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Zero Trust Networks: Evolution and Application from Concept to Practice

computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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Double Averaging and Gradient Projection: Convergence Guarantees for Decentralized Constrained Optimization

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IEEE Transactions on Automatic Control 2025年第5期70卷 3433-3440页

作者： Shahriari-Mehr, Firooz Panahi, Ashkan Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering SE-412 96 Gothenburg Sweden

We consider a generic decentralized constrained optimization problem over static, directed communication networks, where each agent has exclusive access to only one convex, differentiable, local objective term and one convex constraint set. For this setup, we propose a novel decentralized algorithm, called double averaging and gradient projection (DAGP). We achieve global optimality through a novel distributed tracking technique we call distributed null projection. Further, we show that DAGP can be used to solve unconstrained problems with nondifferentiable objective terms with a problem reduction scheme. Assuming only smoothness of the objective terms, we study the convergence of DAGP and establish sublinear rates of convergence in terms of feasibility, consensus, and optimality, with no extra assumption (e.g., strong convexity). For the analysis, we forego the difficulties of selecting Lyapunov functions by proposing a new methodology of convergence analysis, which we refer to as aggregate lower-bounding. To demonstrate the generality of this method, we also provide an alternative convergence proof for the standard gradient descent algorithm with smooth functions. © 1963-2012 IEEE.

关键词： Constrained optimization

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Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5584-5597页

作者： Zeng, Zhihao Zhang, Xiaoning Zhao, Yangming Zoha, Ahmed Imran, Muhammad Ali Zhang, Yan University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China University of Science and Technology of China School of Computer Science and Technology Hefei China University of Glasgow School of Engineering United Kingdom University of Oslo Department of Informatics Norway

The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×. © 2002-2012 IEEE.

关键词： Economic and social effects

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Tight Exponential Strong Converse for Source Coding Problem with Encoded Side Information

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IEEE Transactions on Information Theory 2025年第3期71卷 1533-1545页

作者： Takeuchi, Daisuke Watanabe, Shun Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Koganei184-8588 Japan

The source coding problem with encoded side information is considered. A lower bound on the strong converse exponent has been derived by Oohama, but its tightness has not been clarified. In this paper, we derive a tight strong converse exponent. For the special case where the side-information does not exist, we demonstrate that our tight exponent of the Wyner-Ahlswede-Körner (WAK) problem reduces to the known tight expression of that special case while Oohama's lower bound is strictly loose. The converse part is proved by a judicious use of the change-of-measure argument, which was introduced by Gu and Effros and further developed by Tyagi and Watanabe. A key component of the methodology by Tyagi and Watanabe is the use of soft Markov constraint, which was originally introduced by Oohama, as a penalty term to prove the Markov constraint at the end. A technical innovation of this paper compared to Tyagi and Watanabe is recognizing that the soft Markov constraint is a part of the exponent, rather than a penalty term that should vanish at the end;this recognition enables us to derive the matching achievability bound. In fact, via numerical experiment, we provide evidence that the soft Markov constraint is strictly positive. Compared to Oohama's derivation of the lower bound, which relies on the single-letterization of a certain moment-generating function, the derivation of our tight exponent only involves manipulations of the Kullback-Leibrer divergence and Shannon entropies. The achievability part is derived by a careful analysis of the type argument;however, unlike the conventional analysis for the achievable rate region, we need to derive the soft Markov constraint in the analysis of the correct probability. Furthermore, we present an application of our derivation of the strong converse exponent to the privacy amplification. © 1963-2012 IEEE.

关键词： Markov processes

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Magnetron sputtered nickel oxide with suppressed interfacial defect states for efficient inverted perovskite solar cells

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Journal of Energy Chemistry 2025年第1期100卷 348-355页

作者： Guoqiang Ma Qin Tan Zhaoning Li Jingwei Xiu Jiafeng Wang Tianle Cheng Dong He Qiang Sun Xuhang Ma Francesco Lamberti Zhubing He Department of Materials Science and Engineering Institute of Innovative Materials(I2M)Shenzhen Key Laboratory of Full Spectral Solar Electricity Generation(FSSEG)Southern University of Science and Technology(SUSTech)Shenzhen 518055GuangdongChina Core Research Facilities Southern University of Science and TechnologyShenzhen 518055GuangdongChina Department of Chemical Sciences Department of Industrial EngineeringUniversity of PadovaVia Gradenigo 6a35131 PadovaItaly

Widely used spin-coated nickle oxide (NiOx) based perovskite solar cells often suffer from severe interfacial reactions between the NiOxand adjacent perovskite layers due to surface defect states,which inherently impair device performance in a long-term view,even with surface molecule *** this study,we developed high-quality magnetron-sputtered NiOxthin films through detailed process optimization,and compared systematically sputtered and spin-coated NiOxthin film surfaces from materials to *** sputtered NiOxfilms exhibit improved crystallinity,smoother surfaces,and significantly reduced Ni3+or Ni vacancies compared to their spin-coated ***,the interface between the perovskite and sputtered NiOxfilm shows a substantially reduced density of defect *** solar cells (PSCs) fabricated with our optimally sputtered NiOxfilms achieved a high power conversion efficiency (PCE) of up to 19.93%and demonstrated enhanced stability,maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun ***,with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)p hosphonic acid (DMAcPA),the device PCE was further promoted to 23.07%,which is the highest value reported for sputtered NiOxbased PSCs so far.

关键词： Nickle oxide Magnetron sputter Defect Stability Perovskite solar cell

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis

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IEEE Access 2025年 13卷 74078-74091页

作者： Jeba Priya, S. Anand Deva Durai, C. Subathra, M.S.P. Thomas George, S. Jeyabose, Andrew Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India King Khalid University College of Computer Science Department of Computer Science Abha Saudi Arabia Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India

Parkinson's disease (PD) is a prevalent neurological disorder that significantly impacts posture and gait, leading to movement abnormalities due to malfunctions in the brain and nervous system. Gait signals are essential for identifying PD, and various techniques have been employed for classification, with a focus on spatiotemporal factors. Additionally, cognitive monitoring systems for PD symptoms have been developed. Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. The combination of VMD with the 1D-CNN achieved the highest accuracy, sensitivity, and specificity, with values of 99.1 %, 100 %, and 100 %, respectively. This finding suggests a promising approach for further research in this field. The optimized VMD-1D-CNN combination demonstrated significant potential for accurately diagnosing PD based on gait dynamics. The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders. © 2013 IEEE.

关键词： Wavelet decomposition

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A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

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IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

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