Artificial Intelligence and Machine Learning in Cyber–Physical Systems

Topic Information

Dear Colleagues,

We are pleased to invite you to submit your work to the Special Topic titled “Artificial Intelligence and Machine Learning in Cyber–Physical Systems”, which will be published in Future Internet, Eng, Sensors, Electronics, Information, Sustainability, and AI. This Topic aims to integrate AI and ML techniques into Cyber–Physical Systems (CPS). These systems, which bridge the physical and digital worlds, are critical in domains such as manufacturing, healthcare, transportation, and smart cities. AI and ML enhance CPS by enabling real-time data analysis, decision making, and automation, thus increasing the intelligence and adaptability of such systems. With the increasing need for green technology, sustainable solutions, such as reducing energy consumption and promoting resource-efficient designs, will also be welcomed. This Special Topic aims to explore cutting-edge research and applications that address the challenges and opportunities in this field. In this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: 1. AI-driven optimization and control in CPS; 2. Security and privacy in AI-powered CPS; 3. Resource-aware AI algorithms for green CPS; 4. AI-enabled solutions for sustainable smart cities and societies; 5. Applications of deep learning in autonomous CPS; 6. Predictive maintenance and fault diagnosis in CPS; 7. AI and ML for smart manufacturing and Industry 4.0; 8. AI-enabled IoT and edge computing in CPS. We look forward to receiving your contributions.

Dr. Wei Wang
Dr. Junxin Chen
Dr. Jinyu Tian
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Eng eng	2.4	3.2	2020	18 Days	CHF 1400	Submit
Future Internet futureinternet	3.6	8.3	2009	16.1 Days	CHF 1800	Submit
Information information	2.9	6.5	2010	20.9 Days	CHF 1800	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400	Submit

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Published Papers (7 papers)

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All Electronics Eng Future Internet Information Sensors Sustainability AI

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38 pages, 6153 KB  

Open AccessReview

Machine Learning Strategies for Power Grid Resilience: A Functional and Bibliometric Review

by Cesar A. Vega Penagos, Omar F. Rodriguez-Martinez, Jan L. Diaz, Guiselle A. Feo-Cediel, Adriana C. Luna and Fabio Andrade

Electronics 2026, 15(10), 2001; https://doi.org/10.3390/electronics15102001 - 8 May 2026

Viewed by 280

Abstract

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper [...] Read more.

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper presents a structured review of ML strategies for power-grid resilience applications using a four-phase resilience lens (Prevention and Improvement, Control and Mitigation, Restoration, and Cyber Resilience). The literature is organized through a functional taxonomy that includes fault diagnosis, event prediction, control and stability support, restoration, and cyber resilience. In addition to the qualitative synthesis, a quantitative analysis of a dataset of 13,647 peer-reviewed publications (2015–2026) is conducted to characterize research activity across resilience functions and implementation contexts. This analysis is used to illustrate the increasing adoption of machine learning approaches and to distinguish between simulation-based and real-world applications. The results indicate a methodological shift toward Deep Learning and Reinforcement Learning for complex tasks, while federated and edge-based approaches are gaining attention for privacy preserving and real-time applications. These findings provide a structured view of current research directions and support the growing relevance of machine learning in resilience-oriented power system applications, offering a foundation for the development of intelligent and scalable cyber–physical energy systems. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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28 pages, 15581 KB  

Open AccessArticle

The Multifunctionality of Rural Backyards: Producing Healthy Food Through Sustainable Practices in Households—A Study in Guasave, Sinaloa, Mexico

by Víctor Manuel Peinado-Guevara, Mary Cruz Sánchez-Alcalde, Griselda Karina González-Félix, Héctor José Peinado-Guevara, Jaime Herrera-Barrientos, Jesús Alberto Peinado-Guevara, Luz Isela Peinado-Guevara and Mónica Meneses Soto

Sustainability 2026, 18(6), 2691; https://doi.org/10.3390/su18062691 - 10 Mar 2026

Viewed by 507

Abstract

Backyard activities (BAs) constitute a traditional rural food production strategy, although their sustainability-related outcomes remain underexplored in rural contexts. Our objective is to analyze how BAs in rural communities in the municipality of Guasave, Sinaloa, Mexico, represent a local strategy for producing healthy [...] Read more.

Backyard activities (BAs) constitute a traditional rural food production strategy, although their sustainability-related outcomes remain underexplored in rural contexts. Our objective is to analyze how BAs in rural communities in the municipality of Guasave, Sinaloa, Mexico, represent a local strategy for producing healthy food through sustainable practices, focusing on the relationship between production processes and indicators associated with waste management, water use, and environmental impact. A stratified sampling approach was employed, and a structured survey was administered to 387 households practicing BAs in the region. Instrument consistency was verified using the Kuder–Richardson coefficient. Results show that BAs are relevant in the region: 89.15% of respondents have a family garden and 67.96% raise animals for domestic consumption, supporting an additional income. Chi-square tests revealed statistically significant correlations (p < 0.05) among the study variables, ranging from moderate to strong, with backyard animal husbandry practices standing out in relation to nutritional and health management in animal production, reaffirming that these are not isolated events but complementary activities. These findings indicate that BAs involve interconnected systems rather than isolated activities, reflecting an integrated household-level system with potential implications for resource management and environmental sustainability in rural contexts. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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27 pages, 2905 KB  

Open AccessArticle

A Hybrid Machine Learning Approach for Cyberattack Detection and Classification in SCADA Systems: A Hydroelectric Power Plant Application

by Mehmet Akif Özgül, Şevki Demirbaş and Seyfettin Vadi

Electronics 2026, 15(1), 10; https://doi.org/10.3390/electronics15010010 - 19 Dec 2025

Viewed by 975

Abstract

SCADA systems, widely used in critical infrastructure, are becoming increasingly vulnerable to complex cyber threats, which can compromise national security. This study presents an artificial intelligence-based approach aimed at the early and reliable detection of cyberattacks against SCADA systems. The study physically scaled [...] Read more.

SCADA systems, widely used in critical infrastructure, are becoming increasingly vulnerable to complex cyber threats, which can compromise national security. This study presents an artificial intelligence-based approach aimed at the early and reliable detection of cyberattacks against SCADA systems. The study physically scaled the SCADA communication architecture of a hydroelectric power plant and created a suitable test environment. In this environment, in addition to the benign normal state, attack scenarios such as Man-in-the-Middle (MITM), Denial-of-Service (DoS), and Command Injection were implemented while the process created for the system’s operation was running continuously. While the scenarios were being implemented, the SCADA system was monitored, and network data flow was collected and stored for later analysis. Basic machine learning algorithms, including KNN, Naive Bayes, Decision Trees, and Logistic Regression, were applied to the obtained data. Also, different combinations of these methods have been tested. The analysis results showed that the hybrid model, consisting of a Decision Tree and Logistic Regression, achieved the most successful results, with a 98.29% accuracy rate, an Area Under the Curve (AUC) value of 0.998, and a reasonably short detection time. The results demonstrate that the proposed approach can accurately classify various types of attacks on SCADA systems, providing an effective early warning mechanism suitable for field applications. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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20 pages, 1325 KB  

Open AccessArticle

AI-Driven Threat Hunting in Enterprise Networks Using Hybrid CNN-LSTM Models for Anomaly Detection

by Mark Kamande, Kwame Assa-Agyei, Frederick Edem Junior Broni, Tawfik Al-Hadhrami and Ibrahim Aqeel

AI 2025, 6(12), 306; https://doi.org/10.3390/ai6120306 - 26 Nov 2025

Cited by 1 | Viewed by 2437

Abstract

Objectives: This study aims to present an AI-driven threat-hunting framework that automates both hypothesis generation and hypothesis validation through a hybrid deep learning model that combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. The objective is to operationalize proactive threat [...] Read more.

Objectives: This study aims to present an AI-driven threat-hunting framework that automates both hypothesis generation and hypothesis validation through a hybrid deep learning model that combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. The objective is to operationalize proactive threat hunting by embedding anomaly detection within a structured workflow, improving detection performance, reducing analyst workload, and strengthening overall security posture. Methods: The framework begins with automated hypothesis generation, in which the model analyzes network flows, telemetry data, and logs sourced from IoT/IIoT devices, Windows/Linux systems, and interconnected environments represented in the TON_IoT dataset. Deviations from baseline behavior are detected as potential threat indicators, and hypotheses are prioritized according to anomaly confidence scores derived from output probabilities. Validation is conducted through iterative classification, where CNN-extracted spatial features and LSTM-captured temporal features are jointly used to confirm or refute hypotheses, minimizing manual data pivoting and contextual enrichment. Principal Component Analysis (PCA) and Recursive Feature Elimination with Random Forest (RFE-RF) are employed to extract and rank features based on predictive importance. Results: The hybrid model, trained on the TON_IoT dataset, achieved strong performance metrics: 99.60% accuracy, 99.71% precision, 99.32% recall, an AUC of 99%, and a 99.58% F1-score. These results outperform baseline models such as Random Forest and Autoencoder. By integrating spatial and temporal feature extraction, the model effectively identifies anomalies with minimal false positives and false negatives, while the automation of the hypothesis lifecycle significantly reduces analyst workload. Conclusions: Automating threat-hunting processes through hybrid deep learning shifts organizations from reactive to proactive defense. The proposed framework improves threat visibility, accelerates response times, and enhances overall security posture. The findings offer valuable insights for researchers, practitioners, and policymakers seeking to advance AI adoption in threat intelligence and enterprise security. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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17 pages, 1488 KB  

Open AccessArticle

PG-Mamba: An Enhanced Graph Framework for Mamba-Based Time Series Clustering

by Yao Sun, Dongshi Zuo and Jing Gao

Sensors 2025, 25(16), 5043; https://doi.org/10.3390/s25165043 - 14 Aug 2025

Viewed by 2151

Abstract

Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important [...] Read more.

Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important patterns. When dealing with massive time series data, existing clustering methods often focus on mining associations between sequences. However, ideal clustering results are difficult to achieve by relying solely on pairwise association analysis in the presence of noise and information scarcity. To address these issues, we propose a framework called Patch Graph Mamba (PG-Mamba). For the first time, the spatio-temporal patterns of a single sequence are explored by dividing the time series into multiple patches and constructing a spatio-temporal graph (STG). In this graph, these patches serve as nodes, connected by both spatial and temporal edges. By leveraging Mamba-driven long-range dependency learning and a decoupled spatio-temporal graph attention mechanism, our framework simultaneously captures temporal dynamics and spatial relationships and, thus, enabling the effective extraction of key information from time series. Furthermore, a spatio-temporal adjacency matrix reconstruction loss is introduced to mitigate feature space perturbations induced by the clustering loss. Experimental results demonstrate that PG-Mamba outperforms state-of-the-art methods, offering new insights into time series clustering tasks. Across the 33 datasets of the UCR time series archive, PG-Mamba achieved the highest average rank of 3.606 and secured the most first-place rankings (13). Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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22 pages, 450 KB  

Open AccessArticle

Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering

by M. Baqer

Future Internet 2025, 17(1), 4; https://doi.org/10.3390/fi17010004 - 26 Dec 2024

Cited by 12 | Viewed by 4696

Abstract

Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting [...] Read more.

Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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23 pages, 3198 KB  

Open AccessArticle

Quantitative Modeling and Predictive Analysis of Chemical Oxygen Demand in Wastewater Treatment Systems Utilizing Long Short-Term Memory Neural Network

by Xuanzhen Meng and Yan Zhang

Sustainability 2024, 16(23), 10359; https://doi.org/10.3390/su162310359 - 27 Nov 2024

Cited by 6 | Viewed by 2060

Abstract

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the [...] Read more.

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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