Trust Management Systems in Cloud Services Environment: Taxonomy of Reputation Attacks and Defense Mechanisms

Alshammari, Salah T.; Alsubhi, Khalid; Aljahdali, Hani Moaiteq; Alghamdi, Ahmed Mohammed

doi:10.1109/access.2021.3132580

Cited by 9 publications

(1 citation statement)

References 87 publications

(95 reference statements)

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“…Reputation Systems [6]are widely employed to calculate these ratings or ranks. However, these rating and rankings can be manipulated through reputation attacks [7], that can increase the reputation rank of fake or wrong content. These attacks can be launched on the information dissemination, calculation structure of the reputation systems.…”

Section: Introductionmentioning

confidence: 99%

Defense Mechanism Against Attacks Promoting Spread Of Wrong Information

Mahmood,

Akif,

Ashraf

et al. 2024

Preprint

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The rise in dependency upon information present on web and social networks has increased the importance of content credibility systems. These sys- tems can help the users to make a right decision related to buying a product, utiliz- ing a service and etc. Application areas including social network blogs of different subjects such as health, food, education, politics and product review/ratings some- times suffer incredible and wrong information flow. The content credibility sys- tems can help users to identify the credible information on these various forums. Recently, researchers have proposed certain mechanisms for these systems, out of which reputation based systems have gained most attention. However, reputa- tion systems are vulnerable to reputation based attacks, like Sybil, Slandering, and Whitewash promoting spread of wrong information. The authors have proposed a defense mechanism against these attacks that is based upon Bayesian Model. The proposed defense mechanism provides protection against these attacks so that fake or wrong information could be prevented. The authors evaluated the proposed mechanism in three different scenarios and presented the results in terms of pre- cision, recall and rate of change in rank. The results reveal almost 88%prevention against these attacks in comparison to the baseline systems.

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Section: Introductionmentioning

confidence: 99%