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dc.creatorNicolas, Kervins
dc.creatorWang, Yi
dc.creatorGiakos, George C.
dc.creatorWei, Bingyang
dc.creatorShen, Hongda
dc.date.accessioned2021-03-04T19:21:54Z
dc.date.available2021-03-04T19:21:54Z
dc.date.issued2020-12-25
dc.identifier.urihttps://doi.org/10.1109/access.2020.3047365
dc.identifier.urihttps://repository.tcu.edu/handle/116099117/43806
dc.identifier.urihttps://ieeexplore.ieee.org/document/9308934
dc.description.abstractBlockchain is a technology that ensures data security by verifying database of records established in a decentralized and distributed network. Blockchain-based approaches have been applied to secure data in the fields of the Internet of Things, software engineering, healthcare systems, financial services, and smart power grids. However, the security of the blockchain system is still a major concern. We took the initiative to present a systematic study which sheds light on what defensive strategies are used to secure the blockchain system effectively. Specifically, we focus on blockchain data security that aims to mitigate the two data consistency attacks: double-spend attack and selfish mining attack. We employed the systematic approach to analyze a total of 40 selected studies using the proposed taxonomy of defensive strategies: monitoring, alert forwarding, alert broadcasting, inform, detection, and conceptual research design. It presents a comparison framework for existing and future research on blockchain security. Finally, some recommendations are proposed for blockchain researchers and developers.en_US
dc.language.isoenen_US
dc.publisherIEEE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceIEEE Access
dc.subjectBlockchainen_US
dc.subjectSecurityen_US
dc.subjectSystematicsen_US
dc.subjectData privacyen_US
dc.subjectMedical servicesen_US
dc.subjectTaxonomyen_US
dc.subjectPrivacyen_US
dc.titleBlockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approachen_US
dc.typeArticleen_US
dc.rights.holderNicolas et al
dc.rights.licenseCC BY 4.0
local.collegeCollege of Science and Engineering
local.departmentComputer Science
local.personsWei (COSC)


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