems can cause significant operational disruptions, underscoring the critical need for effective Prognostics and Health Management (PHM). The large volume of data generated by CPSs has made deep learning (DL) methods an attractive solution; however, imbalanced datasets and the limited availability of fault-labeled data continue to hinder their effective deployment in real-world applications. To address these challenges, this paper proposes a transfer learning approach using a pre-trained transformer architecture to enhance fault detection performance in CPSs. A streamlined transformer model is first pre-trained on a large-scale source dataset and then fine-tuned end-to-end on a smaller dataset with a differing data distribution. This approach enables the transfer of diagnostic knowledge from controlled laboratory environments to real-world operational settings, effectively addressing the domain shift challenge commonly encountered in industrial CPSs. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on publicly available datasets generated from a laboratory-scale replica of a modern industrial water purification facility. The results show that the model achieves an average F1-score of 93.38% under K-fold cross-validation, outperforming baseline models such as CNN and LSTM architectures, and demonstrating the practicality of applying transformer-based transfer learning in industrial settings with limited fault data. To enhance transparency and better understand the model’s decision process, SHAP is applied for explainable AI (XAI). Full article

The European Union (EU) is striving to achieve its goal of being climate-neutral by 2050. Aligned with the European Green Deal and in search of means to decarbonize its urban environments, the EU advocates for smart positive energy districts (PEDs). PEDs contribute to the United Nations’ (UN) sustainable development goals (SDGs) of “Sustainable Cities and Communities”, “Affordable and Clean Energy”, and “Climate Action”. PEDs are urban neighborhoods that generate renewable energy to a higher extent than they consume, mainly through the utilization of innovative technologies and renewable energy sources. In accordance with the EU 2050 aim, the PED concept is attracting growing research interest. PEDs can transform existing energy systems and aid in achieving carbon neutrality and sustainable urban development. PED is a novel concept and its implementation is challenging. This study aims to present the emerging technologies enabling the proliferation of PEDs by identifying the main challenges and potential solutions to effective adoption and implementation of PEDs. This paper examines the importance and utilization of cyber-physical systems (CPSs), digital twins (DTs), artificial intelligence (AI), the Internet of Things (IoT), edge computing, and blockchain technologies, which are all fundamental to the creation of PEDs for enhancing energy efficiency, sustainable energy, and user engagement. These systems combine physical infrastructure with digital technologies to create intelligent and autonomous systems to optimize energy production, distribution, and consumption, thus positively contributing to achieving smart and sustainable development. Full article

Simulation-based development is a structured approach that uses formal models to design and test system behavior before building the actual system. The Internet of Things (IoT) connects physical devices equipped with sensors and software to collect and exchange data. Cyber-Physical Systems (CPSs) integrate computing directly into physical processes to enable real-time control. This paper reviews the Discrete-Event System Specification (DEVS) formalism and explores how it can serve as a unified framework for designing, simulating, and implementing systems that combine IoT and CPS—referred to as the Internet of Cyber-Things (IoCT). Through case studies that include home automation, solar energy monitoring, conflict management, and swarm robotics, the paper reviews how DEVS enables construction of modular, scalable, and reusable models. The role of the System Entity Structure (SES) is also discussed, highlighting its contribution in organizing models and generating alternative system configurations. With this background as basis, the paper evaluates whether DEVS provides the necessary modeling power and continuity across stages to support the development of complex IoCT systems. The paper concludes that DEVS offers a robust and flexible foundation for developing IoCT systems, supporting both expressiveness and seamless transition from design to real-world deployment. Full article

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human and organizational dynamics, leaving critical gaps in how cyber risks are assessed and managed across interconnected domains. The challenge lies in building resilient systems that not only resist disruption, but also absorb, recover, and adapt—especially in the face of complex, nonlinear, and often unintentionally emergent threats. This paper introduces the concept of ‘responsible resilience’, defined as the capacity of systems to adapt to cyber risks using trustworthy, transparent agent-based models that operate within socio-technical contexts. We identify a fundamental research gap in the treatment of social complexity and emergence in existing the cyber–physical system literature. To address this, we propose the E3R modeling paradigm—a novel framework for conceptualizing Emergent, Risk-Relevant Resilience in C-CPSS. This paradigm synthesizes human-in-the-loop diagrams, agent-based Artificial Intelligence simulations, and ontology-driven representations to model the interdependencies and feedback loops driving unpredictable cyber risk propagation more effectively. Compared to conventional cyber–physical system models, E3R accounts for adaptive risks across social, cyber, and physical layers, enabling a more accurate and ethically grounded foundation for cyber defence and mission assurance. Our analysis of the literature review reveals the underrepresentation of socio-emergent risk modeling in the literature, and our results indicate that existing models—especially those in industrial and healthcare applications of cyber–physical systems—lack the generalizability and robustness necessary for complex, cross-domain environments. The E3R framework thus marks a significant step forward in understanding and mitigating emergent threats in future digital ecosystems. Full article

Recent advances in technologies such as blockchain, the Internet of Things (IoT), Cyber–Physical Systems (CPSs), and the Industrial Internet of Things (IIoT) have driven the digitalization and intelligent transformation of modern industries. However, embedded control devices within power system communication infrastructures have become increasingly susceptible to cyber threats due to escalating software complexity and extensive network exposure. We have seen that symmetric conventional patching techniques—both static and dynamic—often fail to satisfy the stringent requirements of real-time responsiveness and computational efficiency in resource-constrained environments of all kinds of power grids. To address this limitation, we have proposed a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method has integrated binary-level vulnerability modeling, execution-trace-driven fault localization, and lightweight patch synthesis, enabling dynamic, in-place code redirection without disrupting ongoing operations. By constructing a system-level instruction flow model, the framework has leveraged on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture have revealed that the proposed approach has reduced patch latency by 92% over static techniques, which are symmetrical in a working way, while incurring less than 3% CPU overhead. This work has offered a scalable and real-time model-driven defense strategy that has enhanced the cyber–physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability. Full article

Industrial Control Systems (ICSs), a specialized type of Cyber–Physical System, have shifted from isolated and obscured environments to ones exposed to diverse Information Technology (IT) security threats, which are now highly interconnected. Their adoption of IT introduces vulnerabilities which they were not originally designed to handle, posing critical risks. Thus, it’s imperative to integrate security measures early in CPS development, particularly during the design and implementation phases, to mitigate these vulnerabilities effectively. This study aims to identify, classify, and analyze existing research on the security-by-design paradigm for CPSs, exploring trends and defining the characteristics, advantages, limitations, and open issues of current methodologies. A systematic mapping study was conducted, selecting 55 primary studies through a rigorous protocol. The findings indicate that the majority of methodologies concentrate on the design phase, frequently overlooking other stages of development. Moreover, while there is a notable emphasis on security analysis across most primary studies, there is a notable gap in considering the integration of mitigation measures. This oversight raises concerns about the efficacy of security measures in real-world deployment scenarios. Additionally, there is a significant reliance on human intervention, highlighting the need for further development in automated security solutions. Conflicts between security requirements and other system needs are also inadequately addressed, potentially compromising overall system effectiveness. This work provides a comprehensive overview of CPS security-by-design methodologies and identifies several open issues that require further investigation, emphasizing the need for a holistic approach that includes vulnerability handling, clear security objectives, and effective conflict management, along with improved standard integration, advanced validation methods, and automated tools. Full article

(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, restricting the implementation of complex countermeasures. Traditional countermeasures, such as hiding techniques, attempt to obscure power consumption patterns; however, their effectiveness has been increasingly challenged. This study evaluates the vulnerability of dummy power traces against deep learning-based SCAs (DL-SCAs). (2) Methods: A power trace dataset was generated using a simulation environment based on Quick Emulator (QEMU) and GNU Debugger (GDB), integrating dummy traces to obfuscate execution signatures. DL models, including a Recurrent Neural Network (RNN), a Bidirectional RNN (Bi-RNN), and a Multi-Layer Perceptron (MLP), were used to evaluate classification performance. (3) Results: The models trained with dummy traces achieved high classification accuracy, with the MLP model reaching 97.81% accuracy and an F1-score of 97.77%. Despite the added complexity, DL models effectively distinguished real and dummy traces, highlighting limitations in existing hiding techniques. (4) Conclusions: These findings highlight the need for adaptive countermeasures against DL-SCAs. Future research should explore dynamic obfuscation techniques, adversarial training, and comprehensive evaluations of broader cryptographic algorithms. This study underscores the urgency of evolving security paradigms to defend against artificial intelligence-powered attacks. Full article

As cyber–physical systems are applied not only to crucial infrastructure but also to day-to-day technologies, from industrial control systems through to smart grids and medical devices, they have become very significant. Cyber–physical systems are a target for various security attacks, too; their growing complexity and digital networking necessitate robust cybersecurity solutions. Recent research indicates that deep learning can improve CPS security through intelligent threat detection and response. We still foresee limitations to scalability, data privacy, and handling the dynamic nature of CPS environments in existing approaches. We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. We will present a model that integrates state-of-the-art methodologies in both federated learning and optimization paradigms through the combination of the Federated Residual Convolutional Network (FedRCNet) and the EEL-Levy Fusion Optimization (ELFO) methods. This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. This preserves data privacy through decentralized model training and improves complex security threat detection. We report the results of a rigorous evaluation of CICIoT-2023, Edge-IIoTset-2023, and UNSW-NB datasets containing the CPS ShieldNet Fusion model at the forefront in terms of accuracy and effectiveness against several threats in different CPS environments. Therefore, these results underline the potential of the proposed framework to improve CPS security by providing a robust and scalable solution to current problems and future threats. Full article

An important step toward automation and digitization in Industry 4.0 is the automobile sector’s use of smart manufacturing integrated systems (SMISs). Although this change increases productivity and competitiveness, it also creates new hazards for workplace safety. Key issues include ergonomic and cognitive strain from greater human–machine interactions, particularly with collaborative robots (cobots), and cybersecurity threats from the IIoT and cyber–physical systems. This paper looks at these hazards and stresses the value of safety precautions like predictive maintenance, traceability, and real-time monitoring. This case study investigates how the integration of smart manufacturing integrated systems (SMISs) and cyber–physical systems (CPSs) within Industry 4.0 frameworks enhances workplace safety in the automotive sector. Through a comprehensive case study of the final assembly line, this research explores how these technologies contribute to predictive maintenance, real-time monitoring, and human–machine collaboration, leading to significant reductions in ergonomic and cybersecurity risks. Full article

This paper investigates the problem of interval estimation and fault detection for nonlinear cyber–physical systems (CPSs) subject to disturbances and random actuator/sensor faults. First, with the purpose of reducing the burden of data transmission, we introduce the dynamic event-triggered mechanism (DETM). For systems violating the non-negativity condition, a coordinate transformation method is applied to enhance the design flexibility. Then, the dynamic event-triggered interval observer (DETIO) is constructed and the effectiveness of DETIO is validated by demonstrating the ultimate uniform boundedness of the error system. The fault detection is achieved by considering faults in the CPSs and continuously monitoring residual signals. Finally, two numerical simulations under both faulty and fault-free conditions are shown to prove the effectiveness and superiority of the designed DETIO-based fault detection method. Full article

Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. Integrated IoT devices directly exchange data with the SG cloud, which increases the vulnerability and security threats to the energy system. The review aims to provide a comprehensive analysis of privacy-preserving machine learning (PPML) applications in IoT-Integrated SGs, focusing on non-intrusive load monitoring, fault detection, demand forecasting, generation forecasting, energy-management systems, anomaly detection, and energy trading. The study also highlights the importance of data privacy and security when integrating these applications to enable intelligent decision-making in smart grid domains. Furthermore, the review addresses performance issues (e.g., accuracy, latency, and resource constraints) associated with PPML techniques, which may impact the security and overall performance of IoT-integrated SGs. The insights of this study will provide essential guidelines for in-depth research in the field of IoT-integrated smart grid privacy and security in the future. Full article

Cyber-physical systems (CPSs) are fundamental components of Industry 4.0 production environments. Their interconnection is crucial for the successful implementation of distributed and autonomous production plans. A particularly relevant challenge is the optimal scheduling of tasks that require the collaboration of multiple CPSs. To ensure the feasibility of optimal schedules, two primary issues must be addressed: (1) The design of global systems emerging from the interconnection of CPSs; (2) The development of a scheduling formalism tailored to interconnected Industry 4.0 settings. Our approach is based on a Category Theory formalization of interconnections as compositions. This framework aims to guarantee that the emergent behaviors align with the intended outcomes. Building upon this foundation, we introduce a formalism that captures the assignment of operations to cyber-physical systems. Full article