Robust Expert Ranking in Online Communities - Fighting Sybil Attacks

Rashed, Khaled A. N.; Balasoiu, Cristina; Klamma, Ralf

doi:10.4108/icst.collaboratecom.2012.250439

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Another study on preventing Sybil attacks in expert ranking systems, the SumUp algorithm is used by MHITS [24]. In this system, the nodes are eliminated by the SumUp approach prior to the ranking procedure.…”

Section: White Washingmentioning

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: White Washingmentioning

confidence: 99%

Defense Mechanism Against Attacks Promoting Spread Of Wrong Information

Mahmood,

Akif,

Ashraf

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…One research towards fighting against Sybil attacks in expert ranking systems MHITS [50] utilizes SumUp algorithm. In this system, the nodes are removed through the Sum Up strategy before the ranking process.…”

Section: ) White Washingmentioning

confidence: 99%

Reputation-Based Approach Toward Web Content Credibility Analysis

et al. 2019

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Web content credibility implies finding credible and correct information on the web. Recent studies have shown there is an increasing trend of users turning towards the web for searching information related to a variety of topics including health, stocks, education, politics to name few. Information credibility is a critical factor in these domains for the decision makers. There is no limitation on the authorship of those articles and content. One criterion for evaluating credibility is to check the authority or source of information. However, there are situations when wrong information flows from credible sources. There are various approaches towards credibility assessment, broadly categorized into human-based and computational approaches. Computational approaches utilizing machine learning based techniques are computationally expensive. Reputation based approaches overcome this, however the latest work fails to take into account issue of negative referrals and utilizes simple summation as the calculation structure making it more resilient to attacks. This paper put forth verified hypothesis of direct relationship of credibility to the expertise of entity. Authors proposed a Bayesian based approach using feedback in the form of interaction among the entities to compute their expertise level, thereby showing improved results in terms of Precision, Correlation and Mean Average Error. The experiments are performed on two different datasets, one of the dataset is developed from a survey as the part of the research study. The results from the two experiments show that the reputation ranks are independent of the pattern of ratings and density of data, unlike previous techniques whose results were limited by these factors. The proposed technique gives 27% and 18% more precise results for the two experiments respectively compared to the baseline. The correlation results are also significant in both experiments for the proposed technique with significant values of 0.39 and 0.87 showing a linear relationship between predicted and original data. The paper also discusses the reputation attacks and proposes counter measures to tackle these attacks through simulation results.INDEX TERMS Web content credibility, information ranking, reputation systems, experts ranking.

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Robust Expert Ranking in Online Communities - Fighting Sybil Attacks

Cited by 2 publications

References 29 publications

Defense Mechanism Against Attacks Promoting Spread Of Wrong Information

Defense Mechanism Against Attacks Promoting Spread Of Wrong Information

Reputation-Based Approach Toward Web Content Credibility Analysis

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