DETECTION AND PREVENTION OF MALICIOUS FEEDBACK RATING IN WEB SERVICE RECOMMENDATION SYSTEMS
Journal Title: Elysium Journal of Engineering Research and Management - Year 2015, Vol 2, Issue 3
Abstract
To facilitate a recommendation to users for selecting an appropriate or reputed web service from mass of services by avoiding dishonest recommendation. To prevent, avoid and detect the malicious feedback rating. Large scale feedback ratings are analyzed to validate the scalability of proposed approach. Service users provides feedback ratings, which used to calculate reputation of web service in the field of service computing. The malicious ratings and user’s preferences constitutes either positive bias or negative bias. A novel reputation measure for web services proposed in this paper. The proposed method works in two phases. They are malicious rating detection and rating adjustment. The two phases are used to enhance the reputation measure accuracy. The proposed system detects malicious feedback ratings by using cumulative sum method and reduces the effect of user feedback preferences by Pearson correlation coefficient. The proposed method is implemented on various experiments. Experimental results show that proposed method is effectively used to enhance the reliability of service selection. The exists of vulnerability of malicious feedback rating in the system of recommending reputed web services based on the feedback rating from users on each web service. Such vulnerability is detected, reduced and prevented using Cumulative Sum Control Chart, Pearson Correlation Coefficient, and Bloom filtering techniques respectively.
Authors and Affiliations
Deepika S, Kamala B.
Keywords
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- EP ID EP384005
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