Journal of Information Security and Applications最新文献

{"title":"Beyond Reinforcement Learning for network security: A comprehensive survey and tutorial","authors":"Amir Javadpour ,&nbsp;Forough Ja’fari ,&nbsp;Tarik Taleb ,&nbsp;Fatih Turkmen ,&nbsp;Chafika Benzaïd","doi":"10.1016/j.jisa.2025.104294","DOIUrl":"10.1016/j.jisa.2025.104294","url":null,"abstract":"<div><div>Maintaining strong security is a complex yet vital challenge in the rapidly evolving landscape of modern digital networks. The risks and consequences of security breaches make neglecting network protection unacceptable. Fortunately, ongoing advances in computer science have equipped researchers with powerful tools to reinforce network defenses. Among these, Reinforcement Learning (RL), a branch of machine learning, has gained significant attention for its versatility and effectiveness in strengthening security mechanisms. This paper presents a comprehensive survey and tutorial on the role of RL in network security. It provides background information, a step-by-step tutorial for training RL models, and systematically categorizes research efforts based on the targeted cyber threats. Leveraging recent advances and real-world applications, this survey elucidates how RL enables the development of adaptive and intelligent systems that autonomously learn and respond to evolving threats. Through in-depth analysis, we provide a comprehensive view of the current landscape and the future potential of RL in safeguarding digital assets. The main contributions of this survey are: (1) a systematic and up-to-date review of RL approaches for network security; (2) a unified taxonomy for classifying RL-based solutions; (3) a comparison of the latest advances from 2019 to 2024 across mainstream and emerging research areas; (4) identification of open challenges and future research directions; and (5) a comparative analysis of state-of-the-art models, offering practical insights for both researchers and practitioners. Furthermore, this survey emphasizes the practical translation of RL advances into real-world deployments. By focusing on hands-on implementation guidelines and comparative analyses of deployment scenarios, it bridges the gap between academic research and operational practice. The comprehensive evaluation of RL-based models across different network environments provides actionable insights for practitioners seeking adaptive and scalable security solutions in dynamic and heterogeneous settings.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104294"},"PeriodicalIF":3.7,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vulnerabilities in Machine Learning for cybersecurity: Current trends and future research directions","authors":"Shantanu Pal ,&nbsp;Geeta Yadav ,&nbsp;Zahra Jadidi ,&nbsp;Ahsan Habib ,&nbsp;Md. Palash Uddin ,&nbsp;Chandan Karmakar ,&nbsp;Sandeep Shukla","doi":"10.1016/j.jisa.2025.104269","DOIUrl":"10.1016/j.jisa.2025.104269","url":null,"abstract":"<div><div>Machine learning (ML) has become integral to cybersecurity applications, e.g., phishing detection, intrusion detection systems, malware analysis, and botnet identification. However, the integration of ML also exposes novel attack surfaces that can be exploited through adversarial machine learning (AML). While prior surveys have examined individual threats or defenses, they often focus narrowly on specific stages, e.g., training or testing. In contrast, in this paper, we provide the first comprehensive survey of adversarial attacks and defenses across the entire ML development life cycle within the cybersecurity domain. Using a structured methodology, we categorize vulnerabilities and countermeasures at each stage, data gathering, model training, testing, deployment, and maintenance, highlighting cross-stage interactions and emerging distributed threat models. Our study addresses key gaps in current defenses, including their limited generalizability and lack of standardized evaluation practices, and identifies promising directions, e.g., lifecycle-aware robustness, distributed resilience, and the integration of statistical with generative methods. Consolidating fragmented research into an end-to-end perspective, this study advances the understanding of AML in cybersecurity and outlines a roadmap for building more trustworthy, and resilient ML-driven security systems.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104269"},"PeriodicalIF":3.7,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-based access control model for smart grids using peak hour and privilege level attributes (BACS-HP)","authors":"Sarra Namane ,&nbsp;Imed Ben Dhaou","doi":"10.1016/j.jisa.2025.104261","DOIUrl":"10.1016/j.jisa.2025.104261","url":null,"abstract":"<div><div>The increasing reliance on smart plugs and smart meters in modern electricity grids introduces significant security vulnerabilities, as unauthorized access can compromise grid reliability and stability. Traditional access control models are ill-suited for smart grids’ decentralized and dynamic nature. This paper introduces BACS-HP, a novel Blockchain-Based Access Control Model for Smart Grids that enhances security by incorporating <em>privilege levels</em> and <em>peak hour</em> attributes. Privilege levels prioritize access to critical devices during energy constraints, while the peak hour attribute enables adaptive decision-making to optimize energy allocation during periods of high demand. Unlike existing blockchain-based access control solutions, BACS-HP uniquely combines these context-aware attributes to provide fine-grained access control tailored to the specific needs of smart grids. The model leverages blockchain technology to ensure the secure and decentralized storage of access rights and enforces policies via smart contracts, mitigating single points of failure. Empirical results demonstrate that BACS-HP achieves low-latency security rule updates (between 42 ms and 46 ms), rapid access request processing (between 21 ms and 46 ms), and a high acceptance rate (60%) for critical devices during power outages, outperforming standard ABAC implementations in terms of responsiveness and prioritization. BACS-HP contributes to advancing access control mechanisms in smart grids and highlights the potential of blockchain to meet the security and performance demands of modern energy systems.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104261"},"PeriodicalIF":3.7,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol design of non-linear function in secure multi-party computation based on secret sharing","authors":"Zhongkai Li,&nbsp;Shuyang Fan,&nbsp;Lingfei Jin","doi":"10.1016/j.jisa.2025.104293","DOIUrl":"10.1016/j.jisa.2025.104293","url":null,"abstract":"<div><div>Secure Multi-Party Computation (MPC) enables a group of untrusted parties to collaboratively compute the output of a specified function, while ensuring that each party’s private input remains confidential. Coupled with secret sharing, MPC facilitates privacy-preserving computations, a technique increasingly utilized in diverse fields, such as machine learning. While efficient protocols exist within MPC for linear functions, the evaluation of non-linear functions presents a significant challenge. Existing methods for non-linear functions are often either inefficient or lack the generality for widespread adoption, making them a major impediment in both the design and practical implementation of MPC schemes.</div><div>In this study, we explore the development of a generic protocol for non-linear function computation in MPC, grounded in secret sharing. We have devised a series of protocols to compute fundamental non-linear functions in a three-party setting under a semi-honest security model, representing secret-shared decimal numbers in fixed-point format. These protocols include <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mo>exp</mo></mrow></msub></math></span> for exponential functions, <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mo>log</mo></mrow></msub></math></span> for logarithmic functions, and <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mtext>Inv</mtext></mrow></msub></math></span> for inverse proportion functions. By integrating these basic functions, we can formulate protocols for a broad spectrum of non-linear functions. Specifically, we have developed the <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mtext>Sigmoid</mtext></mrow></msub></math></span> and <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mtext>Tanh</mtext></mrow></msub></math></span> protocols based on the aforementioned methods. Throughout this paper, unless otherwise specified, comparisons refer exclusively to secret-sharing-based (SS-based) MPC protocols in the three-party, semi-honest setting; constant-round garbled-circuit (GC) approaches are outside our comparison scope due to different cost trade-offs. Within this SS-based literature, our protocols offer the lowest online communication rounds. Furthermore, <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mo>exp</mo></mrow></msub></math></span> and <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mtext>inv</mtext></mrow></msub></math></span> support an extended range of inputs, and <span><math><msub><mrow><mi>Π</mi></mrow><mrow><mo>log</mo></mrow></msub></math></span> represents the first protocol capable of handling logarithmic functions with fixed-point inputs. This paper provides a thorough analysis of the security and performance of these innovative protocols.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104293"},"PeriodicalIF":3.7,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-aware regular-singular discriminant analysis for lossless watermarking","authors":"Guo-Dong Su ,&nbsp;Xu Wang ,&nbsp;Ching-Chun Chang","doi":"10.1016/j.jisa.2025.104295","DOIUrl":"10.1016/j.jisa.2025.104295","url":null,"abstract":"<div><div>It remains a major challenge in how to effectively organize and manage digital images stored in cloud. Regular-singular (RS) based watermarking, as one of important technologies, aims to insert watermark into digital images to solve this issue. By revisiting series of RS based watermarking methods, however, how to achieve a better trade-off between enlarging the embedding capacity and keeping the amount of distortion as soon as possible remains an interesting problem, especially deep learning comes powerful. For this, this paper presents a novel lossless watermarking method using uncertainty-aware discriminant analysis and deep learning technology. First, a numerical ordinary differential equation inspired network architecture for cover synthesis we refer to as <strong>NDCS</strong> is introduced. It produces a more realistic cover objective by minimizing a smaller local truncation error. As for <strong>NDCS</strong>, we are also interested in its performance under different network configurations. On this basis, we introduce an uncertainty-aware discriminant analysis in steganographic algorithm, thereby enabling to yield perceptually indistinguishable watermarked images at various capacities. The experimental results demonstrate that our method is conducive to improving the quality of synthetic objective with the mean hamming distance of 0.2231 and achieving a more satisfactory rate-distortion trade-off with an average embedding capacity of 0.2043 bpp, when comparing to the prior regular-singular methods. In addition, our approach can against RS steganalysis and has the identical performance in encrypted domain.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104295"},"PeriodicalIF":3.7,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LeakyDroid: A lightweight method for detecting zero-day leaky Android applications using One-Class Graph Neural Networks","authors":"Neha Sharma,&nbsp;Mayank Swarnkar,&nbsp;Shaan Kumar","doi":"10.1016/j.jisa.2025.104296","DOIUrl":"10.1016/j.jisa.2025.104296","url":null,"abstract":"<div><div>In the current era of mobile technology, ensuring user data security and privacy is very important, particularly with the rise of malicious Android applications that aim to leak end-user data. Moreover, the popularity of the Android OS is resulting in growing numbers of such malicious Android applications. Hackers make the apps malicious by downloading the source code of Android applications and modifying it. Static analysis techniques have traditionally been used to detect such leaky Android applications. However, these methods cannot simulate runtime behaviours, leading to false positives or negatives. Moreover, obfuscated code is also harder to analyse using this technique. On the other hand, dynamic analysis-based methods are used to overcome these issues because they capture the application’s actual behaviour during runtime. However, dynamic analysis methods have high computational complexity. To fill this gap, we propose <em>LeakyDroid</em>, a static but lightweight method for detecting zero-day leaky Android applications using one-class graph neural networks. <em>LeakyDroid</em> distinguishes between the zero-day malicious and genuine versions of Android applications based on function calls inside various class files of the installable APK files. <em>LeakyDroid</em> generates a control flow graph from function calls from several versions of normal APK files of the same application. The graph is trained using OCGNN, which effectively captures relationships and invocation patterns of normal APK files. While testing an unknown version of the same application’s APK, if a considerable deviation is seen from normal behaviour, the application is detected as malicious. We evaluated the performance of <em>LeakyDroid</em> on three applications, namely WhatsApp, Netflix, and Instagram, each with approximately 25 benign and a few malicious and leaky versions. <em>LeakyDroid</em> successfully detected all the malicious versions of APK with no false positives.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104296"},"PeriodicalIF":3.7,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Realization of multi-image encryption algorithm based on DNA and chaotic system on FPGA","authors":"Nermeen H. Abdelzaher ,&nbsp;Mohammed H. Yacoub ,&nbsp;Lobna A. Said","doi":"10.1016/j.jisa.2025.104267","DOIUrl":"10.1016/j.jisa.2025.104267","url":null,"abstract":"<div><div>This paper introduces an efficient FPGA-based image encryption architecture for securing the transmission of grayscale images over high-data-rate networks. The design supports single and multi-image encryption by fusing multiple grayscale input images into a single encrypted three-channel representation. The fractional-order Nose-Hoover hyperchaotic system and the logistic map are employed to generate pseudo-random sequences for the permutation, scrambling, and DNA processing stages. The initial conditions for the chaotic systems result from XORing a SHA-256 hash of the fused image with a user-defined key. Each channel undergoes a sequence of operations: permutation, pixel-level scrambling, DNA encoding, DNA-based XOR operation, and decoding. The proposed algorithm is implemented on an Xilinx Kintex UltraScale KCU105 FPGA and operates at a maximum frequency of 51.3 MHz. The system’s security performance is evaluated through several widely employed statistical metrics. The cipher image achieve an average entropy of 7.9995 in encrypting four 512 × 512 images using the multi-image encryption scheme. The design is robust against differential attacks, achieving high NPCR and UACI averages of 99.6% and 33.47%, respectively. Additionally, it demonstrates robustness against various analysis methods, including cropping attacks and noise attacks. The algorithm passes the NIST statistical test and demonstrates robustness against known plaintext attacks, supporting its suitability for secure and high-throughput image communication applications.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"96 ","pages":"Article 104267"},"PeriodicalIF":3.7,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SecTwin: A secure and efficient authentication mechanism for vehicular digital twins","authors":"Muhammad Tanveer ,&nbsp;Kainat Toor ,&nbsp;Abdullah G. Alharbi ,&nbsp;Syed Rizwan Hassan","doi":"10.1016/j.jisa.2025.104292","DOIUrl":"10.1016/j.jisa.2025.104292","url":null,"abstract":"<div><div>As vehicular digital twin (VDT) networks continue to evolve, ensuring secure and efficient communication between physical vehicles and their digital counterparts is crucial. Traditional authentication protocols rely on computationally intensive cryptographic techniques, leading to increased latency and resource consumption in real-time vehicular environments. To address these challenges, this paper proposes SecTwin, a lightweight authentication mechanism designed specifically for VDT networks. SecTwin leverages TinyJAMBU authenticated encryption and hash-based authentication to establish a secure and resource-efficient communication framework between autonomous vehicles and their DTs. By integrating lightweight cryptographic techniques and secure key management, SecTwin enhances the security and efficiency of VDT networks, paving the way for reliable and safe autonomous vehicle communication. The informal demonstrates that SecTwin is resilient against key security threats, including replay attacks, impersonation, and man-in-the-middle attacks. Moreover, formal security analysis using the random oracle model and Scyther shows SecTwin is secure. Additionally, performance evaluations reveal that SecTwin reduces communication cost by 51.22%, to 52.38% and execution time by 63.29% to 82.63%, making it highly suitable for latency-sensitive vehicular applications.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104292"},"PeriodicalIF":3.7,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defeating evasive malware with Peekaboo: Extracting authentic malware behavior with dynamic binary instrumentation","authors":"Matthew Gaber,&nbsp;Mohiuddin Ahmed,&nbsp;Helge Janicke","doi":"10.1016/j.jisa.2025.104290","DOIUrl":"10.1016/j.jisa.2025.104290","url":null,"abstract":"<div><div>The accuracy of Artificial Intelligence (AI) in malware detection is dependent on the features it is trained with, where the quality and authenticity of these features is dependent on the dataset and the analysis tool. Evasive malware, that alters its behavior in analysis environments, is challenging to extract authentic features from where widely used static and dynamic analysis tools have several limitations. However, Dynamic Binary Instrumentation (DBI) allows deep and precise control of the malware sample, thereby facilitating the extraction of authentic behavior from evasive malware. Considering the limitations of malware analysis for use with AI, this research had two primary objectives: investigation of the evasive techniques used by modern malware and the creation of Peekaboo, a DBI tool to extract authentic data from live Windows malware samples. Peekaboo instruments and defeats evasive techniques that target analysis tools and virtual environments. A dataset of 20,500 samples was assembled and each sample was run for up to 15 min to observe not only the anti-analysis techniques used but also its complete behavior. Peekaboo outperforms other tools on several fronts, it is the only tool to measure start and completion rates, capture the executed Assembly (ASM) instructions, record all network traffic and implements the largest coverage against evasive techniques.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104290"},"PeriodicalIF":3.7,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrusion detection systems in IoT: A detailed review of threat categories, detection strategies, and future technologies","authors":"Burak Aydin ,&nbsp;Hakan Aydin ,&nbsp;Sedat Gormus","doi":"10.1016/j.jisa.2025.104291","DOIUrl":"10.1016/j.jisa.2025.104291","url":null,"abstract":"<div><div>The rapid growth of the Internet of Things (IoT) has transformed numerous sectors by enabling enhanced connectivity and automation among devices in industrial settings. However, this expansion has brought forward notable security concerns, as Internet enabled and connected devices has become increasingly vulnerable to a variety of cyberattacks. This has elevated the importance of Internet of Things security, necessitating robust defense mechanisms. In this paper, we thoroughly examine Intrusion Detection Systems (IDS) within the context of IoT networks, focusing on the different types of attacks and the corresponding detection methods designed to counteract them. Specifically, we classify IoT-specific threats into categories such as network based, device-level, data-centric, and insider attacks, providing insights into their mechanisms, impacts, and real-world occurrences. To address these threats, various IDS approaches are discussed, including signature based IDS, anomaly based IDS, specification based IDS, and hybrid IDS techniques. We further explore the application of Machine Learning in enhancing IDS capabilities for Internet of Things security. Each method’s strengths and limitations are evaluated in terms of accuracy, adaptability, computational efficiency, and scalability. By exploring emerging trends, ongoing challenges, and potential future directions in IDS research for IoT, this study underscores the urgent need for adaptive, scalable, and effective IDS frameworks to protect IoT ecosystems against evolving cyber threats. In addition, this survey provides a critical assessment of the current research landscape, highlighting the fundamental challenges that remain unresolved and outlining future research directions derived both from the existing literature and our own domain-specific analysis.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104291"},"PeriodicalIF":3.7,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}