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25 pages, 6699 KiB  
Article
Protecting Power System Infrastructure Against Disruptive Agents Considering Demand Response
by Jesús M. López-Lezama, Nicolás Muñoz-Galeano, Sergio D. Saldarriaga-Zuluaga and Santiago Bustamante-Mesa
Computers 2025, 14(8), 308; https://doi.org/10.3390/computers14080308 - 30 Jul 2025
Abstract
Power system infrastructure is exposed to a range of threats, including both naturally occurring events and intentional attacks. Traditional vulnerability assessment models, typically based on the N-1 criterion, do not account for the intentionality of disruptive agents. This paper presents a game-theoretic approach [...] Read more.
Power system infrastructure is exposed to a range of threats, including both naturally occurring events and intentional attacks. Traditional vulnerability assessment models, typically based on the N-1 criterion, do not account for the intentionality of disruptive agents. This paper presents a game-theoretic approach to protecting power system infrastructure against deliberate attacks, taking into account the effects of demand response. The interaction between the disruptive agent and the system operator is modeled as a leader–follower Stackelberg game. The leader, positioned in the upper-level optimization problem, must decide which elements to render out of service, anticipating the reaction of the follower (the system operator), who occupies the lower-level problem. The Stackelberg game is reformulated as a bilevel optimization model and solved using a metaheuristic approach. To evaluate the applicability of the proposed method, a 24-bus test system was employed. The results demonstrate that integrating demand response significantly enhances system resilience, compelling the disruptive agent to adopt alternative attack strategies that lead to lower overall disruption. The proposed model serves as a valuable decision-support tool for system operators and planners seeking to improve the robustness and security of electrical networks against disruptive agents. Full article
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19 pages, 13424 KiB  
Article
A Comprehensive Analysis of Security Challenges in ZigBee 3.0 Networks
by Akbar Ghobakhlou, Duaa Zuhair Al-Hamid, Sara Zandi and James Cato
Sensors 2025, 25(15), 4606; https://doi.org/10.3390/s25154606 - 25 Jul 2025
Viewed by 191
Abstract
ZigBee, a wireless technology standard for the Internet of Things (IoT) devices based on IEEE 802.15.4, faces significant security challenges that threaten the confidentiality, integrity, and availability of its networks. Despite using 128-bit Advanced Encryption Standard (AES) with symmetric keys for node authentication [...] Read more.
ZigBee, a wireless technology standard for the Internet of Things (IoT) devices based on IEEE 802.15.4, faces significant security challenges that threaten the confidentiality, integrity, and availability of its networks. Despite using 128-bit Advanced Encryption Standard (AES) with symmetric keys for node authentication and data confidentiality, ZigBee’s design constraints, such as low cost and low power, have allowed security issues to persist. While ZigBee 3.0 introduces enhanced security features such as install codes and trust centre link key updates, there remains a lack of empirical research evaluating their effectiveness in real-world deployments. This research addresses the gap by conducting a comprehensive, hardware-based analysis of ZigBee 3.0 networks using XBee 3 radio modules and ZigBee-compatible devices. We investigate the following three core security issues: (a) the security of symmetric keys, focusing on vulnerabilities that could allow attackers to obtain these keys; (b) the impact of compromised symmetric keys on network confidentiality; and (c) susceptibility to Denial-of-Service (DoS) attacks due to insufficient protection mechanisms. Our experiments simulate realistic attack scenarios under both Centralised and Distributed Security Models to assess the protocol’s resilience. The findings reveal that while ZigBee 3.0 improves upon earlier versions, certain vulnerabilities remain exploitable. We also propose practical security controls and best practices to mitigate these attacks and enhance network security. This work contributes novel insights into the operational security of ZigBee 3.0, offering guidance for secure IoT deployments and advancing the understanding of protocol-level defences in constrained environments. Full article
(This article belongs to the Section Communications)
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20 pages, 5416 KiB  
Article
A Novel One-Dimensional Chaotic System for Image Encryption Through the Three-Strand Structure of DNA
by Yingjie Su, Han Xia, Ziyu Chen, Han Chen and Linqing Huang
Entropy 2025, 27(8), 776; https://doi.org/10.3390/e27080776 - 23 Jul 2025
Viewed by 244
Abstract
Digital images have been widely applied in fields such as mobile devices, the Internet of Things, and medical imaging. Although significant progress has been made in image encryption technology, it still faces many challenges, such as attackers using powerful computing resources and advanced [...] Read more.
Digital images have been widely applied in fields such as mobile devices, the Internet of Things, and medical imaging. Although significant progress has been made in image encryption technology, it still faces many challenges, such as attackers using powerful computing resources and advanced algorithms to crack encryption systems. To address these challenges, this paper proposes a novel image encryption algorithm based on one-dimensional sawtooth wave chaotic system (1D-SAW) and the three-strand structure of DNA. Firstly, a new 1D-SAW chaotic system was designed. By introducing nonlinear terms and periodic disturbances, this system is capable of generating chaotic sequences with high randomness and initial value sensitivity. Secondly, a new diffusion rule based on the three-strand structure of DNA is proposed. Compared with the traditional DNA encoding and XOR operation, this rule further enhances the complexity and anti-attack ability of the encryption process. Finally, the security and randomness of the 1D-SAW and image encryption algorithms were verified through various tests. Results show that this method exhibits better performance in resisting statistical attacks and differential attacks. Full article
(This article belongs to the Topic Recent Trends in Nonlinear, Chaotic and Complex Systems)
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31 pages, 356 KiB  
Article
“Mutual Cunning” in King Lear: A Study of Machiavellian Politics
by Carolyn Elizabeth Brown
Literature 2025, 5(3), 18; https://doi.org/10.3390/literature5030018 - 23 Jul 2025
Viewed by 223
Abstract
When scholars view characters in King Lear through a Machiavellian lens, they read Edmund, Goneril, and Regan as stock Machiavels. In contrast, they often perceive Cordelia, Kent, and Edgar as selfless, apolitical characters. This essay argues that the latter characters are more complicated [...] Read more.
When scholars view characters in King Lear through a Machiavellian lens, they read Edmund, Goneril, and Regan as stock Machiavels. In contrast, they often perceive Cordelia, Kent, and Edgar as selfless, apolitical characters. This essay argues that the latter characters are more complicated and politically adroit than they are often judged to be. They are Machiavellian as well, but Shakespeare conceives them within a more appreciative view of the concept of realpolitik. This essay explains the characters’ strategies by relating them to Machiavelli’s tenets of achieving and maintaining political power. The central quandary of the play is the lack of a male heir to the throne. Cordelia attempts to solve the problem by marrying the King of France for political reasons. She has an alliance with Kent, who helps her to justify her invasion of her homeland with French forces. Once the plans for a surprise attack go awry, Cordelia does not follow Machiavellian strategies and is consequently killed. Ironically, Edgar is as ambitious as Edmund, whom he lets plot against his father and bring about Gloucester’s slow decline so as to inherit his father’s fortune while Edmund incurs the blame for his father’s demise. Like Kent, he enlists a disguise for self-advancement. The most adroit Machiavellian characters—Edgar, Kent, and the King of France—all survive through chicanery and cunning. Shakespeare illustrates that secular methods of governorship defeat the old world of divine politics. Full article
(This article belongs to the Special Issue Realpolitik in Renaissance and Early Modern British Literature)
36 pages, 1680 KiB  
Article
Guarding Our Vital Systems: A Metric for Critical Infrastructure Cyber Resilience
by Muharman Lubis, Muhammad Fakhrul Safitra, Hanif Fakhrurroja and Alif Noorachmad Muttaqin
Sensors 2025, 25(15), 4545; https://doi.org/10.3390/s25154545 - 22 Jul 2025
Viewed by 392
Abstract
The increased occurrence and severity of cyber-attacks on critical infrastructure have underscored the need to embrace systematic and prospective approaches to resilience. The current research takes as its hypothesis that the InfraGuard Cybersecurity Framework—a capability model that measures the maturity of cyber resilience [...] Read more.
The increased occurrence and severity of cyber-attacks on critical infrastructure have underscored the need to embrace systematic and prospective approaches to resilience. The current research takes as its hypothesis that the InfraGuard Cybersecurity Framework—a capability model that measures the maturity of cyber resilience through three functional pillars, Cyber as a Shield, Cyber as a Space, and Cyber as a Sword—is an implementable and understandable means to proceed with. The model treats the significant aspects of situational awareness, active defense, risk management, and recovery from incidents and is measured using globally standardized maturity models like ISO/IEC 15504, NIST CSF, and COBIT. The contributions include multidimensional measurements of resilience, a scored scale of capability (0–5), and domain-based classification enabling organizations to assess and enhance their cybersecurity situation in a formalized manner. The framework’s applicability is illustrated in three exploratory settings of power grids, healthcare systems, and airports, each constituting various levels of maturity in resilience. This study provides down-to-earth recommendations to policymakers through the translation of the attributes of resilience into concrete assessment indicators, promoting policymaking, investment planning, and global cyber defense collaboration. Full article
(This article belongs to the Section Internet of Things)
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87 pages, 5171 KiB  
Review
Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
by Jean Paul A. Yaacoub, Hassan N. Noura, Ola Salman and Khaled Chahine
Future Internet 2025, 17(7), 318; https://doi.org/10.3390/fi17070318 - 21 Jul 2025
Viewed by 392
Abstract
The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. [...] Read more.
The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article
(This article belongs to the Special Issue Secure Integration of IoT and Cloud Computing)
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20 pages, 437 KiB  
Article
Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber)
by Qaiser Khan, Sourav Purification and Sang-Yoon Chang
Information 2025, 16(7), 617; https://doi.org/10.3390/info16070617 - 19 Jul 2025
Viewed by 241
Abstract
5G addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve [...] Read more.
5G addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve algorithm. Consequently, the user privacy in 5G is at stake against quantum attacks. In this paper, we study the incorporation of the post-quantum ciphers in the SUCI calculation both at the user equipment and at the core network, which involves the shared-key exchange and then using the resulting key for the ID encryption. We experiment on different hardware platforms to analyze the PQC key exchange and encryption using NIST-standardized CRYSTALS-Kyber (which is now called an ML-KEM after the standardization selection by NIST). Our analyses focus on the performances and compare the Kyber-based key exchange and encryption with the current (pre-quantum) elliptic curve Diffie–Hellman (ECDH). The performance analyses are critical because mobile networking involves resource-limited and battery-operating mobile devices. We measure and analyze not only the time and CPU-processing performances but also the energy and power performances. Our analyses show that Kyber-512 is the most efficient and even has better performance (i.e., faster computations and lower energy consumption) than ECDH. Full article
(This article belongs to the Special Issue Public Key Cryptography and Privacy Protection)
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25 pages, 2509 KiB  
Article
A Lightweight Intrusion Detection System for IoT and UAV Using Deep Neural Networks with Knowledge Distillation
by Treepop Wisanwanichthan and Mason Thammawichai
Computers 2025, 14(7), 291; https://doi.org/10.3390/computers14070291 - 19 Jul 2025
Viewed by 553
Abstract
Deep neural networks (DNNs) are highly effective for intrusion detection systems (IDS) due to their ability to learn complex patterns and detect potential anomalies within the systems. However, their high resource consumption requirements including memory and computation make them difficult to deploy on [...] Read more.
Deep neural networks (DNNs) are highly effective for intrusion detection systems (IDS) due to their ability to learn complex patterns and detect potential anomalies within the systems. However, their high resource consumption requirements including memory and computation make them difficult to deploy on low-powered platforms. This study explores the possibility of using knowledge distillation (KD) to reduce constraints such as power and hardware consumption and improve real-time inference speed but maintain high detection accuracy in IDS across all attack types. The technique utilizes the transfer of knowledge from DNNs (teacher) models to more lightweight shallow neural network (student) models. KD has been proven to achieve significant parameter reduction (92–95%) and faster inference speed (7–11%) while improving overall detection performance (up to 6.12%). Experimental results on datasets such as NSL-KDD, UNSW-NB15, CIC-IDS2017, IoTID20, and UAV IDS demonstrate DNN with KD’s effectiveness in achieving high accuracy, precision, F1 score, and area under the curve (AUC) metrics. These findings confirm KD’s ability as a potential edge computing strategy for IoT and UAV devices, which are suitable for resource-constrained environments and lead to real-time anomaly detection for next-generation distributed systems. Full article
(This article belongs to the Section ICT Infrastructures for Cybersecurity)
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24 pages, 2173 KiB  
Article
A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence
by Abdullah Alabdulatif
Appl. Sci. 2025, 15(14), 7984; https://doi.org/10.3390/app15147984 - 17 Jul 2025
Viewed by 517
Abstract
In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and [...] Read more.
In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. To address these challenges, Artificial Intelligence and machine learning have emerged as powerful tools for enhancing the accuracy, adaptability, and automation of IDS solutions. This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. The framework is deployed using a Flask-based web interface in the Amazon Elastic Compute Cloud environment, capturing live network traffic and offering sub-second inference with visual alerts. Experimental evaluations using the NSL-KDD dataset demonstrate that the ensemble model outperforms individual classifiers, achieving a high accuracy of 99.40%, along with excellent precision, recall, and F1-score metrics. This research not only enhances detection capabilities but also bridges the trust gap in AI-powered security systems through transparency. The solution shows strong potential for application in critical domains such as finance, healthcare, industrial IoT, and government networks, where real-time and interpretable threat detection is vital. Full article
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12 pages, 231 KiB  
Systematic Review
Cybersecurity Issues in Electrical Protection Relays: A Systematic Review
by Giovanni Battista Gaggero, Paola Girdinio and Mario Marchese
Energies 2025, 18(14), 3796; https://doi.org/10.3390/en18143796 - 17 Jul 2025
Viewed by 218
Abstract
The increasing digitalization of power systems has revolutionized the functionality and efficiency of electrical protection relays. These digital relays enhance fault detection, monitoring, and response mechanisms, ensuring the reliability and stability of power networks. However, their connectivity and reliance on communication protocols introduce [...] Read more.
The increasing digitalization of power systems has revolutionized the functionality and efficiency of electrical protection relays. These digital relays enhance fault detection, monitoring, and response mechanisms, ensuring the reliability and stability of power networks. However, their connectivity and reliance on communication protocols introduce significant cybersecurity risks, making them potential targets for malicious attacks. Cyber threats against digital protection relays can lead to severe consequences, including cascading failures, equipment damage, and compromised grid security. This paper presents a comprehensive review of cybersecurity challenges in digital electrical protection relays, focusing on four key areas: (1) a taxonomy of cyber attack models targeting protection relays, (2) the associated risks and their potential impact on power systems, (3) existing mitigation strategies to enhance relay security, and (4) future research directions to strengthen resilience against cyber threats. Full article
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19 pages, 2632 KiB  
Article
Data-Driven Attack Detection Mechanism Against False Data Injection Attacks in DC Microgrids Using CNN-LSTM-Attention
by Chunxiu Li, Xinyu Wang, Xiaotao Chen, Aiming Han and Xingye Zhang
Symmetry 2025, 17(7), 1140; https://doi.org/10.3390/sym17071140 - 16 Jul 2025
Viewed by 224
Abstract
This study presents a novel spatio-temporal detection framework for identifying False Data Injection (FDI) attacks in DC microgrid systems from the perspective of cyber–physical symmetry. While modern DC microgrids benefit from increasingly sophisticated cyber–physical symmetry network integration, this interconnected architecture simultaneously introduces significant [...] Read more.
This study presents a novel spatio-temporal detection framework for identifying False Data Injection (FDI) attacks in DC microgrid systems from the perspective of cyber–physical symmetry. While modern DC microgrids benefit from increasingly sophisticated cyber–physical symmetry network integration, this interconnected architecture simultaneously introduces significant cybersecurity vulnerabilities. Notably, FDI attacks can effectively bypass conventional Chi-square detector-based protection mechanisms through malicious manipulation of communication layer data. To address this critical security challenge, we propose a hybrid deep learning framework that synergistically combines: Convolutional Neural Networks (CNN) for robust spatial feature extraction from power system measurements; Long Short-Term Memory (LSTM) networks for capturing complex temporal dependencies; and an attention mechanism that dynamically weights the most discriminative features. The framework operates through a hierarchical feature extraction process: First-level spatial analysis identifies local measurement patterns; second-level temporal analysis detects sequential anomalies; attention-based feature refinement focuses on the most attack-relevant signatures. Comprehensive simulation studies demonstrate the superior performance of our CNN-LSTM-Attention framework compared to conventional detection approaches (CNN-SVM and MLP), with significant improvements across all key metrics. Namely, the accuracy, precision, F1-score, and recall could be improved by at least 7.17%, 6.59%, 2.72% and 6.55%. Full article
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19 pages, 5202 KiB  
Article
Optimizing Energy/Current Fluctuation of RF-Powered Secure Adiabatic Logic for IoT Devices
by Bendito Freitas Ribeiro and Yasuhiro Takahashi
Sensors 2025, 25(14), 4419; https://doi.org/10.3390/s25144419 - 16 Jul 2025
Viewed by 382
Abstract
The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a [...] Read more.
The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a promising solution for achieving energy efficiency and enhancing the security of IoT devices. Adiabatic logic circuits are well suited for energy harvesting systems, especially in applications such as sensor nodes, RFID tags, and other IoT implementations. In these systems, the harvested bipolar sinusoidal RF power is directly used as the power supply for the adiabatic logic circuit. However, adiabatic circuits require a peak detector to provide bulk biasing for pMOS transistors. To meet this requirement, a diode-connected MOS transistor-based voltage doubler circuit is used to convert the sinusoidal input into a usable DC signal. In this paper, we propose a novel adiabatic logic design that maintains low power consumption while optimizing energy and current fluctuations across various input transitions. By ensuring uniform and complementary current flow in each transition within the logic circuit’s functional blocks, the design reduces energy variation and enhances resistance against power analysis attacks. Evaluation under different clock frequencies and load capacitances demonstrates that the proposed adiabatic logic circuit exhibits lower fluctuation and improved security, particularly at load capacitances of 50 fF and 100 fF. The results show that the proposed circuit achieves lower power dissipation compared to conventional designs. As an application example, we implemented an ultrasonic transmitter circuit within a LoRaWAN network at the end-node sensor level, which serves as both a communication protocol and system architecture for long-range communication systems. Full article
(This article belongs to the Special Issue Feature Papers in Electronic Sensors 2025)
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18 pages, 3585 KiB  
Article
Dynamic Event-Triggered Switching of LFC Scheme Under DoS Attacks Based on a Predictive Model
by De-Tao Guo, Yong-Xin Zhao, Kai-Bo Shi and Ming Zhu
Electronics 2025, 14(14), 2838; https://doi.org/10.3390/electronics14142838 - 15 Jul 2025
Viewed by 198
Abstract
In this paper, a dynamic event-triggering mechanism (DETM) for load frequency control (LFC) of Denial-of-Service (DoS) attacks based on a predictive model is studied, which has important applications in discrete power systems. Firstly, the prediction model predicts subsequent signals based on observed system [...] Read more.
In this paper, a dynamic event-triggering mechanism (DETM) for load frequency control (LFC) of Denial-of-Service (DoS) attacks based on a predictive model is studied, which has important applications in discrete power systems. Firstly, the prediction model predicts subsequent signals based on observed system states. Secondly, by constructing an improved discrete signal event-triggering scheme, the influence of DoS attacks on the system is weakened. The dynamic trigger condition depends on the past few changes in the system state, rather than real-time sampling values. At the same time, the waiting time of DETM is set to avoid the Zeno phenomenon. Additionally, based on the update period and timestamp technology of the actuator, a control mechanism to resist DoS attacks is implemented in the actuator component. Furthermore, the method uses a double-loop open communication platform to improve reliability and flexibility. Full article
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28 pages, 1727 KiB  
Article
Detecting Jamming in Smart Grid Communications via Deep Learning
by Muhammad Irfan, Aymen Omri, Javier Hernandez Fernandez, Savio Sciancalepore and Gabriele Oligeri
J. Cybersecur. Priv. 2025, 5(3), 46; https://doi.org/10.3390/jcp5030046 - 15 Jul 2025
Viewed by 332
Abstract
Power-Line Communication (PLC) allows data transmission through existing power lines, thus avoiding the expensive deployment of ad hoc network infrastructures. However, power line networks remain vastly unattended, which allows tampering by malicious actors. In fact, an attacker can easily inject a malicious signal [...] Read more.
Power-Line Communication (PLC) allows data transmission through existing power lines, thus avoiding the expensive deployment of ad hoc network infrastructures. However, power line networks remain vastly unattended, which allows tampering by malicious actors. In fact, an attacker can easily inject a malicious signal (jamming) with the aim of disrupting ongoing communications. In this paper, we propose a new solution to detect jamming attacks before they significantly affect the quality of the communication link, thus allowing the detection of a jammer (geographically) far away from a receiver. We consider two scenarios as a function of the receiver’s ability to know in advance the impact of the jammer on the received signal. In the first scenario (jamming-aware), we leverage a classifier based on a Convolutional Neural Network, which has been trained on both jammed and non-jammed signals. In the second scenario (jamming-unaware), we consider a one-class classifier based on autoencoders, allowing us to address the challenge of jamming detection as a classical anomaly detection problem. Our proposed solution can detect jamming attacks on PLC networks with an accuracy greater than 99% even when the jammer is 68 m away from the receiver while requiring training only on traffic acquired during the regular operation of the target PLC network. Full article
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25 pages, 9813 KiB  
Article
Digital Twin Approach for Fault Diagnosis in Photovoltaic Plant DC–DC Converters
by Pablo José Hueros-Barrios, Francisco Javier Rodríguez Sánchez, Pedro Martín Sánchez, Carlos Santos-Pérez, Ariya Sangwongwanich, Mateja Novak and Frede Blaabjerg
Sensors 2025, 25(14), 4323; https://doi.org/10.3390/s25144323 - 10 Jul 2025
Viewed by 324
Abstract
This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar [...] Read more.
This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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