Intelligent methods to identify incorrect reviews in cloud reputation systems

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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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