Accurate Modeling Method of Power System Load Based on Online Measurement under CPS Environment

Recently, with the growth of cyber physical systems (CPS), several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively. Besides, the cloud computing (CC) enabled CPS offers huge processing and storage resources for CPS that finds helpful for a range of application areas. At the same time, with the massive development of applications that exist in the CPS environment, the energy utilization of the cloud enabled CPS has gained significant interest. For improving the energy effectiveness of the CC platform, virtualization technologies have been employed for resource management and the applications are executed via virtual machines (VMs). Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS, this paper focuses on the design of chaotic sandpiper optimization based VM scheduling (CSPO-VMS) technique for energy efficient CPS. The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy. The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory, which substitutes the main parameter and combines it with the chaos. In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm, the chaotic concept is included in the SPO algorithm. The CSPO-VMS technique also derives a fitness function to choose optimal scheduling strategy in the CPS environment. In order to demonstrate the enhanced performance of the CSPO-VMS technique, a wide range of simulations were carried out and the results are examined under varying aspects. The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

Cyber physical system (CPS) is a network of cyber and physical elements, which interact with one another in a feedback form. CPS approves critical infrastructure and is treated as essential in day to day since it forms the basis of futuristic smart devices. An increased usage of CPSs poses security as a challenging issue and intrusion detection systems (IDS) can be applied for the identification of network intrusions. The latest advancements in the field of artificial intelligence (AI) and deep learning (DL) enables to design effective IDS models for the CPS environment. At the same time, metaheuristic algorithms can be employed as a feature selection approach in order to reduce the curse of dimensionality. With this motivation, this study develops a novel Poor and Rich Optimization with Deep Learning Model for Blockchain Enabled Intrusion Detection in CPS Environment, called PRO-DLBIDCPS technique. The proposed PRO-DLBIDCPS technique initially introduces an Adaptive Harmony Search Algorithm (AHSA) based feature selection technique for proper selection of feature subsets. For intrusion detection and classification, and attention based bi-directional gated recurrent neural network (ABi-GRNN) model is applied. In addition, the detection efficiency of the ABi-GRNN technique has been enhanced by the use of Poor and rich optimization (PRO) algorithm based hyperparameter optimizer, which resulted in enhanced intrusion detection results. Furthermore, blockchain technology is applied for enhancing security in the CPS environment. In order to demonstrate the enhanced outcomes of the PRO-DLBIDCPS technique, a wide range of simulations was carried out on benchmark dataset and the results reported the better outcomes of the PRO-DLBIDCPS technique in terms of several measures.

Cyber Physical Systems (CPS) are redefining modern engineering practices by bringing the computational systems together with physical processes, boosting efficiency, security, and sustainability in different industries. This research highlights the influence of CPS in different sectors: its power to facilitate realtime decision making, predictive maintenance, and cybersecurity issues. The context in which this study occurs is to the application of digital twin technology in CPS — which is physical systems’ simulation for performance optimization and downtime reduction. The research includes the analysis of the efficiency of cybersecurity measures applied in CPS environment, the identification of vulnerabilities, and the suggestions of solutions for the mitigation of a cyber threat. Digital twins and predictive maintenance appears to improve the system performance by 30%, reduces the downtime by 25% due to application. Moreover, the research addresses upcoming concerns of sustainable practices in CPS and emerging technologies such as Industry 4.0 that promote resource optimization and waste reduction. The results show that CPS can improve both the operational efficiency and the environmental sustainability by enabling a smarter resource management. This work advances our understanding of trends, challenges and future directions in CPS as a means to inform engineering management practices, which are enabled by CPS.

Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication. In this aspect, this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder (XAIIDS-FSDVAE) model for CPS. The proposed model encompasses the design of coyote optimization algorithm (COA) based feature selection (FS) model is derived to select an optimal subset of features. Next, an intelligent Dirichlet Variational Autoencoder (DVAE) technique is employed for the anomaly detection process in the CPS environment. Finally, the parameter optimization of the DVAE takes place using a manta ray foraging optimization (MRFO) model to tune the parameter of the DVAE. In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique, a wide range of simulations take place using the benchmark datasets. The experimental results reported the better performance of the XAIIDS-FSDVAE technique over the recent methods in terms of several evaluation parameters.

Online measurements can be used for aggregate load modeling, taking advantage of the advent of smart grids. In this paper initial load modeling results are presented using measurements recorded in a laboratory scale microgrid. The examined microgrid configuration includes a load bank and an adjustable speed drive. A methodology for the identification of the model parameters from measurements is presented and the performance of a static and a dynamic load model is investigated in grid-connected and islanded operation modes. Numerical studies indicate that the dynamic model behaves satisfactorily enough in both modes of operation, while the static model can only be used in the islanded case. Generic load models suitable to simulate the real and reactive power for a wide range of load disturbances are proposed and their accuracy is evaluated using the cross-validation technique.

IntroductionThe increasing complexity of cyber-physical systems (CPS) demands robust and efficient action recognition frameworks capable of seamlessly integrating multi-modal data. Traditional methods often lack adaptability and perform poorly when integrating diverse information sources, such as spatial and temporal cues from diverse image sources.MethodsTo address these limitations, we propose a novel Multi-Scale Attention-Guided Fusion Network (MSAF-Net), which leverages advanced image fusion techniques to significantly enhance action recognition performance in CPS environments. Our approach capitalizes on multi-scale feature extraction and attention mechanisms to dynamically adjust the contributions from multiple modalities, ensuring optimal preservation of both structural and textural information. Unlike conventional spatial or transform-domain fusion methods, MSAF-Net integrates adaptive weighting schemes and perceptual consistency measures, effectively mitigating challenges such as over-smoothing, noise sensitivity, and poor generalization to unseen scenarios.ResultThe model is designed to handle the dynamic and evolving nature of CPS data, making it particularly suitable for applications such as surveillance, autonomous systems, and human-computer interaction. Extensive experimental evaluations demonstrate that our approach not only outperforms state-of-the-art benchmarks in terms of accuracy and robustness but also exhibits superior scalability across diverse CPS contexts.DiscussionThis work marks a significant advancement in multi-modal action recognition, paving the way for more intelligent, adaptable, and resilient CPS frameworks. MSAF-Net has strong potential for application in medical imaging, particularly in multi-modal diagnostic tasks such as combining MRI, CT, or PET scans to enhance lesion detection and image clarity, which is essential in clinical decision-making.

Healing Process is a major role in developing resiliency in cyber-physical system where the environment is diverse in nature. Cyber-physical system is modelled with Multi Agent Paradigm and biological inspired Danger Theory based-Artificial Immune Recognization2 Algorithm Methodology towards developing healing process. The Proposed methodology is implemented in a simulation environment and percentage of Convergence rates shown in achieving accuracy in the healing process to resiliency in cyber-physical system environment is shown.

Cyber-Physical Systems (CPS) are increasingly deployed in critical infrastructure yet remain vulnerable to adversarial attacks that manipulate sensor data to mislead AI-based decision-making. These threats demand not only high-accuracy detection but also transparency in model reasoning. This study proposes a Hybrid Explainable AI (XAI) Framework that integrates Convolutional Neural Networks (CNN), SHAP-based feature interpretation, and rule-based reasoning to detect adversarial inputs in CPS environments. The framework is tested on two simulation scenarios: industrial sensor networks and autonomous traffic sign recognition. Using datasets of 10,000 samples (50% adversarial via FGSM and PGD), the model achieved an accuracy of 97.25%, precision of 96.80%, recall of 95.90%, and F1-score of 96.35%. SHAP visualizations effectively distinguished between normal and adversarial inputs, and the added explainability module increased inference time by only 8.5% over the baseline CNN (from 18.5 ms to 20.1 ms), making it suitable for real-time CPS deployment. Compared to prior methods (e.g., CNN + Grad-CAM, Random Forest + LIME), the proposed hybrid framework demonstrates superior performance and interpretability. The novelty of this work lies in its tri-level integration of predictive accuracy, explainability, and rule-based logic within a single real-time detection system—an approach not previously applied in CPS adversarial defense. This research contributes toward trustworthy AI systems that are robust, explainable, and secure by design.

Cyber-physical Systems (CPS) have raised serious security concerns and thus have been subjected to intensive security research lately. Recent publications have shown that there is a potential to transfer hidden information through CPS environments. In comparison to these existing studies, we demonstrate that CPS cannot only be used to covertly transfer secret data but also to store secret data. Using an analogy to the biological concept of animal scatter hoarding behavior we exemplify CPS secret data storage using automated buildings.1

The security of Cyber-Physical Systems (CPS) has been recently receiving significant attention from the research community. To this end, this paper sheds the light on a number of security approaches for CPS from two perspectives, namely, control-theoretic and cyber security. Further, threat detectors in various CPS environments are highlighted and discussed. The aim is to demonstrate the lack of coherent approaches that systematically tackle both security aspects of such systems, in addition to pinpointing several insightful research gaps that endeavor to shape future CPS security solutions.

Chaotic marine predators optimization based task scheduling scheme for resource limited cyber-physical systems

Optimal feature selection for malware detection in cyber physical systems using graph convolutional network

With the wide use of Cyber-Physical Systems (CPS) in many applications, targets of advanced persistent threats (APTs) have been extended to the IoT and industrial control systems. Provenance graph analysis based on system audit logs has become a promising way for APT detection and investigation. However, we cannot afford to ignore that existing provenance-based APT detection systems lack the process–context information at system runtime, which seriously limits detection performance. In this paper, we proposed ConGraph, an approach for detecting APT attacks using provenance graphs combined with process context; we presented a module for collecting process context to detect APT attacks. This module collects file access behavior, network access behavior, and interactive relationship features of processes to enrich semantic information of the provenance graph. It was the first time that the provenance graph was combined with multiple process–context information to improve the detection performance of APT attacks. ConGraph extracts process activity features from the provenance graphs and submits the features to a CNN-BiLSTM model to detect underlying APT activities. Compared to some state-of-the-art models, our model raised the average precision rate, recall rate, and F-1 score by 13.12%, 25.61%, and 24.28%, respectively.

Guest Editorial

In a large-scale transmission network, such as the European power system, the regional characteristics of electrical demand may have an impact on local voltage stability. The dynamic load behaviour depends on the sectoral composition of demand, which is subject to regional and seasonal variation. An aggregate load model is presented, which accounts for the sectoral characteristics of 210 European regions for all hours a year from the perspective of the high, medium, and low voltage levels. The proposed load model is used to better understand the influence of regional characteristics on voltage recovery during a short-circuit event, as well as the static point of voltage collapse. Several countries are considered in terms of their sectoral composition, to represent intermediate regions or such dominated by the industry and households. Moreover, the dynamic load model is benchmarked against two static load models, assuming a constant power (PQ) load or a constant impedance (Z) load. The conducted study demonstrates that regional differences in the composition of demand highly impact local voltage stability.