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

TL;DR

This paper introduces a novel method called Quality Repository Approach (QRA) for detecting malicious users in online reputation systems, effectively reducing unfair ratings and enhancing trust through anomaly detection.

Contribution

The paper presents a new QRA-based technique for identifying malicious users and filtering unfair ratings in reputation systems, improving accuracy over existing methods.

Findings

QRA significantly reduces the impact of unfair ratings.

The system improves trust in reputation scores.

False positive rates are lower compared to other methods.

Abstract

Online reputation system is gaining popularity as it helps a user to be sure about the quality of a product/service he wants to buy. Nonetheless online reputation system is not immune from attack. Dealing with malicious ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user's ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we have proposed a new method to find malicious users in online reputation systems using Quality Repository Approach (QRA). We mainly concentrated on anomaly detection in both rating values and the malicious users. QRA is very efficient to detect malicious user ratings and aggregate true ratings. The proposed reputation system has been evaluated through simulations and it is concluded that the QRA based system significantly reduces the impact of unfair ratings and improve trust on reputation score with lower false positive as compared to other method used for the purpose.