Intelligent methods to identify incorrect reviews in cloud reputation systems

Do, Quynh Ngoc Thuy

Intelligent methods to identify incorrect reviews in cloud reputation systems

Do, Quynh Ngoc Thuy

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Publication Type:: Thesis
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Do, Quynh Ngoc Thuy
dc.date.accessioned	2019-11-29T01:47:14Z
dc.date.available	2019-11-29T01:47:14Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10453/137072
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	With the widespread use of information technology in business, an increasing number of companies are looking for ways to reduce their overheads. The cost of IT development has been a barrier for both medium and large companies. In order to reduce cost, a new technology called cloud computing is used in many companies without private servers. When selecting which cloud provider to go with in the future, some enterprises will check certain websites for cloud reviews to see what previous cloud consumers thought about the various cloud providers. These kinds of reviews will affect their selection of a cloud provider. Therefore, the reliability of cloud reviews is very important to a cloud consumer so that they can choose a trustworthy cloud provider. In this thesis, four kinds of incorrect reviews in reputation systems are presented, namely ballot stuffing, bad mouthing, spammer groups and cliques. Previous studies on how to identify these types of incorrect reviews are also assessed in this study. Then, new methods to identify incorrect reviews, including ballot stuffing, bad mouthing, spammer groups and cliques are proposed. Finally, the solutions to identify ballot stuffing, bad mouthing and spammer groups are then validated.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/137072/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.subject	intelligent method
dc.subject	review
dc.subject	reputation system
dc.subject	information technology
dc.subject	business
dc.subject	business-IT alignment
dc.subject	company
dc.subject	enterprise
dc.subject	cloud computing
dc.subject	cloud provider
dc.subject	consumer
dc.subject	ballot stuffing
dc.subject	spammer group
dc.subject	clique
dc.title	Intelligent methods to identify incorrect reviews in cloud reputation systems	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

With the widespread use of information technology in business, an increasing number of companies are looking for ways to reduce their overheads. The cost of IT development has been a barrier for both medium and large companies. In order to reduce cost, a new technology called cloud computing is used in many companies without private servers. When selecting which cloud provider to go with in the future, some enterprises will check certain websites for cloud reviews to see what previous cloud consumers thought about the various cloud providers. These kinds of reviews will affect their selection of a cloud provider. Therefore, the reliability of cloud reviews is very important to a cloud consumer so that they can choose a trustworthy cloud provider. In this thesis, four kinds of incorrect reviews in reputation systems are presented, namely ballot stuffing, bad mouthing, spammer groups and cliques. Previous studies on how to identify these types of incorrect reviews are also assessed in this study. Then, new methods to identify incorrect reviews, including ballot stuffing, bad mouthing, spammer groups and cliques are proposed. Finally, the solutions to identify ballot stuffing, bad mouthing and spammer groups are then validated.

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http://hdl.handle.net/10453/137072