网络信息共享中的复原力调查:分类学与应用技术

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Agnaldo de Souza Batista, Aldri L. dos Santos
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引用次数: 0

摘要

信息共享在任何通信网络环境中都至关重要,它能使网络运营服务根据多台部署的计算设备收集到的信息做出决策。作为物联网(IoT)生态系统,组成网络空间的各种网络大大增加了不断共享信息的需求,而这些信息往往会受到干扰。从这个意义上说,异常操作的破坏推动了旨在提高信息共享弹性的研究。因此,在本调查报告中,我们系统地介绍了为实现网络信息共享的弹性而做出的科学努力。首先,我们介绍了一种分类法,以整理实现网络信息共享复原力的策略,并提供了有关网络异常和连接服务的简要概念。然后,面对恶意威胁、网络中断和性能问题,我们详细介绍了该分类法,并讨论了提出的解决方案。接下来,我们分析了文献中现有的促进通信网络信息交换弹性的技术,以验证其优势和限制因素。在全文中,我们强调并论证了在设计和运行过程中限制使用这些技术的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Resilience in Information Sharing on Networks: Taxonomy and Applied Techniques

Information sharing is vital in any communication network environment to enable network operating services take decisions based on the information collected by several deployed computing devices. The various networks that compose cyberspace, as Internet-of-Things (IoT) ecosystems, have significantly increased the need to constantly share information, which is often subject to disturbances. In this sense, the damage of anomalous operations boosted researches aimed at improving resilience to information sharing. Hence, in this survey, we present a systematization of knowledge about scientific efforts for achieving resilience to information sharing on networks. First, we introduce a taxonomy to organize the strategies applied to attain resilience to information sharing on networks, offering brief concepts about network anomalies and connectivity services. Then, we detail the taxonomy in the face of malicious threats, network disruptions, and performance issues, discussing the presented solutions. Next, we analyze the techniques existing in the literature to foster resilience to information exchanged on communication networks to verify their benefits and constraints. Throughout the text, we highlight and argue issues that restrain the use of these techniques during the design and runtime.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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