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Online reputation system is gaining popularity as it helps a user to be sureabout the quality of a product/service he wants to buy. Nonetheless onlinereputation system is not immune from attack. Dealing with malicious ratings inreputation systems has been recognized as an important but difficult task. Thisproblem is challenging when the number of true user's ratings is relativelysmall and unfair ratings plays majority in rated values. In this paper, we haveproposed a new method to find malicious users in online reputation systemsusing Quality Repository Approach (QRA). We mainly concentrated on anomalydetection in both rating values and the malicious users. QRA is very efficientto detect malicious user ratings and aggregate true ratings. The proposedreputation system has been evaluated through simulations and it is concludedthat the QRA based system significantly reduces the impact of unfair ratingsand improve trust on reputation score with lower false positive as compared toother method used for the purpose.

Detection and Filtering of Collaborative Malicious Users in Reputation System using Quality Repository Approach