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13 pages, 275 KB  
Article
Generalized Gamma Frailty and Symmetric Normal Random Effects Model for Repeated Time-to-Event Data
by Kai Liu, Yan Qiao Wang, Xiaojun Zhu and Narayanaswamy Balakrishnan
Symmetry 2025, 17(10), 1760; https://doi.org/10.3390/sym17101760 - 17 Oct 2025
Viewed by 39
Abstract
Clustered time-to-event data are quite common in survival analysis and finding a suitable model to account for dispersion as well as censoring is an important issue. In this article, we present a flexible model for repeated, overdispersed time-to-event data with right-censoring. We present [...] Read more.
Clustered time-to-event data are quite common in survival analysis and finding a suitable model to account for dispersion as well as censoring is an important issue. In this article, we present a flexible model for repeated, overdispersed time-to-event data with right-censoring. We present here a general model by incorporating generalized gamma and normal random effects in a Weibull distribution to accommodate overdispersion and data hierarchies, respectively. The normal random effect has the property of being symmetrical, which means its probability density function is symmetric around its mean. While the random effects are symmetrically distributed, the resulting frailty model is asymmetric in its survival function because the random effects enter the model multiplicatively via the hazard function, and the exponentiation of a symmetric normal variable leads to lognormal distribution, which is right-skewed. Due to the intractable integrals involved in the likelihood function and its derivatives, the Monte Carlo approach is used to approximate the involved integrals. The maximum likelihood estimates of the parameters in the model are then numerically determined. An extensive simulation study is then conducted to evaluate the performance of the proposed model and the method of inference developed here. Finally, the usefulness of the model is demonstrated by analyzing a data on recurrent asthma attacks in children and a recurrent bladder data set known in the survival analysis literature. Full article
15 pages, 1705 KB  
Article
Enhancing Two-Step Random Access in LEO Satellite Internet an Attack-Aware Adaptive Backoff Indicator (AA-BI)
by Jiajie Dong, Yong Wang, Qingsong Zhao, Ruiqian Ma and Jiaxiong Yang
Future Internet 2025, 17(10), 454; https://doi.org/10.3390/fi17100454 - 1 Oct 2025
Viewed by 230
Abstract
Low-Earth-Orbit Satellite Internet (LEO SI), with its capability for seamless global coverage, is a key solution for connecting IoT devices in areas beyond terrestrial network reach, playing a vital role in building a future ubiquitous IoT system. Inspired by the IEEE 802.15.4 Improved [...] Read more.
Low-Earth-Orbit Satellite Internet (LEO SI), with its capability for seamless global coverage, is a key solution for connecting IoT devices in areas beyond terrestrial network reach, playing a vital role in building a future ubiquitous IoT system. Inspired by the IEEE 802.15.4 Improved Adaptive Backoff Algorithm (I-ABA), this paper proposes an Attack-Aware Adaptive Backoff Indicator (AA-BI) mechanism to enhance the security and robustness of the two-step random access process in LEO SI. The mechanism constructs a composite threat intensity indicator that incorporates collision probability, Denial-of-Service (DoS) attack strength, and replay attack intensity. This quantified threat level is smoothly mapped to a dynamic backoff window to achieve adaptive backoff adjustment. Simulation results demonstrate that, with 200 pieces of user equipment (UE), the AA-BI mechanism significantly improves the access success rate (ASR) and jamming resistance rate (JRR) under various attack scenarios compared to the I-ABA and Binary Exponential Backoff (BEB) algorithms. Notably, under high-attack conditions, AA-BI improves ASR by up to 25.1% and 56.6% over I-ABA and BEB, respectively. Moreover, under high-load conditions with 800 users, AA-BI still maintains superior performance, achieving an ASR of 0.42 and a JRR of 0.68, thereby effectively ensuring the access performance and reliability of satellite Internet in malicious environments. Full article
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20 pages, 643 KB  
Article
Improving Physical Layer Security for Multi-Hop Transmissions in Underlay Cognitive Radio Networks with Various Eavesdropping Attacks
by Kyusung Shim and Beongku An
Electronics 2025, 14(19), 3867; https://doi.org/10.3390/electronics14193867 - 29 Sep 2025
Viewed by 180
Abstract
This paper investigates physical layer security (PHY-security) for multi-hop transmission in underlay cognitive radio networks under various eavesdropping attacks. To enhance secrecy performance, we propose two opportunistic scheduling schemes. The first scheme, called the minimal node selection (MNS) scheme, selects the node in [...] Read more.
This paper investigates physical layer security (PHY-security) for multi-hop transmission in underlay cognitive radio networks under various eavesdropping attacks. To enhance secrecy performance, we propose two opportunistic scheduling schemes. The first scheme, called the minimal node selection (MNS) scheme, selects the node in each cluster that minimizes the eavesdropper’s channel capacity. The second scheme, named the optimal node selection (ONS) scheme, chooses the node that maximizes secrecy capacity by using both the main and eavesdropper channel information. To reveal the relationship between network parameters and secrecy performance, we derive closed-form expressions for the secrecy outage probability (SOP) under different scheduling schemes and eavesdropping scenarios. Numerical results show that the ONS scheme provides the most robust secrecy performance among the considered schemes. Furthermore, we analyze the impact of key network parameters on secrecy performance. In detail, although the proposed ONS scheme requires more channel information than the MNS scheme, under a 20 dB interference threshold, the secrecy performance of the ONS scheme is 15% more robust than that of the MNS scheme. Full article
(This article belongs to the Section Networks)
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21 pages, 2310 KB  
Article
Development of a Model for Detecting Spectrum Sensing Data Falsification Attack in Mobile Cognitive Radio Networks Integrating Artificial Intelligence Techniques
by Lina María Yara Cifuentes, Ernesto Cadena Muñoz and Rafael Cubillos Sánchez
Algorithms 2025, 18(10), 596; https://doi.org/10.3390/a18100596 - 24 Sep 2025
Viewed by 299
Abstract
Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but [...] Read more.
Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article
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27 pages, 2742 KB  
Article
Urban Science Meets Cyber Risk: Quantifying Smart City Downtime with CTMC and H3 Geospatial Data
by Enrico Barbierato, Serena Curzel, Alice Gatti and Marco Gribaudo
Urban Sci. 2025, 9(9), 380; https://doi.org/10.3390/urbansci9090380 - 17 Sep 2025
Viewed by 566
Abstract
This work quantifies downtime caused by cyberattacks for eight critical urban services in Milan by coupling sectoral Continuous-Time Markov Chains (CTMCs) with an approximately equal-area H3 hexagonal grid of the city. The pipeline ingests OpenStreetMap infrastructure, simulates coupled failure/repair dynamics across sectors (power, [...] Read more.
This work quantifies downtime caused by cyberattacks for eight critical urban services in Milan by coupling sectoral Continuous-Time Markov Chains (CTMCs) with an approximately equal-area H3 hexagonal grid of the city. The pipeline ingests OpenStreetMap infrastructure, simulates coupled failure/repair dynamics across sectors (power, telecom, hospitals, ambulance stations, banks, ATMs, surveillance, and government offices), and reports availability, outage burden (area under the infected/down curve, or AUC), and multi-sector distress probabilities. Cross-sector dependencies (e.g., power→telecom) are modeled via a joint CTMC on sector up/down states; uncertainty is quantified with nested bootstraps (inner bands for stochastic variability, and outer bands for parameter uncertainty). Economic impacts use sector-specific cost priors with sensitivity analysis (PRCC). Spatial drivers are probed via hotspot mapping (Getis–Ord Gi*, local Moran’s I) and spatial regression on interpretable covariates. In a baseline short decaying attack, healthcare remains the most available tier, while power and banks bear a higher burden; coupling increases P(≥ksectorsdown) and per-sector AUC relative to an independent counterfactual, with paired-bootstrap significance at α=0.05 for ATMs, banks, hospitals, and ambulance stations. Government offices are borderline, and telecom shows the same direction of effect but is not significant at α=0.05. Under a persistent/adaptive attacker, citywide downtime and P(≥2) rise substantially. Costs are dominated by telecom/bank/power under literature-informed penalties, and uncertainty in those unit costs explains most of the variance in total loss. Spatial analysis reveals statistically significant hotspots where exposure and dependency pressure are high, while a diversified local service mix appears protective. All code and plots are fully reproducible with open data. Full article
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32 pages, 1572 KB  
Article
Intercepting and Monitoring Potentially Malicious Payloads with Web Honeypots
by Rareș-Mihail Visalom, Maria-Elena Mihăilescu, Răzvan Rughiniș and Dinu Țurcanu
Future Internet 2025, 17(9), 422; https://doi.org/10.3390/fi17090422 - 17 Sep 2025
Viewed by 642
Abstract
The rapid development of an increasing volume of web apps and the improper testing of the resulting code invariably provide more attack surfaces to potentially exploit. This leads to higher chances of facing cybersecurity breaches that can negatively impact both the users and [...] Read more.
The rapid development of an increasing volume of web apps and the improper testing of the resulting code invariably provide more attack surfaces to potentially exploit. This leads to higher chances of facing cybersecurity breaches that can negatively impact both the users and providers of web services. Moreover, current data leaks resulting from breaches are most probably the fuel of future breaches and social engineering attacks. Given the context, a better analysis and understanding of web attacks are of the utmost priority. Our study provides practical insights into developing, implementing, deploying, and actively monitoring a web application-agnostic honeypot with the objective of improving the odds of defending against web attacks. Full article
(This article belongs to the Section Cybersecurity)
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15 pages, 2578 KB  
Article
Effects of Composite Cross-Entropy Loss on Adversarial Robustness
by Ning Ding and Knut Möller
Electronics 2025, 14(17), 3529; https://doi.org/10.3390/electronics14173529 - 4 Sep 2025
Viewed by 557
Abstract
Convolutional neural networks (CNNs) can efficiently extract image features and perform corresponding classification. Typically, the CNN architecture uses the softmax layer to map the extracted features to classification probabilities, and the cost function used for training is the cross-entropy loss. In this paper, [...] Read more.
Convolutional neural networks (CNNs) can efficiently extract image features and perform corresponding classification. Typically, the CNN architecture uses the softmax layer to map the extracted features to classification probabilities, and the cost function used for training is the cross-entropy loss. In this paper, we evaluate the influence of a number of representative composite cross-entropy loss functions on the learned feature space at the fully connected layer, when a target classification is introduced into a multi-class classification task. In addition, the accuracy and robustness of CNN models trained with different composite cross-entropy loss functions are investigated. Improved robustness is achieved by changing the loss between the input and the target classification. Preliminary experiments were conducted using ResNet-50 on the Cholec80 dataset for surgical tool recognition. Furthermore, the model trained with the proposed composite cross-entropy loss incorporating another target all-one classification demonstrates a 31% peak improvement in adversarial robustness. Adversarial training with target adversarial samples yields 80% robustness against PGD attack. This investigation shows that the careful choice of the loss function can improve the robustness of CNN models. Full article
(This article belongs to the Special Issue Convolutional Neural Networks and Vision Applications, 4th Edition)
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21 pages, 867 KB  
Article
Homophily-Guided Backdoor Attacks on GNN-Based Link Prediction
by Yadong Wang, Zhiwei Zhang, Pengpeng Qiao, Ye Yuan and Guoren Wang
Appl. Sci. 2025, 15(17), 9651; https://doi.org/10.3390/app15179651 - 2 Sep 2025
Viewed by 516
Abstract
Graph Neural Networks (GNNs) have shown strong performance in link prediction, a core task in graph analysis. However, recent studies reveal their vulnerability to backdoor attacks, which can manipulate predictions stealthily and pose significant yet underexplored security risks. The existing backdoor strategies for [...] Read more.
Graph Neural Networks (GNNs) have shown strong performance in link prediction, a core task in graph analysis. However, recent studies reveal their vulnerability to backdoor attacks, which can manipulate predictions stealthily and pose significant yet underexplored security risks. The existing backdoor strategies for link prediction suffer from two key limitations: gradient-based optimization is computationally intensive and scales poorly to large graphs, while single-node triggers introduce noticeable structural anomalies and local feature inconsistencies, making them both detectable and less effective. To address these limitations, we propose a novel backdoor attack framework grounded in the principle of homophily, designed to balance effectiveness and stealth. For each selected target link to be poisoned, we inject a unique path-based trigger by adding a bridge node that acts as a shared neighbor. The bridge node’s features are generated through a context-aware probabilistic sampling mechanism over the joint neighborhood of the target link, ensuring high consistency with the local graph context. Furthermore, we introduce a confidence-based trigger injection strategy that selects non-existent links with the lowest predicted existence probabilities as targets, ensuring a highly effective attack from a small poisoning budget. Extensive experiments on five benchmark datasets—Cora, Citeseer, Pubmed, CS, and the large-scale Physics graph—demonstrate that our method achieves superior performance in terms of Attack Success Rate (ASR) while maintaining a low Benign Performance Drop (BPD). These results highlight a novel and practical threat to GNN-based link prediction, offering valuable insights for designing more robust graph learning systems. Full article
(This article belongs to the Special Issue Adversarial Attacks and Cyber Security: Trends and Challenges)
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19 pages, 5365 KB  
Article
Ferulic Acid Promotes Hematopoietic Stem Cell Maintenance in Homeostasis and Injury Through Diminishing Ferroptosis Susceptibility
by Shuzhen Zhang, Yimin Zhang, Jiacheng Le, Kuan Yu, Xinliang Chen, Jun Chen, Mo Chen, Yiding Wu, Yang Xu, Song Wang, Chaonan Liu, Junping Wang and Changhong Du
Antioxidants 2025, 14(9), 1053; https://doi.org/10.3390/antiox14091053 - 27 Aug 2025
Viewed by 699
Abstract
Redox balance is essential for maintenance of the hematopoietic stem cell (HSC) pool, which ensures the lifelong hematopoiesis. However, oxidative attack induced by various physiopathological stresses always compromises HSC maintenance, while there remains lack of safe and effective antioxidative measures combating these conditions. [...] Read more.
Redox balance is essential for maintenance of the hematopoietic stem cell (HSC) pool, which ensures the lifelong hematopoiesis. However, oxidative attack induced by various physiopathological stresses always compromises HSC maintenance, while there remains lack of safe and effective antioxidative measures combating these conditions. Here, we show that ferulic acid (FA), a natural antioxidant abundantly present in Angelica sinensis which is a traditional Chinese herb commonly used for promotion of blood production, distinctively and directly promotes HSC maintenance and thereby boosts hematopoiesis at homeostasis, whether supplemented over the long term in vivo or in HSC culture ex vivo. Using a mouse model of acute myelosuppressive injury induced by ionizing radiation, we further reveal that FA supplementation effectively safeguards HSC maintenance and accelerates hematopoietic regeneration after acute myelosuppressive injury. Mechanistically, FA diminishes ferroptosis susceptibility of HSCs through limiting the labile iron pool (LIP), thus favoring HSC maintenance. In addition, the LIP limitation and anti-ferroptosis activity of FA is independent of nuclear-factor erythroid 2-related factor 2 (NRF2), probably relying on its iron-chelating ability. These findings not only uncover a novel pharmacological action and mechanism of FA in promoting HSC maintenance, but also provides a therapeutic rationale for using FA or FA-rich herbs to treat iron overload- and ferroptosis-associated pathologies such as acute myelosuppressive injury. Full article
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17 pages, 1852 KB  
Article
A Hybrid Classical-Quantum Neural Network Model for DDoS Attack Detection in Software-Defined Vehicular Networks
by Varun P. Sarvade, Shrirang Ambaji Kulkarni and C. Vidya Raj
Information 2025, 16(9), 722; https://doi.org/10.3390/info16090722 - 25 Aug 2025
Viewed by 864
Abstract
A typical Software-Defined Vehicular Network (SDVN) is open to various cyberattacks because of its centralized controller-based framework. A cyberattack, such as a Distributed Denial of Service (DDoS) attack, can easily overload the central SDVN controller. Thus, we require a functional DDoS attack recognition [...] Read more.
A typical Software-Defined Vehicular Network (SDVN) is open to various cyberattacks because of its centralized controller-based framework. A cyberattack, such as a Distributed Denial of Service (DDoS) attack, can easily overload the central SDVN controller. Thus, we require a functional DDoS attack recognition system that can differentiate malicious traffic from normal data traffic. The proposed architecture comprises hybrid Classical-Quantum Machine Learning (QML) methods for detecting DDoS threats. In this work, we have considered three different QML methods, such as Classical-Quantum Neural Networks (C-QNN), Classical-Quantum Boltzmann Machines (C-QBM), and Classical-Quantum K-Means Clustering (C-QKM). Emulations were conducted using a custom-built vehicular network with random movements and varying speeds between 0 and 100 kmph. Also, the performance of these QML methods was analyzed for two different datasets. The results obtained show that the hybrid Classical-Quantum Neural Network (C-QNN) method exhibited better performance in comparison with the other two models. The proposed hybrid C-QNN model achieved an accuracy of 99% and 90% for the UNB-CIC-DDoS dataset and Kaggle DDoS dataset, respectively. The hybrid C-QNN model combines PennyLane’s quantum circuits with traditional methods, whereas the Classical-Quantum Boltzmann Machine (C-QBM) leverages quantum probability distributions for identifying anomalies. Full article
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13 pages, 3502 KB  
Article
Genome-Wide Association Study of Soybean Mosaic Virus Resistance with a GFP-Based Rapid Evaluation System
by Jiaying Zhou, Hao Su, Yunlai Gao, Huilin Tian, Yun Hao, Yuxi Hu, Mingze Zhu, Qingshan Chen, Dawei Xin and Shuang Song
Agronomy 2025, 15(8), 1960; https://doi.org/10.3390/agronomy15081960 - 14 Aug 2025
Viewed by 459
Abstract
Soybean mosaic virus (SMV) is a major viral pathogen that causes significant yield losses and a reduction in seed quality in susceptible soybean cultivars. Resistance breeding is the most effective, economical, and eco-friendly strategy for prevention of SMV-induced damage. Accurate and convenient assessment [...] Read more.
Soybean mosaic virus (SMV) is a major viral pathogen that causes significant yield losses and a reduction in seed quality in susceptible soybean cultivars. Resistance breeding is the most effective, economical, and eco-friendly strategy for prevention of SMV-induced damage. Accurate and convenient assessment of SMV resistance is an essential prerequisite for resistance breeding. In this study, we constructed a green fluorescent protein (GFP)-tagged SMV recombinant virus (SMV-GFP) by yeast homologous recombination technology. It was proved that the recombinant virus can not only be used to track the viral infection process in Nicotiana benthamiana and soybean, but also to quantify the viral load based on relative fluorescence area (RFA) value. Using this recombinant virus, the resistance of 286 soybean germplasms from Northeast China to SMV was evaluated. A genome-wide association study (GWAS) was conducted using the RFA values of the 286 soybean accessions to find possible SMV-resistance genes. The results revealed 72 single nucleotide polymorphism (SNP) loci on chromosome 13 closely associated with SMV resistance, and a total of 40 genes were discovered within the candidate regions. By integrating the results of gene functional annotation and haplotype analysis, Glyma.13g176600 encoding a membrane attack complex/perforin (MACPF) domain-containing protein and Glyma.13g177000 encoding a DUF761-containing protein were identified as the most probable candidate genes associated with SMV resistance. Overall, the GFP-based rapid evaluation system developed in this study will facilitate breeding for resistance to SMV in soybean. Full article
(This article belongs to the Section Pest and Disease Management)
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12 pages, 1122 KB  
Article
Context-Dependent Anti-Predator Behavior in Nymphs of the Invasive Spotted Lanternfly (Lycorma delicatula): Effects of Development, Microhabitat, and Social Environment
by Ellen van Wilgenburg, Crystal Aung and Julia N. Caputo
Insects 2025, 16(8), 815; https://doi.org/10.3390/insects16080815 - 6 Aug 2025
Viewed by 647
Abstract
Antipredator behaviors in animals often vary with developmental stage, microhabitat, and social context, yet few studies examine how these factors interact in species that undergo ontogenetic shifts in chemical defense. The spotted lanternfly (Lycorma delicatula) is an invasive planthopper whose nymphs [...] Read more.
Antipredator behaviors in animals often vary with developmental stage, microhabitat, and social context, yet few studies examine how these factors interact in species that undergo ontogenetic shifts in chemical defense. The spotted lanternfly (Lycorma delicatula) is an invasive planthopper whose nymphs transition from cryptically colored early instars to aposematically colored fourth instars that feed primarily on chemically defended host plants. We conducted 1460 simulated predator attacks on nymphs across four developmental stages to examine how antipredator behavior varies with instar, plant location (leaf vs. stem), host plant species, and local conspecific density. Nymphs exhibited three primary responses: hiding, sidestepping, or jumping. We found that location on the plant had the strongest effect, with nymphs on stems more likely to hide than those on leaves. Older instars were significantly less likely to hide and more likely to sidestep, particularly on stems, suggesting reduced reliance on energetically costly escape behaviors as chemical defenses accumulate. First instars were less likely to jump from their preferred host plant (tree of heaven) compared to other plant species. Higher local conspecific density reduced hiding probability, likely due to the dilution effect. These results demonstrate that antipredator strategies in L. delicatula are flexibly deployed based on developmental stage, microhabitat structure, and social context, with implications for understanding evolution of antipredator behavior in chemically protected species. Full article
(This article belongs to the Section Insect Behavior and Pathology)
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28 pages, 1874 KB  
Article
Lexicon-Based Random Substitute and Word-Variant Voting Models for Detecting Textual Adversarial Attacks
by Tarik El Lel, Mominul Ahsan and Majid Latifi
Computers 2025, 14(8), 315; https://doi.org/10.3390/computers14080315 - 2 Aug 2025
Viewed by 650
Abstract
Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense [...] Read more.
Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense mechanisms: the Lexicon-Based Random Substitute Model (LRSM) and the Word-Variant Voting Model (WVVM). LRSM employs randomized substitutions from a dataset-specific lexicon to generate diverse input variations, disrupting adversarial strategies by introducing unpredictability. Unlike traditional defenses requiring synonym dictionaries or precomputed semantic relationships, LRSM directly substitutes words with random lexicon alternatives, reducing overhead while maintaining robustness. Notably, LRSM not only neutralizes adversarial perturbations but occasionally surpasses the original accuracy by correcting inherent model misclassifications. Building on LRSM, WVVM integrates LRSM, Frequency-Guided Word Substitution (FGWS), and Synonym Random Substitution and Voting (RS&V) in an ensemble framework that adaptively combines their outputs. Logistic Regression (LR) emerged as the optimal ensemble configuration, leveraging its regularization parameters to balance the contributions of individual defenses. WVVM consistently outperformed standalone defenses, demonstrating superior restored accuracy and F1 scores across adversarial scenarios. The proposed defenses were evaluated on two well-known sentiment analysis benchmarks: the IMDB Sentiment Dataset and the Yelp Polarity Dataset. The IMDB dataset, comprising 50,000 labeled movie reviews, and the Yelp Polarity dataset, containing labeled business reviews, provided diverse linguistic challenges for assessing adversarial robustness. Both datasets were tested using 4000 adversarial examples generated by established attacks, including Probability Weighted Word Saliency, TextFooler, and BERT-based Adversarial Examples. WVVM and LRSM demonstrated superior performance in restoring accuracy and F1 scores across both datasets, with WVVM excelling through its ensemble learning framework. LRSM improved restored accuracy from 75.66% to 83.7% when compared to the second-best individual model, RS&V, while the Support Vector Classifier WVVM variation further improved restored accuracy to 93.17%. Logistic Regression WVVM achieved an F1 score of 86.26% compared to 76.80% for RS&V. These findings establish LRSM and WVVM as robust frameworks for defending against adversarial text attacks in sentiment analysis. Full article
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30 pages, 1042 KB  
Article
A Privacy-Preserving Polymorphic Heterogeneous Security Architecture for Cloud–Edge Collaboration Industrial Control Systems
by Yukun Niu, Xiaopeng Han, Chuan He, Yunfan Wang, Zhigang Cao and Ding Zhou
Appl. Sci. 2025, 15(14), 8032; https://doi.org/10.3390/app15148032 - 18 Jul 2025
Viewed by 633
Abstract
Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for [...] Read more.
Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (102 probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article
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15 pages, 959 KB  
Article
Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease
by Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Abdelrahman Zamzam, Rawand Abdin and Mohammad Qadura
Biomolecules 2025, 15(7), 991; https://doi.org/10.3390/biom15070991 - 11 Jul 2025
Viewed by 1055
Abstract
Peripheral artery disease (PAD) is associated with an elevated risk of major adverse cardiovascular events (MACE). Despite this, few reliable biomarkers exist to identify patients at heightened risk of MACE. Growth differentiation factor 15 (GDF15), a stress-responsive cytokine implicated in inflammation, atherosclerosis, and [...] Read more.
Peripheral artery disease (PAD) is associated with an elevated risk of major adverse cardiovascular events (MACE). Despite this, few reliable biomarkers exist to identify patients at heightened risk of MACE. Growth differentiation factor 15 (GDF15), a stress-responsive cytokine implicated in inflammation, atherosclerosis, and thrombosis, has been broadly studied in cardiovascular disease but remains underexplored in PAD. This study aimed to evaluate the prognostic utility of GDF15 for predicting 2-year MACE in PAD patients using explainable statistical and machine learning approaches. We conducted a prospective analysis of 1192 individuals (454 with PAD and 738 without PAD). At study entry, patient plasma GDF15 concentrations were measured using a validated multiplex immunoassay. The cohort was followed for two years to monitor the occurrence of MACE, defined as stroke, myocardial infarction, or death. Baseline GDF15 levels were compared between PAD and non-PAD participants using the Mann–Whitney U test. A machine learning model based on extreme gradient boosting (XGBoost) was trained to predict 2-year MACE using 10-fold cross-validation, incorporating GDF15 and clinical variables including age, sex, comorbidities (hypertension, diabetes, dyslipidemia, congestive heart failure, coronary artery disease, and previous stroke or transient ischemic attack), smoking history, and cardioprotective medication use. The model’s primary evaluation metric was the F1 score, a validated measurement of the harmonic mean of the precision and recall values of the prediction model. Secondary model performance metrics included precision, recall, positive likelihood ratio (LR+), and negative likelihood ratio (LR-). A prediction probability histogram and Shapley additive explanations (SHAP) analysis were used to assess model discrimination and interpretability. The mean participant age was 70 ± SD 11 years, with 32% (n = 386) female representation. Median plasma GDF15 levels were significantly higher in PAD patients compared to the levels in non-PAD patients (1.29 [IQR 0.77–2.22] vs. 0.99 [IQR 0.61–1.63] pg/mL; p < 0.001). During the 2-year follow-up period, 219 individuals (18.4%) experienced MACE. The XGBoost model demonstrated strong predictive performance for 2-year MACE (F1 score = 0.83; precision = 82.0%; recall = 83.7%; LR+ = 1.88; LR− = 0.83). The prediction histogram revealed distinct stratification between those who did vs. did not experience 2-year MACE. SHAP analysis identified GDF15 as the most influential predictive feature, surpassing traditional clinical predictors such as age, cardiovascular history, and smoking status. This study highlights GDF15 as a strong prognostic biomarker for 2-year MACE in patients with PAD. When combined with clinical variables in an interpretable machine learning model, GDF15 supports the early identification of patients at high risk for systemic cardiovascular events, facilitating personalized treatment strategies including multidisciplinary specialist referrals and aggressive cardiovascular risk reduction therapy. This biomarker-guided approach offers a promising pathway for improving cardiovascular outcomes in the PAD population through precision risk stratification. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Cardiology 2025)
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