首页 > 最新文献

Computer Science Review最新文献

英文 中文
Secret sharing: A comprehensive survey, taxonomy and applications 秘密共享:一个全面的调查、分类和应用
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-30 DOI: 10.1016/j.cosrev.2023.100608
Arup Kumar Chattopadhyay , Sanchita Saha , Amitava Nag , Sukumar Nandi

The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day’s major challenge. Secret sharing is a method that allows a trusted authority (the dealer) to distribute a secret or a number of secrets among some target participants with the intention that certain predetermined groups of participants can collaborate to recover the secret or secrets. Any other group formed by the participants cannot do so. Threshold secret sharing (TSS) is a particular form of secret sharing. It permits any group consisting of at least a specific number (called the threshold) of participants to reconstruct the secret or secrets. However, any group with fewer than the specified number of participants is forbidden to do so. It provides tolerance against single point of failure (SPOF), which has attracted a large number of researchers to contribute in this field. It has the potential to be implemented in numerous practical and secure applications. In this paper, we present a comprehensive survey of a variety of existing threshold secret sharing schemes. We have identified various aspects of developing secure and efficient secret sharing schemes. We have also highlighted some of the applications based on secret sharing. Finally, the open challenges and future research directions in the field of secret sharing are identified and discussed.

普适计算和各种颠覆性技术的出现,使信息通信技术得到了巨大的发展。同样,网络犯罪分子也在仔细考虑不同的新攻击方式。保护敏感信息的机密性、完整性和身份验证是当今的主要挑战。秘密共享是一种允许受信任的权威机构(经销商)在一些目标参与者之间分发一个或多个秘密的方法,目的是某些预定的参与者组可以协作以恢复秘密。任何其他参与者组成的小组不能这样做。阈值秘密共享(TSS)是一种特殊的秘密共享形式。它允许至少由特定数量(称为阈值)的参与者组成的任何组来重建一个或多个秘密。但是,任何少于规定人数的小组都禁止这样做。它提供了对单点故障(SPOF)的容错,这吸引了大量的研究人员在这一领域做出贡献。它具有在许多实际和安全的应用程序中实现的潜力。在本文中,我们对现有的各种阈值秘密共享方案进行了全面的综述。我们已经确定了开发安全和有效的秘密共享方案的各个方面。我们还重点介绍了一些基于秘密共享的应用程序。最后,对秘密共享领域存在的挑战和未来的研究方向进行了识别和讨论。
{"title":"Secret sharing: A comprehensive survey, taxonomy and applications","authors":"Arup Kumar Chattopadhyay ,&nbsp;Sanchita Saha ,&nbsp;Amitava Nag ,&nbsp;Sukumar Nandi","doi":"10.1016/j.cosrev.2023.100608","DOIUrl":"10.1016/j.cosrev.2023.100608","url":null,"abstract":"<div><p>The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day’s major challenge. Secret sharing is a method that allows a trusted authority (the dealer) to distribute a secret or a number of secrets among some target participants with the intention that certain predetermined groups of participants can collaborate to recover the secret or secrets. Any other group formed by the participants cannot do so. Threshold secret sharing (TSS) is a particular form of secret sharing. It permits any group consisting of at least a specific number (called the threshold) of participants to reconstruct the secret or secrets. However, any group with fewer than the specified number of participants is forbidden to do so. It provides tolerance against single point of failure (SPOF), which has attracted a large number of researchers to contribute in this field. It has the potential to be implemented in numerous practical and secure applications. In this paper, we present a comprehensive survey of a variety of existing threshold secret sharing schemes. We have identified various aspects of developing secure and efficient secret sharing schemes. We have also highlighted some of the applications based on secret sharing. Finally, the open challenges and future research directions in the field of secret sharing are identified and discussed.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100608"},"PeriodicalIF":12.9,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013723000758/pdfft?md5=aabda2ece860c3a66317209935753119&pid=1-s2.0-S1574013723000758-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IoT systems modeling and performance evaluation 物联网系统建模和性能评估
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-01 DOI: 10.1016/j.cosrev.2023.100598
Alem Čolaković

The continuous increase of IoT applications leads to a vast amount of data that needs to be transmitted, stored, and processed. Many IoT applications rely on the Cloud infrastructure to handle these specific application demands. However, the integration of IoT and Cloud poses challenges such as network delays, throughput, energy consumption, reliability, etc. Therefore, a new computing concept is required to support emerging IoT applications. These new concepts include fog computing, edge computing, mobile edge computing, mobile cloud computing, and cloudlets. They use various approaches to distribute resources, processes, and services among IoT system architecture layers. The challenge is to decide which offloading system is the best for a specific use case that emphasizes the IoT system modeling issue. In this paper, a model for the formal description of IoT systems is presented. In addition, an analytical evaluation method was proposed to design these systems using the corresponding architecture, technologies, protocols, and integration model to optimize performance. The proposed approach facilitates and simplifies the selection of the corresponding model for the system architecture. This approach enables an efficient method for performance optimization based on offloading processes (load balancing). Also, this paper provides some insights into specific emerging issues and ideas to be addressed by future research.

物联网应用的不断增加导致了大量需要传输、存储和处理的数据。许多物联网应用程序依赖云基础设施来处理这些特定的应用程序需求。然而,物联网和云的集成带来了网络延迟、吞吐量、能耗、可靠性等挑战。因此,需要一种新的计算概念来支持新兴的物联网应用。这些新概念包括雾计算、边缘计算、移动边缘计算、手机云计算和cloudlets。他们使用各种方法在物联网系统架构层之间分配资源、流程和服务。挑战在于决定哪种卸载系统最适合强调物联网系统建模问题的特定用例。本文提出了一个物联网系统的形式化描述模型。此外,还提出了一种分析评估方法来设计这些系统,使用相应的体系结构、技术、协议和集成模型来优化性能。所提出的方法便于并简化了系统架构的相应模型的选择。这种方法实现了基于卸载过程(负载平衡)的性能优化的有效方法。此外,本文还对未来研究中需要解决的具体新问题和想法提供了一些见解。
{"title":"IoT systems modeling and performance evaluation","authors":"Alem Čolaković","doi":"10.1016/j.cosrev.2023.100598","DOIUrl":"10.1016/j.cosrev.2023.100598","url":null,"abstract":"<div><p>The continuous increase of IoT applications leads to a vast amount of data that needs to be transmitted, stored, and processed. Many IoT applications rely on the Cloud infrastructure to handle these specific application demands. However, the integration of IoT and Cloud poses challenges such as network delays, throughput, energy consumption, reliability, etc. Therefore, a new computing concept is required to support emerging IoT applications. These new concepts include fog computing, edge computing, mobile edge computing, mobile cloud computing, and cloudlets. They use various approaches to distribute resources, processes, and services among IoT system architecture layers. The challenge is to decide which offloading system is the best for a specific use case that emphasizes the IoT system modeling issue. In this paper, a model for the formal description of IoT systems is presented. In addition, an analytical evaluation method was proposed to design these systems using the corresponding architecture, technologies, protocols, and integration model to optimize performance. The proposed approach facilitates and simplifies the selection of the corresponding model for the system architecture. This approach enables an efficient method for performance optimization based on offloading processes (load balancing). Also, this paper provides some insights into specific emerging issues and ideas to be addressed by future research.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100598"},"PeriodicalIF":12.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Model-based joint analysis of safety and security:Survey and identification of gaps 基于模型的安全和安保联合分析:差距调查和识别
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-01 DOI: 10.1016/j.cosrev.2023.100597
Stefano M. Nicoletti , Marijn Peppelman , Christina Kolb , Mariëlle Stoelinga

We survey the state-of-the-art on model-based formalisms for safety and security joint analysis, where safety refers to the absence of unintended failures, and security to absence of malicious attacks. We conduct a thorough literature review and – as a result – we consider fourteen model-based formalisms and compare them with respect to several criteria: (1) Modeling capabilities and Expressiveness: which phenomena can be expressed in these formalisms? To which extent can they capture safety-security interactions? (2) Analytical capabilities: which analysis types are supported? (3) Practical applicability: to what extent have the formalisms been used to analyze small or larger case studies? Furthermore, (1) we present more precise definitions for safety-security dependencies in tree-like formalisms; (2) we showcase the potential of each formalism by modeling the same toy example from the literature and (3) we present our findings and reflect on possible ways to narrow highlighted gaps. In summary, our key findings are the following: (1) the majority of approaches combine tree-like formal models; (2) the exact nature of safety-security interaction is still ill-understood and (3) diverse formalisms can capture different interactions; (4) analyzed formalisms merge modeling constructs from existing safety- and security-specific formalisms, without introducing ad hoc constructs to model safety-security interactions, or (5) metrics to analyze trade offs. Moreover, (6) large case studies representing safety-security interactions are still missing.

我们调查了用于安全和安全联合分析的基于模型的形式主义的最新技术,其中安全是指没有意外故障,安全是指不存在恶意攻击。我们进行了全面的文献综述,因此,我们考虑了14种基于模型的形式主义,并根据几个标准对其进行了比较:(1)建模能力和表达能力:哪些现象可以用这些形式主义来表达?他们能在多大程度上捕捉安全保障互动?(2) 分析功能:支持哪些分析类型?(3) 实际适用性:形式主义在多大程度上被用于分析小型或大型案例研究?此外,(1)我们在树状形式主义中给出了安全-安全依赖性的更精确定义;(2) 我们通过对文献中相同的玩具示例进行建模,展示了每种形式主义的潜力。(3)我们展示了我们的发现,并反思了缩小突出差距的可能方法。总之,我们的主要发现如下:(1)大多数方法结合了树状形式模型;(2) 安全-安保互动的确切性质仍不清楚,(3)不同的形式主义可以捕捉不同的互动;(4) 分析的形式主义合并了现有安全和安全特定形式主义的建模构造,而没有引入特殊构造来建模安全-安全交互,或者(5)分析权衡的度量。此外,(6)代表安全-安保互动的大型案例研究仍然缺失。
{"title":"Model-based joint analysis of safety and security:Survey and identification of gaps","authors":"Stefano M. Nicoletti ,&nbsp;Marijn Peppelman ,&nbsp;Christina Kolb ,&nbsp;Mariëlle Stoelinga","doi":"10.1016/j.cosrev.2023.100597","DOIUrl":"10.1016/j.cosrev.2023.100597","url":null,"abstract":"<div><p>We survey the state-of-the-art on model-based formalisms for safety and security joint analysis, where safety refers to the absence of unintended failures, and security to absence of malicious attacks. We conduct a thorough literature review and – as a result – we consider fourteen model-based formalisms and compare them with respect to several criteria: (1) <em>Modeling capabilities and Expressiveness:</em> which phenomena can be expressed in these formalisms? To which extent can they capture safety-security interactions? (2) <em>Analytical capabilities:</em> which analysis types are supported? (3) <em>Practical applicability:</em> to what extent have the formalisms been used to analyze small or larger case studies? Furthermore, (1) we present more precise definitions for safety-security dependencies in tree-like formalisms; (2) we showcase the potential of each formalism by modeling the same toy example from the literature and (3) we present our findings and reflect on possible ways to narrow highlighted gaps. In summary, our key findings are the following: (1) the majority of approaches combine tree-like formal models; (2) the exact nature of safety-security interaction is still ill-understood and (3) diverse formalisms can capture different interactions; (4) analyzed formalisms merge modeling constructs from existing safety- and security-specific formalisms, without introducing <em>ad hoc</em> constructs to model safety-security interactions, or (5) metrics to analyze trade offs. Moreover, (6) large case studies representing safety-security interactions are still missing.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100597"},"PeriodicalIF":12.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013723000643/pdfft?md5=e6c1bf928918e2a341e966fad8babde0&pid=1-s2.0-S1574013723000643-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Flow based containerized honeypot approach for network traffic analysis: An empirical study 基于流量的容器化蜜罐网络流量分析方法的实证研究
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-01 DOI: 10.1016/j.cosrev.2023.100600
Sibi Chakkaravarthy Sethuraman , Tharshith Goud Jadapalli , Devi Priya Vimala Sudhakaran , Saraju P. Mohanty

The world of connected devices has been attributed to applications that relied upon multitude of devices to acquire and distribute data over extremely diverse networks. This caused a plethora of potential threats. In the field of IT security, the concept of digital baits, or honeypots, which are typically network components (computer systems, access points, or switches) launched to be interrogated, savaged, and impacted, is currently popular as it allows scientists to comprehend further on assault patterns and behavior. Combining the inherent modularity with the administration enabled by the container makes security management simple and permits dispersed deployments, resulting in a very dynamic system. This study delivers several contributions in this regard. First, it comprehends the patterns, methods, and malware types that container honeypots deal with thus examining new developments in existing honeypot research to fill gaps in knowledge about the honeypot technology. A broad range of independently initiated and jointly conducted container honeypot strategies and studies that encompass various methodologies is surveyed. Second, using numerous use cases that aid scientific research, we address and investigate a number of challenges pertaining to container honeypots, such as identification problems, honeypot security issues, and dependability issues. Furthermore, based on our extensive honeypot research, we developed VIKRANT, a containerized research honeypot which assists researchers as well as enthusiasts in generating real-time flow data for threat intelligence. The configured approach was monitored resulting in several data points that allowed relevant conclusions about the malevolent users’ activities.

连接设备的世界被认为是依赖于大量设备在极其多样化的网络上获取和分发数据的应用程序。这造成了过多的潜在威胁。在信息技术安全领域,数字诱饵或蜜罐的概念目前很流行,因为它可以让科学家进一步了解攻击模式和行为,数字诱饵通常是为了被审问、攻击和影响而启动的网络组件(计算机系统、接入点或交换机)。将固有的模块化与容器启用的管理相结合,使安全管理变得简单,并允许分散部署,从而形成一个非常动态的系统。这项研究在这方面作出了若干贡献。首先,它了解了容器蜜罐处理的模式、方法和恶意软件类型,从而考察了现有蜜罐研究的新进展,以填补有关蜜罐技术的知识空白。调查了一系列独立发起和联合进行的容器蜜罐策略和研究,包括各种方法。其次,使用大量有助于科学研究的用例,我们解决并调查了与容器蜜罐有关的许多挑战,如识别问题、蜜罐安全问题和可靠性问题。此外,在我们广泛的蜜罐研究的基础上,我们开发了VIKRANT,这是一种集装箱化的研究蜜罐,它可以帮助研究人员和爱好者为威胁情报生成实时流量数据。对配置的方法进行了监控,得到了几个数据点,这些数据点允许对恶意用户的活动得出相关结论。
{"title":"Flow based containerized honeypot approach for network traffic analysis: An empirical study","authors":"Sibi Chakkaravarthy Sethuraman ,&nbsp;Tharshith Goud Jadapalli ,&nbsp;Devi Priya Vimala Sudhakaran ,&nbsp;Saraju P. Mohanty","doi":"10.1016/j.cosrev.2023.100600","DOIUrl":"10.1016/j.cosrev.2023.100600","url":null,"abstract":"<div><p><span>The world of connected devices has been attributed to applications that relied upon multitude of devices to acquire and distribute data over extremely diverse networks. This caused a plethora of potential threats. In the field of IT security, the concept of digital baits, or honeypots, which are typically network components (computer systems, access points, or switches) launched to be interrogated, savaged, and impacted, is currently popular as it allows scientists to comprehend further on assault patterns and behavior. Combining the inherent modularity with the administration enabled by the container makes security management simple and permits dispersed deployments, resulting in a very dynamic system. This study delivers several contributions in this regard. First, it comprehends the patterns, methods, and </span>malware types that container honeypots deal with thus examining new developments in existing honeypot research to fill gaps in knowledge about the honeypot technology. A broad range of independently initiated and jointly conducted container honeypot strategies and studies that encompass various methodologies is surveyed. Second, using numerous use cases that aid scientific research, we address and investigate a number of challenges pertaining to container honeypots, such as identification problems, honeypot security issues, and dependability issues. Furthermore, based on our extensive honeypot research, we developed VIKRANT, a containerized research honeypot which assists researchers as well as enthusiasts in generating real-time flow data for threat intelligence. The configured approach was monitored resulting in several data points that allowed relevant conclusions about the malevolent users’ activities.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100600"},"PeriodicalIF":12.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A comprehensive survey on data aggregation techniques in UAV-enabled Internet of things 无人机物联网数据聚合技术综述
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-01 DOI: 10.1016/j.cosrev.2023.100599
Asif Mahmud Raivi, Sangman Moh

In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) framework owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be collected is increased, data aggregation is of great importance. Recently, the UAV can also function as a mobile edge computing server in association with aerial data aggregation. This paper is the first to survey the various aspects and techniques of UAV-based aerial data aggregation for IoT networks. After addressing key design issues, we review the existing data aggregation techniques along with possible future direction. They are then compared with each other in terms of major operational features, performance characteristics, advantages, and limitations. Open issues and research challenges are also discussed with possible solution approaches.

近年来,无人机由于其广泛的应用、监测和监视能力、普遍性和移动性,已被用于扩展物联网(IoT)框架。为了支持物联网需求,无人机必须能够实时聚合、处理和传输数据。随着物联网设备数量的增加,以及要收集的数据量的增加,数据聚合至关重要。最近,无人机还可以作为与空中数据聚合相关联的移动边缘计算服务器。本文首次综述了物联网网络中基于无人机的空中数据聚合的各个方面和技术。在解决了关键的设计问题后,我们回顾了现有的数据聚合技术以及未来可能的方向。然后,将它们在主要操作特征、性能特征、优势和局限性方面进行比较。还讨论了悬而未决的问题和研究挑战,以及可能的解决方法。
{"title":"A comprehensive survey on data aggregation techniques in UAV-enabled Internet of things","authors":"Asif Mahmud Raivi,&nbsp;Sangman Moh","doi":"10.1016/j.cosrev.2023.100599","DOIUrl":"10.1016/j.cosrev.2023.100599","url":null,"abstract":"<div><p>In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) framework owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be collected is increased, data aggregation is of great importance. Recently, the UAV can also function as a mobile edge computing server in association with aerial data aggregation. This paper is the first to survey the various aspects and techniques of UAV-based aerial data aggregation for IoT networks. After addressing key design issues, we review the existing data aggregation techniques along with possible future direction. They are then compared with each other in terms of major operational features, performance characteristics, advantages, and limitations. Open issues and research challenges are also discussed with possible solution approaches.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100599"},"PeriodicalIF":12.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013723000667/pdfft?md5=6f2cd703bc7b8c1c9010e724a3c8a10e&pid=1-s2.0-S1574013723000667-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review 遥感应用的基于图的深度学习技术:技术、分类和应用-综合综述
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-10-05 DOI: 10.1016/j.cosrev.2023.100596
Manel Khazri Khlifi , Wadii Boulila , Imed Riadh Farah

In the last decade, there has been a significant surge of interest in machine learning, primarily driven by advancements in deep learning (DL). DL has emerged as a powerful solution to address various challenges in numerous fields, including remote sensing (RS). Graph Deep Learning (GDL), a sub-field of DL, has recently gained increasing attention in the RS community. Tasks in RS requiring detailed information about the relationships between image/scene features are particularly well-suited for GDL. This study examines the notion of GDL and its recent developments in RS-related fields. An extensive survey of the current state-of-the-art in GDL is presented in this paper, with a specific emphasis on five established graph learning techniques: Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Recurrent Neural Networks (GRNNs), Graph Auto-encoders (GAEs), and Graph Generative Adversarial Networks (GGANs). A taxonomy is proposed based on the input data type (dynamic or static) or task being considered. Several promising research directions for GDL in RS are suggested in this paper to foster productive collaborations between the two domains. To the best of our knowledge, this study is the first to provide a comprehensive review that focuses on graph deep learning in remote sensing.

在过去的十年里,人们对机器学习的兴趣激增,这主要是由深度学习(DL)的进步推动的。DL已成为解决包括遥感(RS)在内的众多领域的各种挑战的强大解决方案。图深度学习(GDL)是DL的一个子领域,近年来在RS社区越来越受到关注。RS中需要有关图像/场景特征之间关系的详细信息的任务特别适合GDL。本研究考察了GDL的概念及其在RS相关领域的最新发展。本文对GDL的当前技术进行了广泛的综述,特别强调了五种已建立的图学习技术:图卷积网络(GCN)、图注意力网络(GATs)、图递归神经网络(GRNN)、图自动编码器(GAE)和图生成对抗性网络(GGAN)。根据所考虑的输入数据类型(动态或静态)或任务,提出了一种分类法。本文提出了RS中GDL的几个有前景的研究方向,以促进这两个领域之间的富有成效的合作。据我们所知,这项研究首次对遥感中的图形深度学习进行了全面综述。
{"title":"Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review","authors":"Manel Khazri Khlifi ,&nbsp;Wadii Boulila ,&nbsp;Imed Riadh Farah","doi":"10.1016/j.cosrev.2023.100596","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100596","url":null,"abstract":"<div><p><span><span>In the last decade, there has been a significant surge of interest in machine learning<span>, primarily driven by advancements in deep learning<span><span> (DL). DL has emerged as a powerful solution to address various challenges in numerous fields, including remote sensing (RS). Graph Deep Learning (GDL), a sub-field of DL, has recently gained increasing attention in the RS community. Tasks in RS requiring detailed information about the relationships between image/scene features are particularly well-suited for GDL. This study examines the notion of GDL and its recent developments in RS-related fields. An extensive survey of the current state-of-the-art in GDL is presented in this paper, with a specific emphasis on five established graph learning techniques: Graph Convolutional Networks (GCNs), Graph </span>Attention Networks<span> (GATs), Graph Recurrent Neural Networks (GRNNs), Graph Auto-encoders (GAEs), and Graph </span></span></span></span>Generative Adversarial Networks (GGANs). A taxonomy is proposed based on the </span>input data type (dynamic or static) or task being considered. Several promising research directions for GDL in RS are suggested in this paper to foster productive collaborations between the two domains. To the best of our knowledge, this study is the first to provide a comprehensive review that focuses on graph deep learning in remote sensing.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100596"},"PeriodicalIF":12.9,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49739147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Asynchronous federated learning on heterogeneous devices: A survey 异构设备上的异步联邦学习:综述
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-10-04 DOI: 10.1016/j.cosrev.2023.100595
Chenhao Xu , Youyang Qu , Yong Xiang , Longxiang Gao

Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. Nonetheless, the synchronous aggregation strategy in the classic FL paradigm, particularly on heterogeneous devices, encounters limitations in resource utilization due to the need to wait for slow devices before aggregation in each training round. Furthermore, the uneven distribution of data across devices (i.e. data heterogeneity) in real-world scenarios adversely impacts the accuracy of the global model. Consequently, many asynchronous FL (AFL) approaches have been introduced across various application contexts to enhance efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing AFL variations using a novel classification scheme, including device heterogeneity, data heterogeneity, privacy, and security on heterogeneous devices, as well as applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated domain.

联合学习(FL)是一种分布式机器学习框架,其中基于本地模型的参数在集中式聚合服务器上生成全局模型,解决了由于收集本地训练数据而导致的隐私泄露问题。随着边缘设备和物联网设备的计算和通信能力不断增长,在异构设备上应用FL来训练机器学习模型正成为一种流行趋势。尽管如此,经典FL范式中的同步聚合策略,特别是在异构设备上,由于在每一轮训练中聚合之前需要等待慢速设备,因此在资源利用率方面遇到了限制。此外,在现实世界场景中,数据在设备之间的不均匀分布(即数据异构性)对全局模型的准确性产生了不利影响。因此,在各种应用程序上下文中引入了许多异步FL(AFL)方法,以提高效率、性能、隐私和安全性。这项调查使用一种新的分类方案全面分析和总结了现有的AFL变体,包括设备异构性、数据异构性、异构设备上的隐私和安全性,以及异构设备的应用。最后,这项调查揭示了日益增长的挑战,并在这一研究不足的领域提出了潜在的有前景的研究方向。
{"title":"Asynchronous federated learning on heterogeneous devices: A survey","authors":"Chenhao Xu ,&nbsp;Youyang Qu ,&nbsp;Yong Xiang ,&nbsp;Longxiang Gao","doi":"10.1016/j.cosrev.2023.100595","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100595","url":null,"abstract":"<div><p>Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. Nonetheless, the synchronous aggregation strategy in the classic FL paradigm, particularly on heterogeneous devices, encounters limitations in resource utilization due to the need to wait for slow devices before aggregation in each training round. Furthermore, the uneven distribution of data across devices (i.e. data heterogeneity) in real-world scenarios adversely impacts the accuracy of the global model. Consequently, many asynchronous FL (AFL) approaches have been introduced across various application contexts to enhance efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing AFL variations using a novel classification scheme, including device heterogeneity, data heterogeneity, privacy, and security on heterogeneous devices, as well as applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated domain.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100595"},"PeriodicalIF":12.9,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49738884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 90
Blockchain-based solutions for mobile crowdsensing: A comprehensive survey 基于区块链的移动众测解决方案:一项综合调查
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-09-16 DOI: 10.1016/j.cosrev.2023.100589
Ruiyun Yu , Ann Move Oguti , Mohammad S. Obaidat , Shuchen Li , Pengfei Wang , Kuei-Fang Hsiao

Mobile crowdsensing (MCS) is an emerging data-driven paradigm that leverages the collective intelligence of the crowd, their mobility, and the crowd-companioned smart mobile devices embedded with powerful sensors to acquire information from the physical environment for crowd intelligence extraction and human-centric service delivery. However, existing MCS systems operate in a centralized manner, giving rise to several challenges, including privacy, security, incentives, and dependence on a central service provider. Blockchain is a novel application paradigm that incorporates point-to-point transmission, consensus mechanisms, cryptography, intelligent contracts, distributed data storage, and other computing technologies, creating a shift from the current centralized paradigm to a decentralized paradigm. Nonetheless, the convergence of MCS and blockchains necessitates addressing numerous fundamental challenges arising from their merger. This paper examines the major issues facing MCS systems and blockchain’s potential role in addressing them. We present the MCS-blockchain integrated deployment strategies, architectural designs, and core blockchain technology principles that contribute significantly to the performance of blockchain-based MCS applications. Additionally, the advancement of blockchain technology and its impact on MCS system security and performance requirements are investigated. Finally, we highlight current research gaps and future research opportunities that may inspire the deployment of novel blockchain-based MCS systems.

移动众包感知(MCS)是一种新兴的数据驱动范式,它利用人群的集体智能、他们的移动性以及嵌入强大传感器的与人群相关的智能移动设备,从物理环境中获取信息,用于人群智能提取和以人为中心的服务提供。然而,现有的MCS系统以集中的方式运行,这带来了一些挑战,包括隐私、安全、激励和对中央服务提供商的依赖。区块链是一种新的应用范式,它融合了点对点传输、共识机制、密码学、智能合约、分布式数据存储和其他计算技术,创造了从当前集中式范式向去中心化范式的转变。尽管如此,MCS和区块链的融合需要解决它们合并带来的许多根本挑战。本文探讨了MCS系统面临的主要问题以及区块链在解决这些问题中的潜在作用。我们介绍了MCS区块链集成部署策略、架构设计和核心区块链技术原则,这些原则对基于区块链的MCS应用程序的性能做出了重大贡献。此外,还研究了区块链技术的发展及其对MCS系统安全性和性能要求的影响。最后,我们强调了当前的研究空白和未来的研究机会,这些空白和机会可能会启发部署基于区块链的新型MCS系统。
{"title":"Blockchain-based solutions for mobile crowdsensing: A comprehensive survey","authors":"Ruiyun Yu ,&nbsp;Ann Move Oguti ,&nbsp;Mohammad S. Obaidat ,&nbsp;Shuchen Li ,&nbsp;Pengfei Wang ,&nbsp;Kuei-Fang Hsiao","doi":"10.1016/j.cosrev.2023.100589","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100589","url":null,"abstract":"<div><p>Mobile crowdsensing (MCS) is an emerging data-driven paradigm that leverages the collective intelligence<span> of the crowd, their mobility, and the crowd-companioned smart mobile devices<span><span> embedded with powerful sensors to acquire information from the physical environment for crowd intelligence extraction and human-centric service delivery. However, existing MCS systems operate in a centralized manner, giving rise to several challenges, including privacy, security, incentives, and dependence on a central service provider. Blockchain<span> is a novel application paradigm that incorporates point-to-point transmission, consensus mechanisms, cryptography, intelligent contracts, </span></span>distributed data storage<span><span>, and other computing technologies, creating a shift from the current centralized paradigm to a decentralized paradigm. Nonetheless, the convergence of MCS and blockchains necessitates addressing numerous fundamental challenges arising from their merger. This paper examines the major issues facing MCS systems and blockchain’s potential role in addressing them. We present the MCS-blockchain integrated deployment strategies, </span>architectural designs, and core blockchain technology principles that contribute significantly to the performance of blockchain-based MCS applications. Additionally, the advancement of blockchain technology and its impact on MCS system security and performance requirements are investigated. Finally, we highlight current research gaps and future research opportunities that may inspire the deployment of novel blockchain-based MCS systems.</span></span></span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100589"},"PeriodicalIF":12.9,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49739211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A systematic review of federated learning incentive mechanisms and associated security challenges 联邦学习激励机制和相关安全挑战的系统综述
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-09-13 DOI: 10.1016/j.cosrev.2023.100593
Asad Ali , Inaam Ilahi , Adnan Qayyum , Ihab Mohammed , Ala Al-Fuqaha , Junaid Qadir

In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine learning (ML) models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to the required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. To the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that require further research attention.

为了应对各种隐私风险,研究人员和从业者一直在探索不同的范式,这些范式可以利用消费者设备增加的计算能力,以分布式方式训练机器学习(ML)模型,而不需要将训练数据从单个设备上传到中央设施。为此,提出了联邦学习(FL),作为一种可以通过聚合使用私有数据在本地训练的模型来在中央主节点学习全局机器模型的技术。然而,组织可能不愿意在本地训练模型并共享这些本地ML模型,这是因为它们末端的模型训练所需的计算资源,以及由于对手反转这些模型以推断有关私人训练数据的信息可能导致的隐私风险。已经提出了激励机制来激励最终用户参与ML模型的协作训练(使用他们的本地数据),以换取某些奖励。然而,由于FL的分布式性质以及中央服务器无法访问客户端的超参数信息和用于训练的数量/质量数据,因此FL的最佳激励机制的设计具有挑战性,这使得基于单个客户端在FL环境中的贡献来确定奖励的任务变得困难。尽管已经为FL提出了几种激励机制,但缺乏最新的系统综述,本文填补了这一空白。据我们所知,本文是第一篇系统综述,全面列出了实施这些激励机制所需的设计原则,然后根据其设计原则对各种激励机制进行了分类。此外,我们还全面概述了与激励驱动的FL相关的安全挑战。最后,我们强调了这些激励方案的局限性和陷阱,并详细阐述了需要进一步研究关注的开放研究问题。
{"title":"A systematic review of federated learning incentive mechanisms and associated security challenges","authors":"Asad Ali ,&nbsp;Inaam Ilahi ,&nbsp;Adnan Qayyum ,&nbsp;Ihab Mohammed ,&nbsp;Ala Al-Fuqaha ,&nbsp;Junaid Qadir","doi":"10.1016/j.cosrev.2023.100593","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100593","url":null,"abstract":"<div><p>In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine learning<span> (ML) models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to the required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. To the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that require further research attention.</span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100593"},"PeriodicalIF":12.9,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49739207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A quest for research and knowledge gaps in cybersecurity awareness for small and medium-sized enterprises 对中小型企业网络安全意识的研究和知识差距的探索
IF 12.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-09-12 DOI: 10.1016/j.cosrev.2023.100592
Sunil Chaudhary , Vasileios Gkioulos , Sokratis Katsikas

The proliferation of information and communication technologies in enterprises enables them to develop new business models and enhance their operational and commercial activities. Nevertheless, this practice also introduces new cybersecurity risks and vulnerabilities. This may not be an issue for large organizations with the resources and mature cybersecurity programs in place; the situation with small and medium-sized enterprises (SMEs) is different since they often lack the resources, expertise, and incentives to prioritize cybersecurity. In such cases, cybersecurity awareness can be a critical component of cyberdefense. However, research studies dealing with cybersecurity awareness or related domains exclusively for SMEs are rare, indicating a pressing need for research addressing the cybersecurity awareness requirements of SMEs.

Prior to that, though, it is crucial to identify which aspects of cybersecurity awareness require further research in order to adapt or conform to the needs of SMEs. In this study, we conducted a systematic literature review that focused on cybersecurity awareness, prioritizing those performed with a particular focus on SMEs. The study seeks to analyze and evaluate such studies primarily to determine knowledge and research gaps in the cybersecurity awareness field for SMEs, thus providing a direction for future research.

信息和通信技术在企业中的普及使它们能够发展新的商业模式,加强其经营和商业活动。尽管如此,这种做法也带来了新的网络安全风险和漏洞。对于拥有资源和成熟网络安全计划的大型组织来说,这可能不是一个问题;中小企业的情况有所不同,因为它们往往缺乏资源、专业知识和激励措施来优先考虑网络安全。在这种情况下,网络安全意识可能是网络防御的关键组成部分。然而,专门针对中小企业的网络安全意识或相关领域的研究很少,这表明迫切需要针对中小企业网络安全意识要求进行研究。不过,在此之前,至关重要的是要确定网络安全意识的哪些方面需要进一步研究,以适应或符合中小企业的需求。在这项研究中,我们进行了一项系统的文献综述,重点关注网络安全意识,优先考虑那些特别关注中小企业的研究。该研究旨在分析和评估此类研究,主要是为了确定中小企业在网络安全意识领域的知识和研究差距,从而为未来的研究提供方向。
{"title":"A quest for research and knowledge gaps in cybersecurity awareness for small and medium-sized enterprises","authors":"Sunil Chaudhary ,&nbsp;Vasileios Gkioulos ,&nbsp;Sokratis Katsikas","doi":"10.1016/j.cosrev.2023.100592","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100592","url":null,"abstract":"<div><p>The proliferation of information and communication technologies in enterprises enables them to develop new business models and enhance their operational and commercial activities. Nevertheless, this practice also introduces new cybersecurity risks and vulnerabilities. This may not be an issue for large organizations with the resources and mature cybersecurity programs in place; the situation with small and medium-sized enterprises (SMEs) is different since they often lack the resources, expertise, and incentives to prioritize cybersecurity. In such cases, cybersecurity awareness can be a critical component of cyberdefense. However, research studies dealing with cybersecurity awareness or related domains exclusively for SMEs are rare, indicating a pressing need for research addressing the cybersecurity awareness requirements of SMEs.</p><p>Prior to that, though, it is crucial to identify which aspects of cybersecurity awareness require further research in order to adapt or conform to the needs of SMEs. In this study, we conducted a systematic literature review that focused on cybersecurity awareness, prioritizing those performed with a particular focus on SMEs. The study seeks to analyze and evaluate such studies primarily to determine knowledge and research gaps in the cybersecurity awareness field for SMEs, thus providing a direction for future research.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100592"},"PeriodicalIF":12.9,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49739189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Computer Science Review
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1